Bulk NPI finder
Recover missing NPIs from messy provider lists.
Match provider and practice names to NPI records, even when your roster has old names or incomplete addresses. Review confidence, candidate records, and sources before exporting the results. No NPI column required.
20 signup credits + 20 monthly credits. No card required. Then 1 credit per row; $1 buys 4.
NPPES-first
Discovery starts with the federal NPPES registry.
Name-gated
Location signals support a name match but can't replace it.
Auditable
Confidence, candidates, and sources travel with every row.
Resolution ledger
providers_q2.csv
5
rows
8
NPPES verifies
12
source URLs
Sarah K. Lindqvist, MD
Family Medicine · Portland, OR
Sarah K. Lindqvist, MD
HighLINDQVIST, SARAH K · street match
1487362091
Sarah K. Lindqvist, MD
LINDQVIST, SARAH K · street match
1487362091HighCascade Heart & Vascular
Cardiology group · Bend, OR
Cascade Heart & Vascular
HighCASCADE HEART AND VASCULAR LLC · street match
1663984528
Cascade Heart & Vascular
CASCADE HEART AND VASCULAR LLC · street match
1663984528HighDr. Mike Okafor
Internal Medicine · Spokane, WA
Dr. Mike Okafor
HighOKAFOR, MICHAEL · nickname expanded
1248573903
Dr. Mike Okafor
OKAFOR, MICHAEL · nickname expanded
1248573903HighRiverstone Pediatrics
Pediatrics · Boise, ID
Riverstone Pediatrics
MediumFormer name: LAKEVIEW CHILDREN'S CLINIC · ZIP match
1582603472
Riverstone Pediatrics
Former name: LAKEVIEW CHILDREN'S CLINIC · ZIP match
1582603472MediumSummit Care Clinic
Urgent care · Reno, NV
Summit Care Clinic
No matchname gate failed · 3 candidates rejected
No NPI
Summit Care Clinic
name gate failed · 3 candidates rejected
No NPINo match
Your first roster
See whether it works on the rows you actually need.
Start with 10 representative rows from your provider roster, credentialing queue, or CRM. Include a few difficult cases and check the evidence before running the rest.
Test my provider listDownload a blank CSV template ↓- 01
Bring the details you have
A provider or organization name, plus practice address, city, state, and ZIP when available. You do not need an existing NPI.
- 02
Review the result, including uncertainty
Check the matched registry record and cited sources. Review low-confidence results and no-matches instead of treating every answer as ready to use.
- 03
Export, then decide on the rest
Keep your original columns alongside the results. A further 100 row runs use 100 credits, available for $25. No subscription required.
Use provider business information only. Remove patient data and PHI. Completed no-matches use credits; system errors are credited back. How your data is handled.
Operating model
Built for provider files that need a clear answer.
NPI Finder is intentionally narrow: it resolves provider and organization rows, verifies candidate identifiers, and returns the trail behind the match.
Cited
completed rows include the matched registry record, reasoning, candidate context, and source URLs
NPPES-first
healthcare entity matching starts with the federal registry before escalating to public web evidence
1 credit
per row run after the first 20 each month, which are free. One dollar buys 4 credits. System failures are credited back automatically
50k
rows per uploaded table in the app, with controlled parallel runs and streaming results
Built for
Explore the problem
Four ways teams describe finding NPIs in bulk.
Teams call this bulk NPI lookup, provider roster cleanup, or NPI enrichment. Data engineers often call it healthcare entity resolution. The job is the same: put the right NPI on every row and show the evidence.
Why it's hard
Looking up one NPI is easy. Looking up ten thousand isn't.
01
Claims bounce
A wrong NPI turns into denials, resubmissions, and follow-up work that costs far more than the original typo.
02
Directories go stale
Providers move, change names, rebrand, and merge. A spreadsheet that was right last quarter won't tell you which rows have changed since.
03
Lookup doesn't resolve identity
Manual NPPES searches take minutes per row and struggle with nicknames, DBAs, and misspellings. Fuzzy matching is faster, but it can't tell you whether a near-match is the same entity.
The method
Every row gets its own investigation.
NPI Finder runs a research process for each row across registry records, public web evidence, source pages, and NPPES verification. The evidence behind every answer is recorded before it's submitted.
- 01
Discover
NPI Finder searches NPPES first, expanding nicknames, former names, DBAs, and wildcards. It searches the public web when the registry cannot see a misspelling, marketing name, or bare domain.
- 02
Verify
NPI Finder screens candidate NPIs with the check-digit algorithm, then verifies each 10-digit number against the federal registry. An unverified fallback receives low confidence and a review flag.
- 03
Match
The name-identity gate requires the registered name to represent your entity. Address, ZIP, and phone can support a name match but never replace one. A different practice at the same address is rejected.
- 04
Show its work
Each completed row returns the chosen NPI, confidence rating, matched registry record, other candidates, and source URLs. You can review the decision later.
- nppes_search“okafor, michael” · Spokane, WA3 candidates
- web_search“Dr. Mike Okafor Spokane internal medicine”nickname evidence
- web_fetchspokane-internalmed.com/providersaddress confirmed
- nppes_verify1248573903✓ check digit · ✓ registry
- submit_answername gate passed · street matchconfidence: high
Investigation path: registry discovery · public web evidence · NPPES verification · confidence-rated answer or no-match.
What you get
Built for files where provider identity matters.
01
Streaming results
Rows land in the grid the moment they resolve, so you can review early results while the rest of the batch runs.
02
Graded confidence
Each result receives a high, medium, or low rating based on how well the registry record matches your street, ZIP, city, state, and phone.
03
Explained no-matches
When the name-identity gate fails, the row comes back as a no-match with the reasoning and sources from the investigation, rather than the nearest plausible-looking record.
04
One-click reruns
Rerun a single row, rerun everything below high confidence, or rerun the whole batch. Each rerun is a fresh investigation.
05
Complete CSV export
Export your original columns with the NPI, matched name and address, confidence, reasoning, candidates, and source URLs.
06
Individuals and organizations
Type 1 and Type 2, with an enforced filter so an organization is never resolved to a person's NPI or vice versa.
Validated claims
NPI facts you can check at the source.
Public NPI facts on this site link to the CMS and HHS documents they come from. Product claims describe how the app behaves, including its review limits.
Pricing
Simple pricing for messy provider files.
Per-row pricing keeps the cost tied to completed work. You can review a small batch, top up for a larger cleanup, and export the supported results you want to use in another system.
1credit
per row run. $1 buys 4
Completed no-matches stay charged because the research was completed.
20credits
free every month
Enough to judge the output on your own file before adding a card.
0credits
for our failures
System failures and cancelled unfinished rows are credited back automatically.
Start with your file
Put your next provider roster to the test.
Your first 20 rows are on us, and they reset every month. Upload the provider file you have now and review the evidence before you use it in another system.