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NIX has developed an AI-driven chatbot that provides users with 24/7 mental health support.
Healthcare
Data Science, Data Engineering, DevOps Services, Generative AI, AWS
AWS, AWS Lambda & CLI, CloudWatch, OpenAI
Our client is a US-based startup that aims to automate wellness services and provide psychological support to clients through innovative AI and LLM technologies. Their core objective is to develop an AI-powered chatbot designed to gather user information regarding personal conflicts and incidents, subsequently offering personalized psychological guidance and mental support. Utilizing their limited in-house capabilities, they had developed a prototype of the product, which had garnered significant industry recognition, securing financial backing from AWS. Recognizing the need for additional technical expertise to enhance the prototype and turn it into a market-ready product, the client partnered with NIX, leveraging our profound knowledge in data science and AI solutions.
Due to constraints caused by a limited budget and an increasing need to showcase a functional product, our team had a limited window for prompt engineering and delivering a comprehensive solution. Despite these restrictions, we succeeded in delivering a full-fledged, market-ready solution on time and within budget.
During the development of the robust and functional AI chatbot, we leveraged a comprehensive tech stack suite, including AWS Lambda, CloudWatch, and OpenAI models. The core functionality of the app revolves around user-initiated “check-ins,” which are sessions about incidents, conflicts, or stressful situations. Users log in and provide context about an incident, and the application then identifies the core problem and provides tailored advice and mental health support. For serious incidents, the check-in is seamlessly escalated to human specialists in the wellness sector.
A key challenge we had to solve was the prototype’s inability to access a user’s previous sessions and general profile information, which was caused by the lack of in-house technical expertise and capacity during its initial creation. This limitation severely hindered its performance, allowing it to only answer direct questions without understanding the broader context. To address this, we developed a prompt generation service with a REST API interface, utilizing user demographic data, conversation history, and past check-ins, along with an ETL pipeline.
Extract: We extract the user’s old summary, new messages, and checkins from RDS
Transform: The LLM generates a new summary, using previously extracted data
Load: We update the user’s summary in RDS with the transformed result
Other key improvements we made to the prototype as it developed into a market-ready product include:
Our collaboration with the client resulted in the successful launch of a market-ready solution that immediately generated significant traction and visibility. The client’s unique idea, enhanced with our technical excellence and seamless execution, drove substantial new investments, securing the project’s future and setting the stage for exponential growth.
1000+
users in the first few days after launch
208+
successfully resolved check-ins
30%
increase in investor inquiries within the first month
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