Gia.

The problem

Clinical decision-making relies heavily on peer consultation — the senior colleague down the hall, the specialist on the call, the informal "what would you do?" conversation that happens dozens of times per day in every hospital. But that informal knowledge is never systematically captured: it walks out the door when the consulting physician retires, and the next colleague with the same question has to re-ask the same expert.

The structural drivers are worsening: physician retirement is accelerating (institutional knowledge loss compounds), AI adoption in clinical settings is now mainstream (the cultural barrier dropped post-2023), and published literature doesn't answer clinical questions the way real peers do — the nuance, the "in our institution we do X because Y," the practical workarounds, all live in the informal consultation layer that's invisible to literature search.

The solution

Gia captures, indexes, and retrieves real doctor-to-doctor clinical consultations — making peer knowledge searchable, attributable, and reusable. A physician asks a question; the AI retrieves the most relevant prior peer consultations (with role, specialty, credentials, institution metadata visible); the answer is synthesized strictly from retrieved context.

The product's distinctive bet: strict RAG with refusal-when-unknown is the product. Hallucinated medical guidance is the failure mode that disqualifies almost every AI-for-healthcare tool from clinical adoption — Gia's architecture eliminates it by design, not by aspiration.

How it works

Two-model RAG pipeline: retriever surfaces relevant peer consultations from the indexed knowledge base; the answer generator synthesizes the response strictly from retrieved context. The architecture explicitly does not require a model trained or fine-tuned on medical literature — the generator's only job is structured synthesis from provided context, not domain recall.

System prompt enforces strict rules: Rule 1 (context available) — answer ONLY using the information within context tags, do not supplement, infer beyond, or mix with general medical knowledge. Rule 2 (no context) — say so honestly, do not invent. Required inputs: USER_QUESTION (physician's question, plain text) and RAG_CONTEXT (retrieved chunks with metadata: role, specialty, credentials, institution). Optional: question_tags for filtering (not always used to avoid filtering out relevant untagged chunks).

Automated evaluation is set up; human-graded ground truth is WIP.

Who it's for

Target users: licensed health professionals — specifically physicians seeking peer-validated answers to clinical questions (diagnosis support, treatment recommendations) without having to re-consult the same experts repeatedly. Use cases: a primary-care physician encountering a case they'd typically refer to a specialist friend; a resident with a question their senior would normally answer in passing; a rural physician with no on-site specialty colleague.

B2B (institutional): health systems, physician groups, hospital networks — buying for their physician workforce as an internal knowledge layer. B2C (individual): licensed physicians paying directly for access. The institutional layer is the larger revenue play; the consumer layer is the trust-building and adoption channel.

Why it matters

Institutional medical knowledge is leaving with retiring physicians faster than it's being captured. Peer consultation is the most-used and least-instrumented form of clinical knowledge transfer in medicine — and the post-2023 cultural shift in clinical AI adoption finally creates space for a product that addresses it.

Gia's strategic bet is on architecture as positioning: strict RAG with refusal-when-unknown isn't a feature, it's the product. In a category where every other AI tool's reputation is one hallucinated drug interaction away from disqualification, trust mechanics built into the architecture rather than disclaimed in the UI is what makes physician adoption possible at scale.

At a glance

Project

Gia

Built by

Sindhu Bobba

One-liner

AI peer-knowledge platform for physicians — strict RAG over real doctor-to-doctor consultations with full source attribution. No hallucinated medical guidance.