Why PolarisRay
Built for assistants, not humans
Public-data APIs were built for developers writing code, not for AI assistants spending tokens. PolarisRay reshapes the same public data so your assistant answers in a fraction of the context — and can ask questions the raw APIs simply can't.
Answer-ready, not raw JSON
Official APIs hand back verbose records your assistant has to download and parse, burning context on data it throws away. PolarisRay returns just the answer, already structured — with exact counts, not capped estimates.
Payload size for one openFDA device-event record versus a PolarisRay structured count, on “How many adverse events for insulin pumps in 2023?” About 26× less context to read a total — not a typical search-result ratio, and not the same count as FDA coded fields.
Search by meaning, not just keywords
Recent reports are enriched with normalized concepts mined from the free-text narratives — disease, anatomy, medical specialty — so you can facet and filter on them directly, then drill into the source reports.
“What conditions do insulin-pump failures involve?”
- Hyperglycemia
- Diabetic Ketoacidosis
- Hypoglycemia
The raw FDA API can't answer this — the signal lives in the narrative text, not the coded fields. Labels are illustrative; live totals change. Concepts are mined from unverified reports — treat them as signal, not diagnosis.