What started with helping patients connect with mental health experts online has today expanded to AI-powered bots talking to people in their time of need. 

One such bot-powered application that we are going to discuss today is Wysa – a mental health application that gives its users a listening ear at the time of their immediate need while giving them the option to connect with real mental health experts. 

Now before we deep dive into the features and technicalities of the Wysa app model, let us answer the key question you are here for. What is the cost of a Wysa-like AI mental health app?

The cost of an AI based mental health app like Wysa can cost anywhere between $90k-$120k to build from concept to execution. How we got to these numbers is what we will discuss in great detail in this article. 

The must-have features of a mental health app like Wysa

Typically, a mental healthcare app like Wysa is made up of three versions – Patients, Therapists, and Admin. Let us look into the individual feature set of these three application versions. 

On a high level, when you, as an entrepreneur are looking to develop AI based mental health app, here are the features you should aim for.

End-users version

The patients-level version of the mental health app like Wysa is made up of multiple functionalities and features aimed at giving them a listening ear, long-term guide, or medical assistance. Let us dive into those features. 

Questions 

Wysa mental healthcare app directly starts with the app asking users questions about their mental, physical wellbeing, and what they expect to achieve with the application. Based on these questions, the application suggests the user of guides and exercises. 

AI chatbot

The primary feature of the Wysa mental health app is its AI chatbot. The bot is built to interact with users around their well-being, if they need to share something, suggest exercises, or even initiate a conversation with a therapist. 

The app also comes with an SOS feature that guides people toward local and national crisis care helplines, while giving them the ability to create a personal safety plan and practice grounding exercises.

Exercises 

The Wysa mental healthcare app has multiple guided exercises that the users can follow to lead a mentally peaceful life. These exercises generally vary from overcoming anxiety, having good sleep quality, handling difficult conversations, and improving productivity.

Therapist

This is another one of the critical Wysa-like mental health app features. Here, the users can talk to a therapist from inside the application and work with them for motivational interviewing, behavioral activation, cognitive reframing, etc. The wait time that Wysa coaches generally maintain is a few hours. 

Journal 

Wysa also comes with a journal feature where the users can make entries about their day, how they are feeling, and whatever they want to share in an encrypted, secure platform. These journals can also be shared with the therapists during the sessions.

In-app payments

The AI based mental healthcare app charges users for accessing features – with extended functionalities such as 150+ therapeutic exercises, access to the complete library of sleep sounds, stories, meditations, etc.

Progress report 

An app similar to Wysa can come with a progress report feature where the users can track the progress they have made in the in-app exercises, have daily check-ins which record their mood, and the times they have shown up for scheduled sessions, etc. 

Notifications

When in the mental health app development space, it is critical to focus on notifications. The idea is to not overwhelm the users by sending too many notifications or not sending them important updates at all. Your push notification mechanism should be well-timed to align with the users’ needs. 

Multiple language support

The application, to be more localized, has expanded its language offerings from English and is constantly adding more to the list. This move works great when you operate in a region where there are multiple language-speaking nationals. 

While this is from the end-users end, let us look into the feature sets specific to the other two stakeholders – Therapists and Administrators. 

Therapists version 

Compared to the patients’ version, the therapists’ side of the application has limited functionalities. It is majorly made up of the following features set. 

Initiate conversations

The therapists can initiate conversations with the app users after getting notified that somebody wants to have a session. The session typically is of three types – audio call, video call, and in-app messaging but what is unique with the Wysa mental healthcare app is that the users and therapists can converse between sessions as well.

Report a message 

The in-app therapists also get the option to report a message or mark SOS when they feel that the users require immediate attention. 

Admin version

The last version of mental health app development that businesses should focus on is the one that the admin team will use. While it is difficult to know the exact features of this version, here are some that we believe should be a part of an app similar to Wysa. 

App performance

The admin should be able to view and track the app’s performance in terms of new customers’ onboarding, their in-app sessions, feedback, etc. 

Memberships view and management

The application should make it easy for the admin team to view memberships – active, about to expire, and canceled. This set of information will help the team in planning respective marketing efforts toward keeping the users involved in the application. 

Therapists’ view and management 

Managing – adding and removing therapists from the application – should be made easy in the application with the help of a single view in the dashboard. 

FAQs management 

As the application grows, the FAQs and privacy policy should be constantly updated to answer users’ queries. This is where FAQ and policy documents’ management becomes important. 

Reports and SOS management

Managing issues and SOS requests on time is also a key part of Wysa-like application development. Admins should have the functionality where they can track all the app issues and manage the SOS requests from within the dashboard. 

Now that we have looked at the biggest factors of AI-powered mental healthcare app development, let us look into the stage-wise cost of developing an app like Wysa. 

Stage-wise Wysa Mental Health App Cost

Estimating the cost of developing an AI chatbot-powered mental healthcare application like Wysa involves multiple factors and can vary significantly based on the complexity, features, and development rates in different regions. 

Below is a high-level breakdown of the different development stages, along with approximate costs and development hours:

Project Planning and Research

• Hours: 40-80
• Cost: $2,000 – $4,000

UI/UX Design

• Hours: 80-160
• Cost: $4,000 – $8,000

Frontend Development

• Hours: 200-400
• Cost: $10,000 – $20,000

Backend Development

• Hours: 300-600
• Cost: $15,000 – $30,000

Integrating AI in mental healthcare app

• Hours: 400-800
• Cost: $20,000 – $40,000

Integration with Mental Health Tools

• Hours: 80-160
• Cost: $4,000 – $8,000

User Authentication and Data Security

• Hours: 120-240
• Cost: $6,000 – $12,000

Testing and Quality Assurance

• Hours: 160-320
• Cost: $8,000 – $16,000

Deployment and Launch

• Hours: 40-80
• Cost: $2,000 – $4,000

Post-Launch Support and Maintenance (first year)

• Hours: 200-400
• Cost: $10,000 – $20,000

Please note that these figures are rough estimates and actual costs can vary based on specific project requirements, the geographic location of the development team, and any additional features or integrations. 

While we have looked into one aspect of the Wysa-like mental health app cost – features set, let us look into another factor that is a part of this cost range. 

Technology stack for Wysa-like application

The technology stack for an AI-driven mental healthcare application like Wysa can vary based on specific requirements and preferences. However, here’s a generalized technology stack that could be suitable for developing a similar application.

Frontend

React Native or Flutter: For cross-platform mobile app development.

JavaScript/TypeScript, HTML5, CSS3: For building the user interface.

Backend

Node.js or Django: For server-side application logic.

Express.js or Flask: As a lightweight web application framework.

Python, JavaScript: For backend programming.

Database

MongoDB or PostgreSQL: Depending on the data structure and requirements.

Firebase: For real-time data synchronization and storage.

AI and Chatbot Integration

Natural Language Processing (NLP) Libraries: Such as spaCy or NLTK for text analysis.

Machine Learning Frameworks: TensorFlow or PyTorch for training and deploying machines learning models.

Dialogflow or Rasa: For building conversational AI/chatbot capabilities.

Authentication and Authorization

OAuth 2.0 or JWT: For secure user authentication and authorization.

APIs and Third-Party Integrations

Twilio or Nexmo: For SMS notifications and reminders.

Payment Gateway Integration: If the app includes premium features or subscriptions.

Real-time Communication

Socket.io or WebSockets: For real-time chat functionality.

Cloud Services

AWS, Google Cloud Platform, or Azure: For hosting, storage, and other cloud services.

Firebase Cloud Functions: For serverless computing.

Security

SSL/TLS: For secure data transmission.

OAuth/OpenID Connect: For secure API authentication.

Version Control and Deployment

Git: For version control.

Docker: For containerization.

CI/CD tools: Such as Jenkins or GitLab CI for automated testing and deployment.

Monitoring and Analytics

Google Analytics or Mixpanel: For user analytics.

Logging and Error Tracking Tools: Such as Sentry or Loggly.

This is a comprehensive stack, and the choice of technologies may depend on factors like the development team’s expertise, scalability requirements, and specific features of the mental healthcare application. Moreover, compliance with healthcare data regulations should always be considered a priority, and the technology stack should align with those requirements. 

Now that we have looked into the number side of the business model, let us get down to the USP aspect of the application. What would give your Wysa-like app a leg up in the market. 

