One provider, end to end.
This is the artifact a rep gets before picking up the phone — from a plain-English request to a scored, cited brief. Everything below is an illustration with sample data: the provider is fictional and the NPI is masked. The mechanics are exactly what the product does.
A sentence, not a query builder.
The rep describes the ideal provider the way they would describe them to a colleague. The platform parses it into structured targeting — each extracted field shown with its confidence, so nothing happens invisibly.
- Every parsed field is visible and editable before anything runs
- Confidence is shown per field, not asserted in aggregate
Ranked, with the why attached.
Every matched provider is scored 0–100 across Filter Match, Clinical Fit, and Accessibility, then tiered Hot / Warm / Cool / Cold. The explanation is not a tooltip afterthought — it is the product.
- Three dimension scores, each inspectable — no single opaque number
- The "why this score" line is what reps actually read before calling
Research your rep can defend.
For any provider worth a call, agents assemble the pre-call brief: findings with citations, practice context, and suggested openers. A finding that cannot cite a source is dropped, not asserted.
Dr. Maya Shreve, MD
Hosted a robotics adoption panel last month.
Chaired the AAOS regional session “Scaling Robotic-Assisted TKA in Mid-Size Practices” — a strong technology-adoption indicator for your category.
Owner, not employed. Partnered-MSO model.
Practice growth from 4 → 9 surgeons in 24 months. Two new locations in high-cost ZIPs. Owner-operated — decision authority is hers.
- Reference her AAOS robotics panel — congratulate, ask what surprised her.
- Pattern-match: two other mid-size owner-operated practices who adopted in month nine.
- ROI angle for her second location, not her flagship.
- Every finding carries its citations — in the product they resolve to the source
- Openers are grounded in the cited findings, not generic scripts
Let the agents
do the research.
Your team does
what it does best.
Want proof first? Read an example dossier — sample data, real mechanics.