Duplicates are a symptom. The cause is upstream, where two systems disagree about what makes a person the same person. Fix it there and the merges stop. From $4,999.
- Map
- Normalize
- Sync
scope-based
Who this is for
Revenue operations or marketing lead whose HubSpot portal keeps growing duplicate contacts faster than anyone can merge them.
The pain today
- Your team merges duplicates weekly and the count never goes down
- The same person exists three times with three different emails
- Reporting overstates contact counts and nobody trusts the number
- Two systems feed HubSpot and neither owns the match key
- HubSpot's built-in dedupe tool cannot see why records differ
The outcome you get
- The match and merge rules agreed in writing before any record moves
- Normalization and deduplication applied before anything reaches HubSpot
- An audit trail from any HubSpot record back to its source payload
- Sync monitoring with per-source pause control, inside your own account
- Code, infrastructure, and documentation yours as work made for hire
Why duplicates keep coming back
Merging duplicates is treating the symptom. If two systems keep writing records into HubSpot and neither one owns the definition of identity, the duplicates regenerate faster than a human can clear them. Every merge is work that will be undone by the next sync.
The usual root causes are dull and specific. Email is used as the match key, but one system stores a personal address and the other a work address. Casing and whitespace differ so two identical strings do not compare equal. One system creates a record before the email is known, so it lands with a placeholder. A form fills a field the API also writes, and the two race. None of these are fixed by merging.
The match and merge rules agreed in writing before any record moves
Fixing identity at the source
The fix is a match key agreed before any record moves, plus normalization applied before anything reaches HubSpot. That means deciding what identity means for your business: email alone, email plus domain, an external ID carried from the source system, or a composite key with documented fallbacks.
Then every payload gets normalized to that key on the way in, and every write is safe to repeat without creating duplicates so a retry cannot create a second record. Where two records genuinely tie, the merge rules are explicit and the outcome is logged rather than silently chosen. Existing duplicates get a one-time reconciliation pass against the same rules.
2M+: Records processed.
This has been solved at scale
Reevia ran four separate platforms feeding HubSpot with raw records, inconsistent formatting, and no shared identifiers. I built the layer that normalizes and maps every record to the correct object type before it lands, with full association handling and a traceable batch history. Over 2 million records processed. A lead from any of the four systems reaches HubSpot, standardized, in under 50 seconds, and the whole integration went to production in under four weeks.
What fixing this costs
The published floor is $4,999 and the real number comes from a mapping audit, in writing, before any build work starts. Scope moves with how many systems feed HubSpot, how many records exist, whether data flows one way or both, which objects are involved, and how much history needs cleaning. You can price your own scope on the calculator first. Every engagement carries a 14-day money-back guarantee and a 1-year bug warranty.
Recent proof
A comparable engagement, delivered and documented.
Four systems, one source of truth: HubSpot visibility for one of Brazil's largest vet networks
Built a custom integration layer connecting four source systems into HubSpot for one of Brazil's largest veterinary companies. Leads sync in under 50 seconds.
Read the case studyRelated services
The same work, framed for a different situation.
Frequently asked questions
The questions prospects ask before they book.
Yes. A one-time reconciliation pass runs against the same match and merge rules as the ongoing sync, so the cleanup and the prevention agree. Doing only the cleanup without fixing the source means the duplicates come back.
Use it if it works for you. It compares records already inside HubSpot and suggests merges, which helps when duplicates are occasional. It cannot see why the records differ upstream, so if a system keeps writing them it stays a treadmill.
The merge rules decide, and the decision is logged rather than silent. Which field wins, which record survives, and what happens to associations are all part of the mapping audit.
The audit takes a few days because the identity decisions are the hard part. The build follows from them. A single-source deduplication problem is much faster than a multi-system one.