Having worked on similar projects in our AI app development services portfolio, we have gained not just technical expertise but also a deep-level understanding of the mental health industry. A skillset that can help you get a massive edge over your competitors. 

How to get a competitive edge?

Considering the fast-growing mental health app market size, it is critical to launch an application which is built to take the existing apps in the market, heads-on. Here are some ideas that we would suggest to our clients looking to enjoy the benefits of mental healthcare apps.

Virtual Reality (VR) Integration

Explore the use of VR for immersive therapeutic experiences, creating virtual environments that aid relaxation and mindfulness.

Wearable Device Integration

Connect the app with wearable devices to gather additional health data, providing insights into users’ physical and mental well-being.

Social Support Communities

Build a community within the app where users can connect with others facing similar challenges, fostering a sense of support and camaraderie.

These are only a few ideas of how you can get an edge over the other players in the market. Talk to our business consultants and be a part of an extensive discovery workshop session where more such ideas will be discussed along with dedicated support for end-to-end application conceptualization, development, and deployment. 

Partner with our healthcare app development company to get your Wysa-like app idea to life with a product that is not only advanced and efficient but also compliant with all the necessary rules. 

FAQs

Q. What factors influence the cost of building an app like Wysa?

A. The factors which can influence the cost to build an app like Wysa include-

• Design system
• Features
• Number of platforms 
• Technology stack
• Development time and resources. 

Q. What are the common features that I should consider to build a Wysa-like mental health application? 

A. Wysa-like app is typically made up of three versions, each with their own feature sets. Let us dive into them.

• End-users version

Questions, AI chatbot, Exercises, Therapist, Journal, In-app payments, Progress report, Notifications, and Multiple language support.

• Therapists version 

Initiate conversations and Report a message 

• Admin version

App performance, Memberships view and management, Therapists’ view and management, FAQs management, and Reports and SOS management.

The post How much does it cost to develop an app like Wysa? appeared first on Neojn Blogs.

One such convergence that we will dive into today is generative AI and blockchain. Blockchain offers a secure and decentralized approach to storing and sharing information, while generative AI powers self-learning and creativity in machines and algorithms. By combining the two technologies, enhanced systems can be built that are not just secure but intelligent and adaptive.

While the merger is still being studied on the front of efficacy and profitability, tech enthusiasts have started coming up with probable use cases of their convergence. Let us look at some of them. 

The fusion of blockchain technology with generative AI

The integration of generative AI with blockchain offers immense potential for industries to get creative, and productive with the assurance of security and accountability. Let us look into the role of blockchain in generative AI to understand the extent of their combined offerings. 

Enhanced data integrity and security 

Blockchain’s distributed ledger functionality offers a great solution for securing data utilized by generative AI. By storing the data in a decentralized, tamper-proof place, blockchain ensures its integrity and security. This, in turn, protects against manipulation, unauthorized access, or data compromise, making generative AI outputs a lot more accurate and reliable. 

Guaranteed provenance and transparency

One of the biggest challenges that businesses aim to solve with blockchain in generative AI is guaranteeing traceability and transparency in the training models’ data. The immutable nature of blockchain enables the execution of an audit trail that would verify the origin and authenticity of data. This level of data provenance leads to better trust between stakeholders and users, allowing them to validate the quality and authenticity of data. 

Powering decentralized training 

By using the blockchain’s distributed functionality, training can be held across an entire network of nodes, which reduces the dependency on centralized data centers and servers. This level of decentralization improves scalability, grows resource efficiency, and promotes collaboration among participants, making the generative AI model a lot more cost-effective and accessible. 

Improved efficiency and scalability 

Generative AI solutions demand extensively large datasets and computational resources for training. Blockchain’s scalability prowess can easily accommodate these demands. By using its distributed computing power, the generative AI models can process large data points, expediting the inference and training tasks. 

This scalability also plays a role in lowering the processing time, enhancing performance, and extending the scope of new generative AI use case development. 

Better trading strategies 

The volatility of cryptocurrencies needs traders to adapt quickly and make timely decisions. Generative AI and blockchain become crucial here since by analyzing the historical data and identifying the trends, and patterns, the AI model can help the traders make informed decisions. This role can further be seen to influence DeFi platforms as well where generative AI can give personalized recommendations on risk management and investments based on users’ behavior and transaction data. 

Innovative tokenization

Tokenization refers to the process where real-world assets are brought to the blockchain. Generative AI solutions can help revolutionize the process through the automation of the creation and handling of tokenized assets. By using the technology, the entire tokenization of assets process can be made accurate, secure, and efficient – an advancement that directly impacts fractional ownership, allowing individuals to make investments in high-value assets such as artwork or real estate. 

Secure marketplace for AI content 

Combining blockchain with AI can power the creation of secure market platforms for generated content and AI models. Companies can use the combination of smart contracts and generative AI to enable automated transactions that ensure all the licenses, ownership, and usage rights are transferred securely and transparently.
The AI models can get deployed as dApps on the platforms, making it possible for the developers and users to access and use the models through a decentralized approach. 

Addressal of intellectual property issues

The two ways AI and blockchain combination can be used for solving IP issues are – Controlled Usage and Unintended IP Infringements.

Creators can add IP assets to a blockchain network as non-fungible tokens with smart contracts that would establish clear permissions – attribution requirements, free usage, or compensation terms for reuse, directly in the AI models. This approach guarantees precise control over how AI interacts with proprietary data thus lowering the chance of IP violation or misuse.

A blockchain-powered IP system can also address unintentional IP infringements. Businesses can establish a system where every utilization or transaction made on AI-generated content comes with corresponding payments. This system makes sure that the creators automatically get compensation when their content is used by any machine learning model.

Looking into the role of blockchain in generative models and vice versa on a holistic level will be insufficient if you wish to understand the entirety of the convergence. To get a complete grasp on the technologies’ combination, let us look into their industry-wise impact. 

Real-world use cases of blockchain integration in generative AI

The potential impact of using blockchain for generative AI or generative AI in blockchain projects is massive and far-reaching in terms of industry-wide scope. While a use case can be built for every industry where either blockchain or generative AI can be integrated, let’s have a look at the obvious participants.

Supply chain

Blockchain holds the potential to enhance traceability and visibility across the entire supply chain. Generative AI can build upon this advantage further by powering real-time decision-making for optimizing routes, cutting costs, and navigating unexpected gaps.

Smart contracts can also utilize AI predictions to place new orders in the hopes of future resource shortages. All of this can ultimately foster a proactive and agile supply chain network.

Financial solutions 

Blockchain-powered DeFi solutions have already transformed traditional financial processes. When merged with generative AI, the technology can also forecast market trends, study historical transactions, and detect possible fraudulent activities.

Smart contracts can also be used to study AI-driven market insights for adapting loan interest rates in real time. It would ultimately expand flexibility within the DeFi ecosystems. Real-world applications of this combination in the fintech domain can be seen in –

• Symbiont: A fintech company using blockchain to build generative AI models that predict financial markets. Their models are being trained on massive datasets of historical financial data, which can be used for making accurate predictions compared to the traditional methods.
• R3: R3 is a fintech company building a blockchain-based platform for financial solutions. This platform can be used for training generative AI models based on large financial datasets, which can help improve these models’ accuracy.

Energy grids

Blockchain can ensure efficient and transparent energy transactions and distribution. When clubbed with a generative AI model, it can optimize the distributions of energy grids in real-time, accounting for variables such as supply, demand, and weather conditions. 

This approach promises a transformative shift focused on decentralized and adaptive energy infrastructure. Moreover, the integration can foster an efficient grid with the capability of autonomously adapting to varying conditions. It, in turn, can expedite the transition towards renewable and sustainable energy solutions. 

Healthcare 

Blockchain technology has already proven to offer a safe platform for the healthcare domain to store patient records. It can manage a high level of data integrity and adhere to all the privacy regulations. Integrated with generative AI, healthcare experts can analyze the secure dataset to identify patterns, estimate potential health concerns, and suggest customized treatment methods.

