Hi HN,

I’m a solo dev building a clinical search engine to help my wife (a resident physician) and her colleagues.

The Problem: Current tools (UpToDate/OpenEvidence) are expensive, slow, or increasingly heavy with pharma ads.

The Solution: I built Evidex to be a clean, privacy-first alternative. Search Demo (GIF): https://imgur.com/a/zoUvINt

Technical Architecture (Search-Based RAG): Instead of using a traditional pre-indexed vector database (like Pinecone) which can serve stale data, I implemented a Real-time RAG pattern:

Orchestrator: A Node.js backend performs "Smart Routing" (regex/keyword analysis) on the query to decide which external APIs to hit (PubMed, Europe PMC, OpenAlex, or ClinicalTrials.gov).

Retrieval: It executes parallel fetches to these APIs at runtime to grab the top ~15 abstracts.

Local Data: Clinical guidelines are stored locally in SQLite and retrieved via full-text search (FTS) ensuring exact matches on medical terminology.

Inference: I’m using Gemini 2.5 Flash to process the concatenated abstracts. The massive context window allows me to feed it distinct search results and force strict citation mapping without latency bottlenecks.

Workflow Tools (The "Integration"): I also built a "reasoning layer" to handle complex patient histories (Case Mode) and draft documentation (SOAP Notes). Case Mode Demo (GIF): https://imgur.com/a/h01Zgkx Note Gen Demo (GIF): https://imgur.com/a/DI1S2Y0

Why no Vector DB? In medicine, "freshness" is critical. If a new trial drops today, a pre-indexed vector store might miss it. My real-time approach ensures the answer includes papers published today.

Business Model: The clinical search is free. I plan to monetize by selling billing automation tools to hospital admins later.

Feedback Request: I’d love feedback on the retrieval latency (fetching live APIs is slower than vector lookups) and the accuracy of the synthesized answers.