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Elephant Hawk — Opportunity Seeker

A funnel, not an oracle. SEEKER generates opportunity candidates from real public signals (openFDA, ClinicalTrials.gov, PubMed, WHO burden), triages most away cheaply, runs the full SCORER engine on survivors, then ranks by composite and the independent need-supply gap so genuine white space isn't lost to rubric-gaming. Output = 3–4 theses to test with named experts — humans still make the call.
Target profile & search domains
Seed with Elephant Hawk strengths (preferred) or open-ended clinical areas. Each domain is searched against real databases.
Search domains / substrates (comma-separated)
Quick add: wearable biosensor AI imaging motion / gait point-of-care dx remote monitoring urology
Geographies of interest
Min white-space ratio (triage)
Keep top N survivors
Candidates per domain (AI)
Hard gates a candidate must pass (informational; the AI estimates these per candidate):
— the Deal & Fit layers and AI guidance adapt to whose seat you're scoring from.
Layer weight overrides (re-weight the rubric for this hunt)
Defaults come from the shared engine. Override to crank what this profile cares about (e.g. White-space, India-fit, low Regulatory difficulty, strong Payer). Blank = default.
AI engine settings (needed to name candidates & score)
Key/URL is remembered in this browser only (localStorage) and used solely for a direct browser call. Open the file in a real browser — external APIs are blocked in the in-app preview.
Stage 1 fetches real evidence per domain; the AI only clusters & names candidates from those signals.
Stage 1 — Evidence-first candidates (decoupled from scoring)
Real signals per domain → AI clusters & names candidates. Every candidate carries provenance; none is allowed without a signal it ties to.
Stage 2 — Coarse triage (kill most, cheaply)
Four decisive screens: capability/focus, regulatory quick-read, white-space ratio, payer plausibility. We do not run all 14 layers on everything. Dropped candidates are shown — nothing is silently truncated.
Stage 3 — Full SCORER on survivors
The complete 14-layer engine (same gates → floor → bands as SCORER) runs on each survivor.
Stage 4 — Rank + heatmap (anti-Goodhart)
Ranked by composite (TOPSIS) and the independent need-supply gap. Favour candidates where the two agree; genuine high-need / low-supply white space is surfaced even when the rubric isn't maxed; rubric-gaming is flagged.
Stage 5 — Theses to test (the actual output)
For the top hotspots: a thesis to test, the key uncertainty, and which experts to talk to. Feeds the existing experiment-and-test loop. Export each as a standalone SCORER report.