For example, generative AI algorithms can be built to evaluate medical scans stored on the blockchain to predict a patient’s chances of having certain illnesses. Blockchain can record all the diagnoses and alterations to facilitate audits and ensure transparency. Some companies working in this direction include – 

• Enlitic: A healthcare company using blockchain to build generative AI models that can find and diagnose diseases. Their models are developed on large medical images and datasets, which they use to make accurate diagnoses.
• MediLedger: A blockchain-based platform tracking and sharing medical records. The platform is being used for training generative AI models on large medical records datasets that can help improve the models’ accuracy.

Now that we have looked into the benefits of integrating blockchain in generative AI along with some real-world use cases, let us get down to some challenges that a blockchain development company can come across while in the process. 

Challenges of combining generative AI and blockchain

While the convergence between blockchain and generative AI offers a lot on the innovation and applications level, there are some challenges that both generative AI development company and blockchain solutions developers face. 

Effectiveness and scalability

One of the primary issues with applying generative AI in the blockchain ecosystem is guaranteeing efficiency and scalability. The resource-heavy nature of AI algorithms can put a strain on decentralized networks and slow the system’s performance. To solve this, developers are actively looking at creating lightweight AI models that run seamlessly on distributed networks. Additionally, approaches such as quantization, model pruning, and federated learning are explored to strike a balance between network efficiency and AI complexity.

Data security and privacy

Blockchain’s focus on privacy and data sovereignty builds specific challenges for generative AI. Traditional AI models tend to depend on a centralized state of data sources, which contradicts the decentralization principles and user ownership in blockchain. Striking a balance between user privacy and AI training data is critical. At Neojn, we recommend following the Federated learning approach, where AI models get trained locally on user devices, helping preserve data privacy while elevating AI capabilities.

Diversity and quality of output

Generative AI models are prone to low-quality, biased, and repetitive content. On the front of blockchain where trustlessness and diversity are crucial, making sure that the AI-generated content meets these specifications becomes essential. Researchers are working on designing inclusive training datasets and modifying model architectures to address bias. Moreover, adversarial training and reinforcement learning can better the diversity and quality of AI-generated outputs.

Attribution and intellectual property

Blockchain’s decentralized model causes concerns around the attribution and ownership of AI-powered content. Traditionally, creators used to rely on intellectual property laws to protect their work. However, with AI-backed content, finding the level of human involvement and ownership becomes extremely complex. We suggest using smart contracts to ensure transparent ownership and an automatic attribution mechanism.

Interoperability and standardization

The entire blockchain ecosystem consists of multiple protocols, blockchains, and platforms, each of them coming with its own set of architectures and rules. Ensuring interoperability between multiple generative AI systems around this wide landscape can become a challenge. Collaborative efforts made towards establishing common protocols and standards for AI models’ interaction and deployment can aid seamless integration across every Web3 platform.

Ethical and legal considerations

As AI-powered content becomes integrated into the fabric of blockchain, legal and ethical issues become prevalent. From the probable misuse of AI-powered content to unplanned consequences, a strategic framework is needed to ensure ethical and responsible AI deployment. To solve this, the entire AI community and regulatory bodies will need to collaborate and craft guidelines for the ethical use of AI in blockchain.

At Neojn, we carry expertise in building both generative AI and blockchain applications – a knowledge set that makes us your right partner for integrating the two technologies into your project. Get in touch with us to discuss your innovative business idea. 

FAQs

Q. What are the benefits of integrating blockchain technology with generative AI models?

A. Several advantages come linked with the combination of blockchain in AI.
Here are the top ones: Enhanced data integrity and security, Guaranteed provenance and transparency, Powering decentralized training, Improved efficiency and scalability, Better trading strategies, Innovative tokenization, Secure marketplace for AI content, and Addressal of intellectual property issues.

Q. Is there a connection between NFTs and generative AI?

A. Several businesses are planning to connect generative AI and NFTs in a way that the content generated through AI can be converted into an NFT to be then sold in a blockchain marketplace that manages all the transactions through smart contracts.

Q. Are there any industry-specific applications of blockchain in generative AI?

A. Yes. Almost every industry can benefit from the merger of AI and blockchain. Here are some examples. 

Supply chain: Blockchain holds the potential to enhance traceability and visibility across the entire supply chain. Generative AI can build upon this advantage further by powering real-time decision-making for optimizing routes, cutting costs, and navigating unexpected gaps.

Financial solutions: Blockchain-powered DeFi solutions have already transformed traditional financial processes. When merged with generative AI, the technology can also forecast market trends, study historical transactions, and detect possible fraudulent activities.
Smart contracts can also be used to study AI-driven market insights for adapting loan interest rates in real time. It would ultimately expand flexibility within the DeFi ecosystems. 

Energy grids: Blockchain can ensure efficient and transparent energy transactions and distribution. When clubbed with a generative AI model, it can optimize the distributions of energy grids in real-time, accounting for variables such as supply, demand, and weather conditions. 

Healthcare: Blockchain technology has already proven to offer a safe platform for the healthcare domain to store patient records. It can manage a high level of data integrity and adhere to all the privacy regulations. Integrated with generative AI, healthcare experts can analyze the secure dataset to identify patterns, estimate potential health concerns, and suggest customized treatment methods.

Q. What are some examples of successful integration of blockchain in generative AI?

A. Here are a few –

ChainGPT: The company comes with a wide range of solution offerings – An AI-based chatbot that answers every question around crypto and blockchain, a news model, a smart contracts generator, an AI NFT generator, AI trading, and security extension, etc.

Enlitic: A healthcare company using blockchain to build generative AI models that can find and diagnose diseases. Their models are developed on large medical images and datasets, which they use to make accurate diagnoses.

MediLedger: A blockchain-based platform tracking and sharing medical records. The platform is being used for training generative AI models on large medical records datasets that can help improve the models’ accuracy.

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Neojn Technology has consistently demonstrated a deep-rooted commitment to innovation. This ethos is reflected in its groundbreaking solutions that have revolutionized various industries, from healthcare to fintech.

This award is not just a testament to Neojn’s technological prowess but also to its client-centric approach. The company has a track record of delivering exceptional results, ensuring client satisfaction and measurable business impact.

With this recognition, Neojn Technology is poised to continue its upward trajectory, leading the charge in cutting-edge software development. The company is committed to maintaining its high standards and pushing the boundaries of what’s possible in the tech world.

Founded in 2014, Neojn Technology has established itself as a leading force in the software development industry. With a team of skilled professionals and a robust portfolio of successful projects, Neojn stands at the forefront of technological innovation.

About Techreviewer.co

Techreviewer.co is a respected authority in the tech industry, known for its rigorous evaluation criteria and unbiased reviews. Being included in their list of top software development companies is a significant achievement, reflecting a company’s industry leadership and commitment to quality.

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Being constantly on top of market movements, we know that data does not back this notion. If anything, fintech investors have become overly conscious over time because of the emergence of a myriad number of projects with poor business models and severe operational inefficiencies. 

To find a space in investors’ portfolios, fintech businesses will not have to think of a blue ocean strategy – either solve a problem that no one else is or change the approach to solving an existing problem revolutionarily. Moreover, they will need to keep a lookout on the macro events at play, for example, US elections where it can be perceived that if Trump comes into the picture again there’ll be fewer regulations in cryptocurrencies in the USA, otherwise, crypto projects will face many complex regulations and laws. This is only one example of many geopolitical events entrepreneurs must prepare for. 

Assuming you are in the market with an immense interest in the fintech space and an end goal of getting funded, it is critical that in addition to understanding the micro and macro players, you have a high-level idea of what it is that investors are looking for from fintech subsectors. 

While it is almost impossible to be certain about which sub-domains would get you investment in fintech, since it ultimately boils down to innovation and business models, let us look at some fintech verticals that are showing a promising sign. 

The most investors-friendly segments of fintech industry

The fintech market is known to be the one with maximum innovation-friendliness. With the strategic implementation of new-gen technologies like AI, Blockchain, automation, IoT, etc., the domain operates in a mode of constant newness. 

Out of the many verticals that have gained prominence in the fintech space, here are the ones that got the major chunk of funding in 2023, along with the amount. 

Between these and some sub-sectors that continue to get investors’ attention, we have made a list of some hot fintech investments categories.

Wealth management 

Wealth management or wealthtech as a service is a broader term for applications that deal with an individual’s wealth management across multiple facets like savings, investments, budgeting, and even automated portfolio management. 

