Gia.

Gia is a HealthTech AI platform that captures and indexes real doctor-to-doctor clinical consultations, making peer knowledge searchable and retrievable through AI — with strict RAG and full source attribution (role, specialty, credentials, institution) on every answer. Unlike medical AI tools trained on published literature, Gia's answers come from real clinical conversations: the informal expertise senior physicians share with colleagues but that has never been systematically captured.

The structural opportunity is real and worsening: institutional knowledge loss as the physician retirement wave accelerates, AI adoption in clinical settings is finally crossing the cultural threshold post-2023, and peer consultation is structurally underserved — no existing tool captures and reuses the knowledge senior physicians offer their colleagues every day.

Strict RAG architecture: the system prompt explicitly enforces "Answer ONLY using the information within the context tags. Do not supplement, infer beyond, or mix with general medical knowledge." Every answer is grounded in retrieved physician-sourced context, with source metadata (role, specialty, credentials, institution) cited in-line. If the knowledge base doesn't have an answer, the system says so honestly — refusing to hallucinate is the product, not a side effect.

Two-model RAG architecture: the answer generator does not require a model trained or fine-tuned on medical literature — this is a deliberate architectural decision. The generator's only job is to synthesize a coherent, well-structured response from retrieved context, not to recall medical knowledge from training data. B2B (health systems, physician groups, hospital networks) for institutional sales; B2C (individual licensed physicians) for direct subscription. Target users: licensed physicians seeking peer-validated answers to clinical questions (diagnosis support, treatment recommendations) without having to re-consult the same experts repeatedly.