Healthcare · Mental health
We engineered a voice-first AI companion with a therapeutic framework to validate a new model for mental health support.
Outcome
150ms from a user’s last word to the companion’s reply, inside CBT logic and encrypted end to end; the working demo that carried the seed raise.
Demo build used in the client's seed-fundraising process.
Digital mental wellness tools often lack the nuance of human interaction. Most platforms offer static content or simple chatbots that fail to understand emotional context. A mental wellness startup identified an opportunity to create a more responsive tool-an AI companion that listens to a user's tone of voice and adapts its conversation accordingly.
The concept required a complex synthesis of bleeding-edge technologies. They needed to prove that a voice-first AI could not only understand words but also interpret emotion, follow a therapeutic model, and do so within a secure, private environment. Without a functional product to demonstrate this, securing the necessary seed funding to grow the business would be impossible.
The client engaged Robust Devs to build the minimum viable product. Our objective was to architect and deliver a proof-of-concept that integrated a custom AI conversation engine, a low-latency voice interface, and a secure backend. The final product would serve as the tangible evidence needed to earn investor confidence.
The challenge
How we solved it
Technologies
We developed a custom AI engine built upon a fine-tuned language model. Unlike a standard chatbot, this system is embedded with a Cognitive Behavioral Therapy framework, enabling it to guide conversations constructively. It maintains a memory of past interactions, allowing for context-aware support that becomes more personalized over time.
The platform includes a private dashboard where users can view trends in their emotional state. By translating qualitative conversation data into simple data visualizations, the system provides users with a clear, objective view of their wellness journey. This feedback loop helps users track progress and understand their patterns.
How we built it
Wrapped a fine-tuned model in a Cognitive Behavioural Therapy logic layer, so the conversation follows a framework instead of a prompt. A chatbot that answers questions is not a companion. The engine was built as a fine-tuned large language model with a CBT-based logic layer above it, which gives every conversation structure and contextual memory while still adapting to what the user says and how they say it. Because this is health data, the backend was designed around encryption end to end and a relational store that meets privacy regulation from the first line of code rather than as a later fix. Speech-to-text and text-to-speech were integrated behind one clean interface, and the round trip between a user finishing a sentence and the companion answering came down to 150ms, which is what makes it feel like listening rather than waiting.
In the client's words
“Investors could talk to it. That did more than the deck.”
Tell us what you're building. We'll show you how we'd approach it and what it would take to ship.