From a business perspective, some wealth management software ideas can be – financial advisory, investment management, neo-broker trading, and pension, savings applications. 

Embedded finance

Embedded finance is when you integrate financial services in non-financial offerings. A few examples of the concept can be seen in an e-commerce merchant like Amazon providing insurance, a coffee shop app like Starbucks providing 1-click payments or a department store’s credit card. 

At the back of the convenience it offers to the users, it is estimated that embedded financial services will generate over $384.8B in revenue by 2029 – a nearly 17x increase over the $22.5B in revenue it generated in 2020.

Lending

Lending is one of those segments of fintech industry that remains at the forefront of new technology adoption and innovations. It uses financial technologies like APIs, to enable lenders with faster, more informed lending decisions. The same technology comes in handy when using alternative sources of data to measure the lending risk and linking digital platforms for improved data sharing speed. 

One of the popular fintech subsectors, it empowers traditionally underserved P2P and business borrowers by providing an alternative source of funding and helping better their financial health and freedom. 

Decentralized finance

2024 is shaping up to be a potentially critical year for the crypto industry, especially for institutional investors.

With multiple factors coming together, including an implementation of the Markets in Crypto-Assets (MiCA) legislation in the European Union and pending approval of Bitcoin and Ethereum Exchange-Traded Funds (ETFs) in the United States, the platform is being set for an impactful shift in crypto assets adoption and its mainstream acceptance.

Another event that could power the rise of the crypto market is the April 2024 scheduled Bitcoin halving. If history repeats itself, 2024 halving can ignite another explosive bull run, which would attract institutional investors looking to capitalize on the potential gains.

In addition to crypto-centric projects, the domain is also witnessing a rise in questions about how to create a DAO, backed by the intent to increase the number of projects in the domain.

Banking software

Banking apps or banking as a service software has been receiving constant attention from investors, especially since traditional banks have entered the crucial phase of their journey: digitalization. On the business model end, software companies are finding them getting more inclined toward open banking and B2B SaaS products for banks, while on the banks’ end, the focus can be seen on establishing partnerships between them and tech-first companies to either sell banking services on their platform or using their technology like blockchain or AI to advance banking solutions. 

B2B payments 

B2B payments as a service platform aims to solve the pestering issues of the company to vendors’ payments domain. According to pymnts.com, 63% of invoices need two to five signoffs, it takes at least 14 days to process an invoice, and 80% of all the b2b payments are made via check. This complex set of approaches has been holding the domain back while the overall payment space is becoming innovation-first. 

This is where B2B payment software comes into play with their digital-first offerings. 

Here were the top fintech verticals that are expected to witness massive investor interest. Now that we have looked into the fintech investments landscape, here is what you – an entrepreneur interested in entering the space with the end goal of getting funded should do. 

What do investors expect from a fintech product?

After getting a high-level idea of the fintech product you would be building, it is equally critical to have some pointers to get you a seat in front of investors. 

Strong business model 

The number one expectation that funding agencies have when making investment in fintech is of a strong business model. Your product idea should solve mass-level issues with a creative approach, moreover, it should be unique in terms of your competitors’ offerings and revenue model.  

Skilled team 

Next to the business model, fintech market investments are majorly seen in companies that have a strong core team. So we recommend having industry veterans on your board along with people who have established their brand as being subject matter experts.

Market acceptance 

Gather data on how well the market is accepting your product by surveying real users. One way to approach this would be to create an MVP of your fintech application and then send it to real users in return for motivators like free premium versions, cashback, or coupons. 

What if we told you that there is a way you can manage financial application development and spend time charting through the complexities of fintech investments? The way to make this possible lies in partnering with a fintech app development company like us. We have a team of subject matter experts who specialize in the design, development, and deployment of multiple use cases of the finance domain. 

At Neojn, we have an extensive finance app development services portfolio of products ranging from crypto payments to banking-as-a-service platforms, and even budgeting applications. Explore how we can translate our expertise into your business’s growth.

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Blockchain technology has found a solution to this problem through DAOs – decentralized autonomous organizations. It is a DeFi project that makes use of distributed ledger technology and smart contracts for executing governance decisions based on voting outcomes. Here’s a high-level view of how DAO platforms work. 

• A project gets launched having a defined purpose and specialty. 
• The users join the project by buying native coins and getting voting rights in return. 
• The community members vote on business decisions with every member having the necessary number of tokens getting the voting right. 

Now with something so inherently complex to build and revolutionary, what does a decentralized autonomous organization have to offer that would make it eventually replace the age-old hierarchical format of making decisions?

Traditional organizations vs DAOs

DAOs and traditional organizations are completely different in concept and working. While the former deals with complete decentralization and an emotion-less mode of operation, the latter is more dependent on what individuals’ position in the business.

As you would have gathered by now, DAOs and traditional organizations work with the same end goal – to make decisions easier and free from bias. The only catch is that DAOs make it all easier.  With this, it is time to look into the how. 

How to build a DAO?

At the core, a DAO development platform consists of four key elements – Governance, Objective, Voting, and Rewards. While we have already covered how the platform works, understanding it to a level where you are involved in creating a DAO would need a detailed step-by-step understanding.

Establish the purpose

The primary stage of answering how to create DAO is identifying why it is necessary, the role it would play, and how it would work. While the discovery workshop session will answer all these questions, on the technical side you will need to plan out a foundation having – 

• Encrypted wallet allowing for token storage and transactions
• Smart contracts powering automated processes
• Community of key members
• Specific voting timeline
• Forum or chat room option for members

Plan ownership and voting mechanisms 

Once you know the goals to set up when you build a DAO, next comes establishing the ownership and planning out the voting mechanisms. When it comes to transferring ownership, you will typically get three options – 

• Airdrops – Tokens are given based on contributions to the community
• Rewards – Payments made to members who achieve their duties 
• Token purchase – List tokens on decentralized exchanges

Now as for the voting mechanisms, a popular way is to decide the votes based on the number of tokens. The side that has maximum tokens will get to call the final shot. 

Build the governance structure 

This phase of when you create a DAO deals with detailing the decisions that will be made after the DAO has been set up. It will also have information on processes’ use cases, voting mechanisms, and clarity on the DAO’s essential components, such as – 

• The exchange that will hold the transactions
• Developers who will work on the code 
• Validator who will verify the transactions 
• Users who will participate in the community
• How will the DAO make money – the sale of native tokens or investors’ shares in return for their investments in an early-stage project

Set up incentives or rewards 

The last stage of building DAO in blockchain is setting up a rewards system for the DAO members to pay in turn for their contributions. Usually, the governance tokens are distributed to the contributors who use the DeFi protocol and represent the ownership rights that they carry. The members can also be rewarded through cryptocurrencies, grades, titles, or a combination of them. 

Token creation and allocation 

Once every goal, governance rule, and incentive system is in place, concentrate on how the DAO tokens would work. Noting how the tokens would decide the voting rights, you should have tokenomics in place which would clearly explain the token’s purpose, their allocation, how they would affect supply, etc. 

Now that we have looked at what is DAO and what are the different steps you would need to take to answer how to create DAO, let us get down to the one question that we are sure you must be asking – what would be the cost to start a DAO?

Let’s answer that in a bit after looking into the technology stacks that would help you build the DAO and then launch it in the market. 

Technology stacks for building and launching a DAO

These technology sets when added with the type of DAO you wish to create and its specific features would ultimately decide the cost of your DAO blockchain development solutions. As we conclude the article, let us give you a peek into what to expect when it comes to deciding the cost range you wish to spend on the project. 

What would be the cost of a DAO project?

The cost of DAO project, as we discussed above, would primarily depend on a mix of – type and its features, technology stack, and the blockchain platform you choose. 

Additionally, here are some costs you will have to keep a note of. 

• The DAO setup cost on Aragon and DAOstack Alchemy is 2ETH. 
• Registering DAO as LLC would cost somewhere around $100. 
• You’ll need to hire a blockchain app development services company to create an internal token (ERC-20) and generate its supply, voting system with consistent smart contract logic and a safe functioning.
• DAO smart contract audit 
• The DAO should also have a platform for member communication and essential news updates.

With this, we have looked into every aspect of DAOs – the information you would need to initiate your project with confidence. 

This is the stage where you partner with a blockchain development agency that doesn’t just understand your project’s concept but also has an experience in converting complex blockchain projects into solutions that have a mass usage. Enter Neojn. 

