In tech right now, the spotlight is on “agentic AI,” the autonomous systems that can reserve flights, write software, and even execute trades. While Silicon Valley races to build the next massive productivity platform, a far more urgent and less visible problem continues to unfold in clinics, hospitals, and households: the widening health literacy gap.
The Centers for Disease Control and Prevention (CDC) reports that nearly nine in ten adults have difficulty understanding and using health information for themselves or their communities. When someone leaves an appointment with instructions they cannot interpret or a diagnosis that feels like a foreign language, the consequences go well beyond frustration. Confusion can lead to skipped medications, conditions that deteriorate, and avoidable trips back to the hospital.
I came to understand this problem personally. Professionally, I was an engineer building sophisticated AI systems for Fortune 100 companies, but in my own circles I watched relatives and friends struggle with basic medical guidance because it was packed with jargon or simply not available in their native language, Telugu. That contrast made something clear to me: AI’s most meaningful role was not only generating code, but translating complexity into something people can actually use, in the language and framing that meets them where they are.
Building HealthNeem as a real translator, not a thin chatbot
That insight became HealthNeem, an AI-driven platform built to make health information more accessible. It now supports hundreds of thousands of users and has received multiple MarCom Gold Awards, but reaching that point meant resisting the typical “AI startup” formula.
After ChatGPT arrived, countless products launched that functioned mainly as wrappers, simple interfaces that relay a prompt to a large language model and return the response. In healthcare, that approach can be risky. A question like “Is neem oil safe?” cannot be treated as a generic prompt, because the right guidance depends on what the person means, such as skin use, dental use, or ingestion, which can be toxic.
So the goal was never to create a basic chatbot. HealthNeem was engineered as a context-aware bridge that pulls from trusted sources such as the NHS and the FDA, then reduces complexity without sacrificing correctness. It converts clinical language into everyday wording, turning “hypertension” into “high blood pressure,” and explains why it matters in terms that fit a person’s situation. The real value is not summarizing the internet, but carefully curating, validating, and translating information for a specific user need.
Making trust and usability part of the product
My background in fintech taught me to treat accuracy as non-negotiable, and health technology demands the same zero-tolerance discipline. We could not adopt a mindset of moving fast and breaking things, because the stakes are too high when people act on what they read.
Even though HealthNeem is free to use, we applied enterprise-level standards to how content is generated. We put strict data lineage practices in place so that every simplified piece of information can be traced back to a verified medical source. We also built in guardrails so the system refuses requests for diagnosis, because its role is to educate rather than practice medicine, and we designed transparency into the experience so the AI provides sources instead of unsupported claims. This approach is part of why HealthNeem received a Davey Silver Award, where responsibility mattered as much as functionality, and why I documented these governance-first design standards in the MLOps Manual.
Just as important, we focused on the “last mile” of usability. The people who most need health literacy are often the least comfortable with complex tools, and they are unlikely to craft detailed prompts to get the help they need. Instead of making the AI the front-end experience, we used it behind the scenes to turn dense medical material into clear, vernacular explanations, including translation into regional languages like Telugu where strong medical content is limited. The core point is simple: even the best model is ineffective if the user cannot realistically interact with it, so we aimed to deliver clarity without requiring the perfect question.
