One rate-intelligence estate.Every payer, every code,every market.
~55 federal, commercial, and geographic datasets, fused into one normalized graph — joined on NPI, CPT, and geography — that scores any provider's rate against its true local peers and turns every rate into one comparable number: % of Medicare.
Scope of the public estate we continuously index toward — not a live row count. Coverage by code, payer, and market is disclosed on every result. See our honesty standard →
Every buyer and every competitor speaks in "% of Medicare" — but nobody had a clean Medicare denominator for the down-market layer.
A locality-correct Medicare engine.
Work / PE / MP RVUs × locality GPCIs × the conversion factor → a locality-correct Medicare allowed amount for every code, served by the medicare_allowed() engine.
The moat is integration,
not raw size.
Nine data domains in one normalized graph — commercial TiC, out-of-network, Medicare benchmarks, drug pricing, hospital charges, the provider/group graph, geography, quality, and discovery — joined on NPI, CPT, and market. Every other vendor brings two to four, siloed by delivery format. We normalize ~55 datasets to the same 110 GPCI localities on a $0 public-data cost base, so a single CPT speaks as a % of Medicare, a commercial rate, a hospital charge, and a Medicaid fee at once.
Local-peer scoring is the payoff of that fusion — and it is being hardened from national to true metro-level accuracy. We lead with nine domains in one graph (true today) and frame local-peer scoring as the engine that breadth makes possible, not a production-proven metro number.
Browse the data backbone.
One normalized graph. Filter by what you care about — domain, what's live, the tool it powers, the audience it serves.
What the estate powers.
Every dataset above feeds a live surface. The data doesn't sit in a warehouse — it scores rates, builds memos, and maps markets.
Built for the people who use the data.
You already suspect you're underpaid on some codes and fine on others — you just can't prove which, or by how much. We turn the public rate estate into your answer: for each of your codes, your rate next to your local same-specialty peer set, expressed as a percent of Medicare, with the gap quantified and the target documented.
The moat is a compounding public-data estate: ~55 federal, commercial, and geographic datasets fused into one normalized graph, refreshed on a cadence, with a per-NPI local-peer engine no incumbent offers down-market. The architecture keeps it cheap — a built data-lake offloads the heavy raw; compact rollups serve the live site.
Bring your providers; we bring the backbone. The group graph — 82,817 groups and 1.76M clinician affiliations — rolls a whole MSO or billing book into one view, every NPI scored against its real local peers, every memo white-labelable under your brand.
Honest by construction.
One backbone. Score your rates against the real market.
See your rate against your true local peers — built on the public estate above, in about a minute.
Generated in about 15 seconds. No email. No credit card.
Every number here is the scope of the public estate we index toward — not a live row count. Coverage is disclosed on every result. Methodology & sourcing → · Our honesty standard →