At Neojn, we have a dedicated blockchain R&D division that runs hypothesis tests on different decentralization-focused use cases including DAO. We are prepared to work on your project with the right tech expertise and team. Share your idea with us today. 

FAQs

Q. How does DAOs work? 

A. A Decentralized Autonomous Organization (DAO) operates on the principles of blockchain technology, embodying decentralized decision-making and governance. 

In a blockchain DAO, smart contracts facilitate the organization’s functions. The participants hold tokens that represent their voting power, enabling them to propose and vote on decisions, such as changes to the protocol or allocation of funds. These proposals are executed automatically through smart contracts if they meet the predefined criteria. 

Q. How can DAOs be used? 

A. Decentralized Autonomous Organizations offer a versatile framework for collaborative decision-making and resource management. They can be employed across various industries and applications, ranging from governance and finance to art and gaming. 

• In governance, they enable communities to collectively steer the direction of projects or protocols, fostering a democratic approach to decision-making. 
• In the financial sector, DAOs provide a transparent and decentralized way to manage funds, enabling trustless transactions and reducing reliance on traditional intermediaries. 
• Additionally, DAOs can revolutionize the creative industries by allowing artists to collaboratively govern digital assets and distribute rewards based on community input. 

Q. What are some common types of DAOs? 

A. Some common types of DAOs include – 

• Governance DAO: MakerDAO
• Funding DAO: The DAO (though it faced issues and resulted in a fork leading to Ethereum and Ethereum Classic)
• Collective Ownership DAO: ConstitutionDAO
• Decentralized Venture Capital DAO: MetaCartel Ventures
• Decentralized Autonomous Exchange (DEX DAO): Uniswap Governance
• Decentralized Autonomous Nonprofit: Gitcoin Grants DAO

Q. How much does it cost to build a DAO project? 

A. There is no fixed way to answer this question. The cost to form and then launch a DAO would depend entirely on the type of DAO project you choose and its specific feature sets, the technology stack it will be built on, and the blockchain platforms you will be using.

The post How to create a DAO on blockchain? appeared first on Neojn Blogs.

For some entrepreneurs, it is the digitalization of traditional processes while for others it is using technology in business processes, customer experiences, and culture. While the end goal of the efforts can vary from company to company, one thing is clear: legacy technology investments that once differentiated organizations no longer suffice. This has led to the onset of cloud computing in digital transformation.

In this article, we will explore the nuances of the cloud in digital transformation – its role, the best implementation strategies, and challenges. The idea here is to prepare you for taking your business to the next level with the capabilities of cloud technology.

The role of cloud computing in digital transformation

Emerging as the foundation of digital transformation efforts, the cloud is pushing companies toward their always-online versions with the ability to harness innovation and unlock hidden growth opportunities.

Unmatched flexibility and agility

One of the biggest signs of cloud accelerating digital transformation can be seen in the agility and flexibility the technology offers. The days of on-premise infrastructures and legacy systems are long gone. With the ease of scaling resources (up or down) effortlessly, businesses are entering a new era of multi-scenario adaptation – one where they are launching new products, entering new markets, and unlocking immense growth opportunities.

High cost efficiency

The combination of cloud computing and digital transformation brings forth massive cost efficiency for a company. With the cloud, businesses only have to pay for the resources they use while eliminating all the capital expenses, maintenance costs, and the efforts of managing dedicated teams.
Typically, the cost of digital transformation is explored through three dimensions –

• Calculation of the potential value from IT cost efficiencies around app development and infrastructure spending.
• Cost saving from business operations that focus on IoT, analytics, and automation.
• Costs of exploring new business opportunities with the help of new-gen technologies.

Cloud for digital transformation has a role to play in all three dimensions, making them affordable and accessible for businesses across multiple sizes.

Real-time collaboration

With the corporate world moving to the hybrid mode of operations, cloud-powered collaboration and messaging platforms come to the forefront. Through cloud computing and digital transformation, the teams can access documents and interact with their and other teams from anywhere across the globe. This transition from physical to digital operations, helps businesses double their productivity and unleash incredible innovation with half the money and efforts.

Better security and compliance-readiness

Security breaches have sadly become part and parcel of digitalization. Cloud computing, however, addresses this head-on. The top cloud enabled digital transformation providers like AWS, Microsoft Azure, Google Cloud Platform, etc. invest extensively in encryption protocols, security measures, and unplanned audits to ensure that their infrastructure remains hack-proof.

Supporting innovation

Cloud transformation helps businesses tap into the capabilities of new-gen technologies and toolsets. To remain competitive in the cloud space, providers tend to constantly update their offerings to add the latest advancements in AI, ML, IoT, and now generative AI. Because of this, companies get a readymade repository of innovative tools, ready for experimentation, development, and deployment. 

The benefits of combining digital transformation and cloud computing can truly be realized after its correct implementation in the organization. Let’s look into the stages of integrating the cloud into your operations.

How to implement cloud computing in digital transformation?

When working on the implementation of cloud enabled digital transformation, here are the stages that your organization might come across.

Plan

In addition to outlining the key success metrics, your cloud transformation planning should answer the following questions – what does a successful cloud strategy mean for your business? Do you completely want to move an application to the cloud? What would be the learning curve for your team? What percentage of downtime are you expecting?

Identify roadblocks

Know potential issues you might face when you move your processes to the cloud. For some businesses it is security, for others it can be complex migration efforts and team resistance. Find the challenge specific to your organization and see if it is something that you can eliminate with the help of resources you currently have.

Modernization decisions

Spend time evaluating your infrastructure needs, if there are resources that you are currently hosting but are not necessary, don’t bring them to the cloud. To make this decision easy, you should look into the areas that would benefit from modern technologies or approaches and the cost, time it would take to make them cloud-only.

Cloud deployment

Once you have clarity on the processes that will be modernized, the next step that comes is deployment. It is one of the key stages of cloud transformation where the processes are categorized into bite sizes and migrated to the cloud on a module-by-module level. It is during this phase that access is shared between the right owners and team members and user acceptance testing is carried out on a company level.

Seems pretty straightforward, doesn’t it? On paper it is, but in reality, several things can go wrong with migration. This is where it becomes critical to look into the best strategies and practices.

Best practices and strategies for digital cloud transformation

By now you must have understood how cloud is the key to businesses innovating quickly, scaling efficiently, innovating easily, and seizing new market opportunities faster. However, to maximize the outcome of cloud computing and digital transformation, it is important to follow the best strategies and practices.

Define your roadmap and cloud strategy – As you get on your cloud digital transformation journey, plan a well-established roadmap and strategy. This means setting up clear objectives, aligning the efforts with business strategies, and understanding the user’s needs. Roadmap, on the other hand, should chart the timelines, steps, and milestones of implementation. 

Select the right cloud model – Choosing the best cloud deployment model is crucial to establish the suitability of cloud migration. The decision between the three prominent cloud models can have implications like refactoring, re-architecting, replacement, or retirement. 

While IaaS gives fundamental computing resources, PaaS provides development environments, on the other side, SaaS delivers ready-to-use applications and FaaS powers the developers to run individual functions with a cost-effective approach.

Prioritize cloud-native applications – Building and deploying cloud-native software designed especially for the cloud promises agility, scalability, and resiliency at the back of serverless architecture, DevOps, and microservices practices.
These applications are known to integrate effortlessly with on-premise and cloud-based applications which ultimately power innovation and flexibility in the system. 

Monitor performance and cloud resources – Ongoing monitoring and management of cloud resources are critical for ensuring the reliability, availability, and security of data and processes. Additionally, we recommend orchestration and automation of resource management and provisioning to get 360-degree control, visibility, and optimization of cloud operations. 

Implement cloud governance – Establish a security framework and governance that would define standards, policies, and best practices for cloud usage. This framework that you build must comprise factors like access and identity management, data protection, incident management, and a business continuity plan. 

While the above-mentioned stages can help with the right implementation of cloud computing and digital transformation, companies should remain wary of the challenges that they might encounter in the journey.
As we near the end of the article, let’s look into some challenges that businesses can face with cloud adoption and their solutions.

Overcoming challenges in digital transformation

Let us explore some of the most commonly occurring roadblocks for businesses looking to reap the benefits of cloud computing and their best probable solutions.

