Find → Enrich → Understand → Score
Know your territory better than your competitors.
Ask a plain question. RepVector finds every referral source, facility and prescriber in the market from 70 public datasets, enriches each one with the services, locations and named decision-makers published on its own site, understands where it is weak against competing providers, and scores the whole list 0–100. You get a tiered call list with people and phone numbers on it, a stated reason per name, and a citation behind every number.
RepVector doesn't track calls. It tells you which calls should exist.
Ask it like this
“Every psychiatrist and PCP within 40 miles of our Tampa facility.”
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.
The problem nobody names
Your CRM has a numerator. It has no denominator.
You can see how many calls your reps logged last quarter. You cannot see what percentage of the addressable market that represents — because no one has ever assembled the full list. So “good quarter” and “bad quarter” are judgments, not measurements. RepVector builds the denominator: every target that exists in a market, ranked, with the reason it's on the list.
What the CRM knows
312 calls logged in Q2
What nobody knew
1,847 targets exist in the county
What that makes Q2
17% of the market, measured
Illustrative figures. In a live run every number carries its own source chip.
The new-market hire
You hired someone in a market you can't see. Now what?
When you put a BD hire into a new territory, you're betting their rolodex is real. You'll find out in nine months. RepVector shortens that to week one: hand them the complete target universe for their county — ranked by volume, with a stated reason for each name — and the conversation shifts from “how's it going?” to “walk me through this list.”
- Every psychiatrist, PCP, IOP/PHP, hospital, and facility in the geography
- Ranked by the published volume signals that predict referral potential
- A one-line reason per target, traceable to the source row
- Exports to CSV or JSON — drops into whatever CRM you already run
Where the names come from
76 data sources. Every name has a receipt.
76 registered sources, 62 of them live with no key required. Four questions decide which ones a query actually pulls.
Who's there?
NPPES provider registry, CMS enrollment, Provider of Services, SAMHSA facility locator
Who does the volume?
Physician utilization by provider and service, Part B, Part D prescribers, inpatient DRG discharges
Who pays in this market?
Medicare Advantage penetration by county, Medicaid enrollment, Medicaid state drug utilization
How healthy is the market?
HCRIS hospital cost reports, CDC PLACES, HRSA shortage areas, CDC mortality
Show your work
Confidence isn't a badge. It's a reason.
Every source segment scores 0–100, and every score opens an Explain panel — what was used (rows, geography, vintage, live versus retained, the exact query) and exactly why points were deducted. If a source only publishes statewide, RepVector widens the query and labels it. It never fails silently and never fills a gap with a guess.
Numbers that add up — literally
HCRIS, MA penetration, physician utilization, and Medicaid SDUD are restated into the same canonical KPIs, with ratios recomputed from summed numerators and denominators — never an average of averages. Units switch between dollars, thousands and millions, or percent, per 1,000 and per 100,000, and each metric is badged for whether the switch recomputes or merely rescales. Where two sources can be compared but not added, the dashboard says so.
Take it with you
Every widget exports to CSV or JSON with a header carrying the query, exact parameters, geography, vintage, capture time, selection reason, publisher notes, and citation URL. Plus an on-demand PDF build report. Built to hand off — to a rep, a CRM, or an IC memo.
What RepVector is not
Not claims data.
No commercial rate intelligence, no patient referral graph. That's the $50K vendors.
Honest comparisonNot a guess.
Where the data is silent, RepVector says so — by source name and geography.
The guardrailQuestions
Referral generation and business development, answered
- What is healthcare referral generation?
- Healthcare referral generation is the work of finding, ranking and calling the clinicians, facilities and payers who can send you patients or buy your product. RepVector builds that target universe from public federal datasets, then enriches each name with the organisation's own published services, locations and contacts, and scores the list so the call order is decided before anyone picks up a phone.
- How does RepVector help physician referral marketing and business development teams?
- You describe the product, buyer and market in plain English. RepVector selects the relevant public datasets, resolves the geography down to county and FIPS, pulls live rows, reads each target's website for services and named decision-makers, compares capabilities against competing providers, and returns a tiered call list with a 0–100 fit score and a citation behind every number. Export it to the CRM you already run.
- Which data sources are used?
- NPPES provider registry, CMS enrollment and Provider of Services, physician utilization by provider and service, Part B and Part D prescribers, Medicare Advantage penetration, Medicaid state drug utilization, HCRIS hospital cost reports, SAMHSA facility locator, HRSA shortage areas, and CDC PLACES — among 70 public datasets in the catalog — plus each target's own public website during enrichment.
- What does RepVector add beyond a list of names?
- Four stages. Find builds the complete target universe for the market. Enrich reads each target's own site for services, locations, phones and named decision-makers, with the source page attached. Understand compares those capabilities against competing providers to expose gaps, and watches for changes. Score folds volume, payer mix, opportunity gap, web completeness, contact quality and change recency into one 0–100 fit score with a visible breakdown.
- What does a client actually receive from a run?
- A tiered call list with addresses, phone numbers and named contacts where they are published; a 0–100 fit score you can open to see its parts; drill-down charts on volume, payer mix and capacity; a competitive capability matrix with whitespace marked; a cited one-page positioning summary; a week-by-week market plan; and CSV, Excel and PDF exports that carry the provenance with them.
- Is RepVector a CRM?
- No. There is no call logging, no pipeline stage, no sequencing and no activity tracking. RepVector builds the ranked, enriched target list and hands it off clean to your CRM.
- How is this different from a claims data vendor?
- Claims vendors win on referral graphs and commercial rates and cost tens of thousands a year. RepVector is self-serve, priced per seat with pooled runs, sources only public data and each target's own published site with a citation per figure, and requires no annual contract.
Three runs free. No card, no call, no annual contract.
Pick one county you already know well and check the list against what's in your head. That's the only evaluation that matters.