New market entry
Week one, not month nine.
When you open a market or hire a rep into one, you are betting their rolodex is real — and you find out three quarters later. RepVector produces the full target universe for that geography before the first call, so you review the plan instead of waiting on results.
Questions you can answer
We're opening in Tampa. Who exists in Hillsborough County, and who matters most?
Give my new hire a ranked call list with a one-line reason per name.
How saturated is this market — how many comparable providers already sit between us and the referral base?
What's the payer mix here, and does it support our program?
The datasets behind it
Selected at runtime against your question, each with a stated reason. Anything a source cannot cover at your geography is reported as a gap, not filled in.
Geography resolution
Name a city, town, ZIP, metro, or state and it resolves to state, every county FIPS it touches, place, ZCTA, tract, and CBSA — so a county-keyed dataset never comes back empty because you said Tampa instead of Hillsborough.
NPPES, CMS enrollment, Provider of Services
The who's-there layer: every enrolled clinician, group, and facility in the resolved geography, by taxonomy and address.
Physician utilization and Part B services
The who-does-the-volume layer, so the list arrives ranked rather than alphabetical.
MA penetration and Medicaid enrollment
County-level payer composition — whether the market's coverage mix actually supports the program you're standing up.
HCRIS cost reports and CDC PLACES
Facility financial capacity and population health burden, for the saturation and viability read behind the site decision.
Google Places and OpenStreetMap POIs
Physical presence and drive-distance context around a proposed location, with the ranked shortlist plotted on an interactive map.
Find → Enrich → Understand → Score
Four stages between your question and a call list you can defend.
01
Find
Ask in plain language. The AI reads the intent, resolves the place you named down to county and FIPS, and pulls the datasets that actually apply from the public catalog — no query syntax, no source picking. What comes back is the whole target universe for that market, not a sample.
02
Enrich
Each target is taken past the registry row. RepVector reads the organisation's own website for services, locations, phone numbers, and named decision-makers, and attaches the page every fact came from. If a fact isn't published, it says "not published" — it is never inferred.
03
Understand
Capabilities are lined up against competing providers to expose gaps and whitespace, and each target is re-read over time so you can see what changed — a new location, a new service line, a decision-maker who left — with the evidence URL beside it.
04
Score
Everything folds into one 0–100 fit score per name: published volume, payer mix, opportunity gap, web completeness, whether a named contact was found, and how recently the target changed. The breakdown is shown on the row, not hidden behind a black box.
What you get
What lands in the dashboard
One run produces the whole picture for a market — the names, the people at them, the gaps worth calling about, and the proof behind every figure.
A ranked call list with people on it
Organisation, address, phone, and — where the site publishes one — a named decision-maker with their title and the page it was read from.
Tiers with a stated reason per name
Tier 1, 2 and 3 targets, each carrying one line explaining why it ranked there and the source row that line came from.
A 0–100 fit score you can open
Volume, payer mix, opportunity gap, web completeness, named contact and change recency, each shown as its own contribution.
Charts that drill down
Volume, payer mix, capacity and population widgets — click any figure to see the rows, geography, vintage and query behind it.
A competitive matrix and a one-pager
Your targets against competing providers by capability, with whitespace highlighted, plus a cited one-page positioning summary as PDF.
A market plan, sequenced
A week-by-week territory plan built from the ranked list — who to see first, in what order, and the evidence behind the sequence.
Change alerts on the market
Saved markets are re-checked, so you get a digest of the providers, capabilities and locations that moved since your last run.
Exports that keep the receipts
CSV, Excel and a full PDF build report, each carrying query, parameters, geography, vintage, capture time and citation URL into whatever CRM you run.
Then hand it off
Hand the new rep a CSV on day one and run the first territory review off the same file. The conversation shifts from 'how's it going?' to 'walk me through this list' — which is a question you can actually evaluate.
RepVector doesn't log calls or manage pipeline. Here's where we stop.
The point isn't a bigger list. It's that a manager who has never set foot in Hillsborough County can read the ranking, see the reason attached to each name, and judge the plan before nine months of salary has been spent testing it.
Build the list for one county and see whether the names are right.
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