Security

Keeping the data secure in the cloud system can sometimes pose a challenge, especially since the cloud providers tend to share their resources and infrastructures with multiple companies. The solution to this lies in integrating security mechanisms and monitoring cloud environments for vulnerabilities. We recommend using access controls, encryption, and security audits to protect the data.  

Cost management

Cloud computing can get expensive when not managed accurately. Businesses tend to face unplanned expenses because of storage, overprovisioned resources, and data transfer in multi-cloud environments. Solutions that we propose for this are using cloud cost management tools like Cloud Health and CloudWatch, implementing auto-scaling for adjusting resources based on demand, reviewing cloud usage to identify areas where one can cut costs, etc.

Data migration 

Transferring data to the cloud can lead to probable data loss and downtime during the migration journey. Tackling this expensive activity would require you to choose a migration strategy between re-platforming, lift-and-shift, and refactoring. Additionally, you should follow practices like backing up your data, performing test migrations, and having a rollback strategy in case of issues. 

Multi-cloud strategy 

Handling different cloud environments with their own set of APIs, tools, and billing structures, can complicate the process of gathering an overview of the cloud expense and usage. Solving this would require you to invest in a cloud management platform that gives you a clear view of all your cloud-powered activities. 

How can Neojn help with your cloud transformation journey?

In our role as a trusted digital transformation services provider, who has a dedicated expertise in cloud solutions, we have helped many businesses across different cloud journeys.

We have helped a few companies establish their cloud infrastructure by moving their data to a reliable cloud platform and for some we have kept a more consultative approach where we have aided them in choosing between the best providers after a thorough cost-benefit analysis.

Looking for similar assistance? Get on a call with our cloud consultancy services experts today.

FAQs

Q. What is cloud enabled digital transformation?

A. Cloud-powered digital transformation is the process of adopting and making use of cloud computing technologies to transform the ways a business operates and delivers value to its customers, employees, and partners.

Q. What are the advantages of cloud computing in digital transformation?

A. The benefits of cloud computing in digital transformation can be seen in unmatched flexibility and agility, high-cost efficiency, supporting innovation, powering real-time collaborations, and in ensuring compliance readiness.

Q. Which cloud is most suited for your business objectives?

A. It depends entirely on your needs. Public clouds are considered to be cost-effective and scalable but can raise security concerns. On the other hand, Private clouds provide enhanced security but can exceed the organization’s budget. Hybrid clouds provide a middle ground, allowing businesses to optimize workload and data distribution

Q. How can Neojn help in our cloud computing initiatives?

A. Our cloud experts at Neojn can play an active role in your entire cloud transformation journey. Right from suggesting the best cloud service platform to planning out your migration strategy and deploying your processes on the cloud – our team can do it all. 

The post How does cloud computing accelerate digital transformation? appeared first on Neojn Blogs.

At the intersection of a massive information set and unsurety of how to identify important information lies intelligent automation. Intelligent automation (IA), is the application of automation technologies – business process management (BPM), artificial intelligence (AI), robotic process automation (RPA), and Computer Vision – to streamline and scale up decision-making across businesses.

But just because there is a technology promising to make sense of your business data for you while automating your processes, do you need to incorporate it?

Whenever a client comes to us asking for intelligent automation in business, we ask them five questions whose answers then take the conversation ahead. 

• Do you need more scalability? Your business has plans to grow in the future – new customers, sales territories, etc. but are your processes ready to scale with this growth when these targets are achieved?
• Do you have a lot of manual work? Is the majority of your day going behind manual task completion like transferring information from one system to another?
• Are there a lot of exceptions? If you have already applied automation, are you seeing exceptions because of unstructured, semi-structured data, multiple formats, or system errors?
• Is there a lack of end-to-end process visibility? Do you have complete visibility of the company-wide processes and can you track and measure them? 
• Do you have a single source of information? A common sign to answer this is by looking into the number of sources you have to go through to collect or compare your data.

Assuming 4 out of these 5 questions were a ‘Yes’ for you too (even one yes would have been a good enough reason), this AI business automation article is for you. Let’s look into the trending technology and what you can expect from its implementation.

What are the components of intelligent automation?

When you seek the answer to what is intelligent automation? You will get an answer that the technology consists of a series of digital processes that work in a collaboration-powered system to optimize workflows. On a foundational level, multiple technologies power this process. 

Artificial Intelligence 

AI as you would already know solves cognitive issues linked with human intelligence. Powered by a toolset made up of NLP, Machine Learning, etc. it uses business data to become more effective after every transaction, interaction, and event. 

Machine Learning

It is the science of using models and algorithms to enable machines to perform tasks without any instructions. With the help of historical data, patterns, and inferences, the technology helps machines predict outcomes and act on them. 

Natural Language Processing

The technology enables computers to understand and interpret human language. The text data that you feed into an NLP software, ranging from emails, and texts to social media posts, can be processed for finding trends and replying to people like humans. 

Computer Vision

It gives the software human-like accuracy by identifying things, places, and people in images. The technology helps with the automation of processes like image extraction, classification, and identification. 

In addition to these core components, several other technologies are finding a place in the working of intelligent automation, here’s a look at them –

These elements that make intelligent automation in business possible, tend to leave a huge company-wide impact – ones that also define the benefits of intelligent automation. 

The benefits of business process automation

AI business automation powers businesses with advanced-level technologies and agile processes for smarter and faster decisions. 

Cost and time savings 

With the automation of time-consuming and repetitive tasks, intelligent automation technologies can lower the need for human intervention, thus saving valuable business resources and time. The streamline capability this offers directly leads to better operational efficiency and cost savings. 

Better accuracy 

Intelligent automation tools lower human issues and guarantee consistent intelligent document processing. With the help of AI-powered real-time data validation and decision-making, companies get access to reliable and accurate information which helps them get both proactive and predictive. 

Improved efficiency 

Some of the top applications of intelligent automation can be seen in optimized workflows, eliminated bottlenecks, and improved efficiency. With the automation of activities like document processing, data entry, and reporting, companies can have a dedicated focus on the core business goals and strategic activities. 

Increased data integrity 

Cognitive automation lowers the probability of data errors and inconsistencies, improves data reliability and integrity. Automation of validation and data extraction makes sure that companies have real-time access to accurate and clean data for reporting. 

Compliance-readiness

With the automation of compliance-specific processes and maintenance of standardized workflows, companies can ensure adherence to all regulatory requirements. Intelligent business automation also helps with enabling documentation and audit trails, which simplifies the overall compliance reporting journey. 

Elevated customer experience 

Intelligent automation in business powers personalized customer experience through automated customer support, customized marketing campaigns, and effective, on-time query resolution. This leads to a direct impact on loyalty and customer satisfaction. 

Now that we have looked at the benefits of cognitive automation, let’s study how these translate into the easing of everyday work processes. 

The most visible intelligent automation use cases

Business process automation sees a wide range of applications across industries and business sizes. An example of this can be seen through three very popular but very different companies and how they are using the technology. 

• Coca-Cola – using intelligent automation to streamline supply chain management 
• IBM – using IA for optimizing the financial reporting process 
• JP Morgan – implemented the technology for automating the data analysis of large volume legal documents. 

Here’s a more intricate-level view of how intelligent automation implementation can help your business. 

Financial reporting automation 

The technology can streamline the financial reporting process by automating data gathering, processing, and analysis. It is also able to generate real-time reports, collect data from multiple sources, and power accurate analysis. 

Contract management automation

Intelligent automation makes contract management efficient with the automation of contract creation, monitoring, extraction of relevant data, analysis of terms and conditions, and compliance adherence. 

Customer support automation 

IA betters customer support with the automation of response generation, ticket management, and issue redressal. Businesses also employ AI-based chatbots for providing instant support and managing routine queries. 

Fraud detection 

The technology plays a role in enhancing fraud detection and its prevention by studying large data sets and automating the processes around risk assessments, real-time alerts, and security measures. 

Marketing and sales automation 

Intelligent automation elevates sales and marketing efforts by automating segmentation, lead generation, and campaign execution, which ultimately results in sales productivity. On the other hand, the technology makes it easy for marketers to send emails, plan data-backed social media campaigns and advertisements, etc. 

