AI Advisory · Architecture · Implementation
We help healthcare, legal, insurance, and consumer companies build AI that is not just intelligent — but verifiably accurate and trusted by the people who depend on it.
Start a conversation →We meet you where you are. Whether your AI is already broken, about to be built, or needs end-to-end delivery — there is an engagement designed for your stage.
Your AI is live but something is wrong. We diagnose exactly where it breaks, why, and give you a clear remediation path.
You're about to build. We architect the trust layer before a line of code is written — saving months of painful iteration.
End-to-end implementation. We lead the architecture, manage delivery, and hand you a system your users and regulators trust.
Client identities kept confidential by default. What we share are the problems, the approaches, and the outcomes — which is what actually matters.
A UAE-based digital health platform had deployed an AI triage assistant for chronic condition patients. Internal testing revealed 31% of responses were clinically inaccurate — presented with full confidence. Six weeks from a major hospital partnership launch.
A UK legal tech startup needed an AI contract reviewer for SME clients. Their prototype produced confident legal interpretations with no citation trail — creating significant professional liability exposure before a single paying client had signed.
A consumer platform serving new mothers needed AI to answer sensitive health questions reliably, at scale, without a doctor on every call. Standard LLM responses were inconsistent and occasionally dangerous in the hands of anxious parents.
A Singapore insurtech had built an AI system to guide policyholders through claims. It worked — but produced no audit trail, varied interpretations of identical policy clauses, and was facing a regulatory review that would have shut it down entirely.
Every company we work with arrives with one of six problems. They share the same root cause — an AI system built for capability, without being built for trust.
Your AI is live and generating confident, plausible, wrong answers. Users are catching it. Trust is eroding fast.
Your system cannot distinguish confident knowledge from educated guessing. Everything sounds equally authoritative.
You have domain experts — doctors, lawyers, advisors — but their knowledge isn't embedded in your AI's outputs.
You're in a regulated domain and your AI can't produce an audit trail. A single wrong output creates legal risk.
Users stopped trusting your product after catching errors. The system is technically functional but commercially broken.
Your team is about to build AI in a high-stakes domain with no experience of the failure modes that emerge at scale.
Trust is not a feature you add at the end. It is an architectural decision made at the beginning. We have built it from scratch, under pressure, in production.
Every system anchors outputs in verified source material. We design retrieval before we design generation.
We build confidence scoring into every system. When the AI doesn't know, it says so — and routes to a human who does.
Every output can be traced to its source. Regulators and legal teams can inspect any answer ever generated.
Medhary was founded by a technologist who built a consumer AI platform for new mothers — a domain where every answer carries real consequences. Wrong information meant real harm to real people.
That constraint forced us to solve what most AI teams haven't faced yet: how do you build a system that is genuinely helpful AND verifiably accurate when the stakes are too high for trial and error?
We built the grounding layer. The expert verification system. The community-knowledge synthesis. We made every expensive mistake so your team doesn't have to.
"We help companies build AI that is not just intelligent — but one that your users, regulators, and legal team can actually stand behind."