With this, you have all the information you would need to get started with the implementation of intelligent automation in your business. The next and last step is to know how. 

Usually, you will have two options when it comes to getting into this set of AI development services – invest in a ready-made intelligent automation system or build one from scratch and then add integrations to make it powerful. 

If going with the first option, you can opt for these software – 

• IBM Cloud Pak for Business Automation
• UiPath Business Automation Platform
• Automation Anywhere Automation 360
• Microsoft Power Automate

If opting to build it yourself, here is what the process would look like. 

Requirements Gathering and Design:

• Define the scope of automation.
• Identify processes to be automated.
• Design user interfaces.
• Define integration points with existing systems.

Cost Range: 10-20% of the total project cost.

Development:

• Implement automation workflows.
• Develop and integrate AI/ML algorithms.
• Implement user interfaces and user experience.
• Integrate with existing systems and databases.

Cost Range: 50-70% of the total project cost.

Testing:

• Perform functional testing.
• Conduct performance testing.
• Validate integrations and data flows.

Cost Range: 15-25% of the total project cost.

Deployment:

• Rollout automation to production environment.
• Train users and support staff.

Cost Range: 5-10% of the total project cost.

Maintenance and Support:

• Provide ongoing support.
• Address issues and bugs.
• Perform updates and enhancements.

Cost Range: 15-20% of the total project cost (per year).

Ready to get started with intelligent automation implementation in your business process? Get in touch with our AI development experts to get started within a week. 

FAQs

Q. How does intelligent automation work?

A. Intelligent automation makes use of machine learning (ML) and other cognitive technologies for continuous collection, processing, and analysis of data. This continued process powers the technology to suggest data-based insights to your business while automating all the manual processes.

Q. What are the components of intelligent automation?

A. The core components of IA include – AI, Machine Learning, Natural language processing, and Computer vision. 

Q. What is the purpose of intelligent automation?

A. The need for intelligent automation can be studied by knowing the benefits of the technology. 

Here are the top ones – 

• Cost and time savings
• Better accuracy
• Improved efficiency
• Increased data integrity
• Compliance-readiness, and 
• Elevated customer experience.

Q. What’s the difference between intelligent automation and robotic process automation?

A. RPA can only automate a task once it’s programmed to do it. Meanwhile, intelligent automation can learn task automation through cognitive decision-making abilities.

The post Does my business need intelligent automation? appeared first on Neojn Blogs.

Posing as a solution to this scarcity issue, MVP or minimum viable product has come into existence. While everybody who is interested in the digital space might already know what MVP in software development stands for, there is a need to dive into the concept for first-time entrepreneurs. 

But before we dive into the technicalities of MVP in development, let us have a brief look at the benefits of MVP in software development.

Role of MVP in software development

In addition to the obvious benefits such as quick go-to-market time and cost efficiency, MVP software development has some less-known advantages and roles to play as well. They include:

1. User Feedback Incorporation: MVP comes with the scope for collecting early user feedback, helping the software developers modify and better the product on the basis of real user experiences.
2. Risk Mitigation: By making a scaled-down version of the product at an initial level, you can find potential risks and issues at an early stage, lowering the chances of major setbacks happening later in the development journey.
3. Resource Optimization: It aids in optimizing resource allocation by concentrating on essential features, thus safeguarding unnecessary investment in non-crucial components.
4. Market Validation: MVP serves as an approach for testing the product viability in the market, ensuring that you invest resources in a product that meets user requirements.
5. Early Revenue Generation: If the minimum viable product design meets the user expectations and requirements, it can help generate early-stage revenue, which can be reinvested in advanced-grade product development.
6. Iterative Development: MVP promotes an iterative development methodology, enabling continuous improvement and adaptation on the basis of user feedback and changing market situations.

While extremely impactful, these pros of MVP in development only help entrepreneurs when they are able to translate them into digital products. Knowing how can make all the difference to the growth trajectory of an application. 

While the MVP development for startups process is fairly straightforward, at this point, we believe you must be struggling with a critical question: How to know which features to add to the MVP? After all, this decision will clearly impact the acceptance and future of your product in the market. 

Here’s what we tell our clients when they come to us with little idea of MVP version planning. 

How to choose MVP features for your application?

As a part of our digital product strategy service offering, we help startups in their MVP development journey from scratch – checking the market viability of their idea, deep-level user, market, and competitors’ research, and even the list of features that would ensure they get maximum feedback and acceptance from Day 0. 

This is what we suggest. 

After a detailed analysis of your target audience, the biggest issue they are facing and how they are solving it right now, and your competitors’ offerings you will end up with a list of features that would make your application the go-to solution for your customers. 

The next part that comes is how to prioritize those features. 

The method we use to help define the features is the MoSCow matrix. 

It is a prioritization method that consists of dividing all the MVP features into four categories: must have, should have, could have, and won’t have.

• The ‘must-have’ group is of all the mandatory features. 
• The ‘should-have’ group comprises functionalities that are not vital but are still significant.
• The ‘could-have’ group includes all the nice-to-have functionalities. 
• The ‘won’t have’ group comprises functions that are not of priority at this point but could be added later. 

Using the same matrix, you will be able to finalize the features which will then go into mvp software development. With this addressed, let us look into the MVP development process section next to give you the information you would need to build the version.

Step-by-step of the MVP development process

Building the MVP version of a software comes laced with multiple decisions and processes. The idea behind creating an MVP is to release a version that would help gauge real users’ acceptance. Now in order to make it happen, it is important to maintain a competitive edge so that your app version reaches your customers before the competitors’ and this is where tight timelines and clear stages’ definitions come into the picture.

Here’s a breakdown of the stages (and their timelines) that would help translate the efforts into the foundations of a profitable, valuable product.

Research and planning

The first stage of developing a minimum viable product starts with one to two weeks of dedicated product discovery. Here the development team works with you to gather a deep-level understanding of your product, the vision you are looking to achieve, competitor and market analysis, and the general user trends of your app category. 

MVP development planning 

Typically lasting for less than a week, the stage deals with setting a concrete plan for minimum viable product design, development, and launch. Here, the technicalities of the development journey are fixed ranging from feature selection and design systems to user flow and next-gen tech integrations. 

MVP development

At this stage, all the discussions and planning are executed. The designers, developers, and QA team work in an agile mode to develop and release product builds. The build release is usually kept in line with the milestone plan fixed in the previous step.
Creating a version that will be accepted by the users, enough to give feedback, will call for a dedicated effort of over two to six weeks. 

MVP version launch

Once the MVP software design and development process is complete and all the critical stakeholders have given their go-ahead on the version, preparation for launch starts. Here, the platforms on which the users are active are considered for priority delivery following which extensive marketing and promotion is done for getting maximum attention to the application. 

As an entrepreneur who has chosen to take the MVP route because of its time and cost-efficiency, we are sure it would be critical for you to know how much would it cost to build an MVP. The details that would give you a complete picture of the time and cost of development, however, are too huge to compress in this discussion. So here is a brief look to give you a good enough idea of what to expect cost-wise. 

How much can MVP development cost?

The cost of building an MVP can be anywhere between $15k to $200k. The reason behind this wide range gap is led by a number of factors – 

• Number and complexity of features 
• Applied design system 
• Platforms choice for MVP launch
• Technology addition, if any
• Location and team size of the MVP development services providers. 

To know which end of the range spectrum your MVP development journey will belong to, get in touch with our business development team today. 

As we conclude the article, let us answer another common confusion that entrepreneurs have when starting their digital journey – choosing between different (same intent) models. Oftentimes, businesses are confused between three options – all working with the same intent of finding viability and acceptance of their product – PoC, Prototype, and MVP. 

Here are the key differences between them.

Purpose and scope:

Prototype: Used for visualizing and testing the design concepts, interactions, and user experience.

MVP: Developed for finding product-market fit and refining the idea in a real-world setup.

PoC: Built to showcase the feasibility and potential of a product.

Level of functionality:

Prototype: Limited functionality which allows testing the product’s usability.

MVP: Full functionality of features that avoids scope creep.

PoC: No functionality is needed as the main goal is to showcase the core concept.

Audience and timing:

Prototype: Targets internal teams, stakeholders, and potential users in the early development stages.

MVP: Aimed at early adopters, potential customers, and investors to validate the product-market fit.

PoC: Intended for stakeholders, investors, and potential partners to showcase the viability and potential of the idea.

Assuming that your requirements align with what MVP stands for, we suggest not spending too much time to get on the design and development stage. Keeping this article as the base, we hope that you have gathered all the information you need to get started with investing in MVP development services in New Delhi or any other location across the globe. 

FAQ

Q. What is MVP development?

A. MVP development for startups is a concept where a critical-level features product is launched in the market to study users’ feedback and acceptance rate. The idea here is to do a quick launch with a minimal set of functionalities that demonstrate the value of your application in the best manner. 

Q. How long should MVP development take?

A. Minimum Viable Product design and development can take anywhere between two to six months, or even longer depending on the complexity of the features, platform choice, tech additions, etc. 

Q. How much does it cost to develop an MVP?

A. Like time, there is no concrete answer to how much MVP development costs. This too can be anywhere between $15k to $200k depending on elements like the complexity of the features, design system, platform choice, tech additions, etc.

Q. What is the main goal of developing the MVP?

A. The main purpose of building an MVP is to measure the market acceptance of a digital product before its full-fledged version is launched with a lot of money and effort at stake. 

Q. What comes after developing an MVP?

A. After the MVP is built, the deployment process starts with priority being given to the platforms and operating systems the majority of users are active on. Post this, dedicated efforts are placed behind promoting the application in order to get maximum user attention. 

Q. What is the difference between MVP and MMP?

A. Minimum Viable Product (MVP) helps businesses validate their ideas so it is a very basic version in terms of UX and functionality. The Minimum Marketable Product (MMP), on the other hand, is a version that is ready to be sold, thus it is developed better and offers a good overall user experience.

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Noting the high monetary ROI at stake, entrepreneurs have started looking for different mobile app monetization models to incorporate in their applications. 

In this article, we are going to explore the multiple types of app revenue models and which strategy would fit which type of business. 

Now before we reach that stage, let’s dive into some present-day app revenue statistics highlighting the state of app owners’ earnings. 

Top Mobile App Monetization Statistics

• 38.5k apps in the Google Play Store are priced at less than a dollar. 26.6 thousand are priced between 1 and 2 dollars, and 2300 are priced in the range of 9 and 10 dollars. [Statista]
• 1.6 million apps in the App Store are priced lower than a dollar. 2800 apps are priced between 9 and 10 dollars. [Statista]
• Paid apps are expected to generate a maximum of $7 billion USD from app purchases by 2026. [Statista]
• 3% of apps globally are monetized by being paid apps. 36% through ads, and 4% through in-app purchases. [Statista]

The way to get your business become a part of these statistics, it is critical to implement the best mobile app revenue models. Let us look into them. 

The Best Mobile App Revenue Models

Depending on your application type, there are a number of mobile app monetization strategies you can choose from. The decision, however, should be taken from the early stages of the digital product development process, since the choice would affect the app’s structure, user experience, and feature sets.  

1. In-App Advertising

One of the most commonly used strategies, the app advertising revenue model, is one that you also must have come across in your app-using journey. This is where ads are displayed to the users after relevant intervals and the moment users click on the ad, the app owners get a revenue hit from the ad company. 

The different ad formats that the app owners can choose from include – 

• Banner ads
• Full-screen or Interstitial ads
• Reward ads
• Affiliate ads
• Video ads
• Playable ads

2. In-App Purchases 

The in-app purchases model is probably one of the most used strategies in the digital sphere. The idea behind it is that the users buy some elements of the app in order to move further or get a better, exclusive experience. 

Although extremely common, it can be difficult to manage in the long run, especially because it is difficult to keep offering new features or experiences to the users. 

Which apps benefit most from the model: Gaming, Fitness, and Relationship-based apps.

3. Subscription 

Subscription or Freemium app revenue model is one where the users get free access to the application with some features or content locked for in-app purchases. Some of the top benefits that come at the back of this model include – 

• A ‘try before you buy’ approach enables users to try the application without any upfront costs. 
• Lowered customer acquisition cost because of easy in-app entry. 
• A predictable revenue stream. 

Which apps does the freemium model work best for: All media-heavy apps like OTT, Music apps, Courses apps, and Dating apps.

4. Paid Apps

This revenue model works in a way that the users pay for the app at the time of download itself. While being one of the top used models since the time of app sector’s inception, paid apps are slowly losing their place to freemium or in-app purchase models because of the inability to constantly give ROI-friendly features and experience. 

It usually works for applications that have established their name in the industry or have come up with a unique solution to a mass-level problem.

5. Hybrid Monetization Model 

This is nothing but a combination of multiple app revenue models. Based on the business model, app companies can choose to maximize their profits by applying more than one model. 

The most common combination when it comes to hybrid monetization is that of in app advertising revenue model, in-app purchase revenue model, and data monetization revenue model. 

Which apps can benefit from this: While any application can benefit from combining two or more models, this is most commonly seen in practice in gaming applications. 

6. Partnership/Sponsorship Model

Brand sponsorships can be an efficient way to get app revenue. Here the app owners partner with complementary brands by offering them exposure within the application. For every booking or purchase the user makes on the sponsored brand, the application gets a commission. 

The key here is to ensure that the sponsorship is relevant to the app’s audience as only then it would be a win-win situation for both. 

Which apps work best for this model: Super apps, Finance, and Travel.

7. Affiliate Marketing 

In this app monetization model, you earn commission by marketing or selling other company’s offerings within your application. When you direct the traffic from your application to the affiliate partners’, you get a percentage of the subscription or purchase that the users make on their platform. 

The way to approach this is by researching programs around your niche and then joining them as a partner. Some of the famous platforms you can do this with are – Rakuten Marketing, CJ Affiliate, and Impact. 

Apps that resonate best with this model: eCommerce, SaaS, Travel and hospitality.

8. Data Monetization 

Under this model, businesses sell users data to third parties in return of revenue. While we know how controversial and unethical data monetization strategy sounds, the model is legal if you have explained the usage, type of data, and frequency of data collection to the users. 

The only catch here is that users have, over time, become very conscious of how their data is used and shared, so if you fail to mention it to them, it may lead to severe financial and legal consequences. 

Amidst so many app revenue models already in the market and a number of others emerging on a frequent basis, it can become difficult for entrepreneurs to choose the best approach. Let us look into the how next. 

How to Choose the Best App Revenue Models?

There are a number of elements that help select the best revenue model for an app business to go with. Here are the top ones.

• Competitor analysis 
• App’s usage patterns
• Availability of natural add-ons
• Target audience 

In addition to this, the app domain is also witnessing a trend where the returns of revenue models are getting affected by the platform your app is active on. Have a look. 

Hopefully, at this point in the article, you would have gotten clarity on what to ask of your mobile app development company in terms of the right monetization technique. As we conclude the piece, let us look into the different strategies that would make the journey of implementing ads profitable. 

Best Strategies for App Revenue Model Integration

App monetization should be directly aligned with the user experience – this is the one golden rule that will greatly impact your app revenue generation efforts. Here are some ways to support the process – ones that we, at Neojn, always suggest to our clients. 

1. Understand what makes your users pay for a product, service, or experience by looking into your competitors or running extensive market research. 
2. Map out your user’s journey to know the areas in which they are most likely to convert into paying customers. 
3. Test pricing carefully by offering tiered options. A price that is too high or too low can backfire. 
4. Offer the option to not see ads or prompts to become a premium member. 
5. Have enough items of value to make users appreciate your application and pay for the service. 
6. Keep an eye out on metrics like LTV, churn rate, conversion rate, etc. to know which model is working for you. 

FAQs

Q. How much do apps generate in revenue?

A. There is no bar around how much an application can generate on the back of a well-planned app monetization strategy built on a solid user experience study. The sign of this is that the revenue count of both the app stores combined is set to reach  $171 billion globally.  

Q. Which is the best monetization model for my app?

A. There is no one answer to this. The ideal model will depend on your user base, app usage patterns, competitors, frequency of new experiences and features you can add, etc. 

Q. How does an app generate revenue?

A. There are a number of ways an application can generate revenue. Here are the top options – In-app advertising, In-app purchases, Subscription, Paid apps, Hybrid monetization model, Partnership/sponsorship model, Affiliate marketing, and Data monetization. 

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