Deduplicate Master Data Across Systems
The same customer, vendor, or product ends up as multiple separate records across systems, and often within a single system, because of typos, different naming conventions between departments, or records created independently by different people who didn't check for an existing match first. This causes real operational problems: a customer gets two different account managers who don't know about each other, a vendor gets paid against two separate records with different payment terms, and reporting undercounts or overcounts activity depending on which duplicate happens to be pulled. Manual deduplication is slow and risky, because merging the wrong two records, two similarly named but genuinely different entities, can silently combine data that should have stayed separate.
STARTING PRICE
From €299
Standard tier · Multi-step workflow with AI extraction/decisioning and 2-3 integrations.
Get a quote →Saves roughly 5-8 hrs/week plus cleaner downstream reporting and fewer duplicate-payment risks.
How the automation works
We scan your records across systems and within each system for likely duplicates, using fuzzy matching on name, address, tax ID, email domain, and other identifying fields rather than requiring an exact text match, and score each candidate pair by match confidence. High-confidence matches (clear typos, formatting differences, obvious duplicate entry) can be merged automatically following a defined survivorship rule that decides which record's data wins per field. Anything below the confidence threshold, or any match involving records with conflicting critical data like different tax IDs, is routed to a human reviewer with both records shown side by side, so ambiguous cases get a human decision instead of an automated guess that could merge two genuinely different entities.
Process flow
- 01
Scheduled or triggered scan trigger
A deduplication scan runs on a schedule, or is triggered by new record creation, across the connected systems (CRM, ERP, vendor management).
- 02
Fuzzy match candidate duplicates ai
Records are compared using fuzzy matching on name, address, tax ID, email domain and other identifying fields, generating a confidence score per candidate pair.
- 03
Apply survivorship rules ai
For each field in a matched pair, a defined rule determines which record's value should survive the merge (most recently updated, most complete, or a specific system of record).
- 04
Auto-merge high-confidence matches integration
Matches above the confidence threshold, with no conflicting critical fields, are merged automatically, with the merge logged and reversible for a defined window.
- 05
Route ambiguous matches for review output
Lower-confidence matches or pairs with conflicting critical data are routed to a human reviewer with both records shown side by side for a manual decision.
Inputs
- Records across connected systems
- Field weighting and fuzzy match configuration
- Survivorship rules per field
- Confidence threshold for auto-merge vs. review
Outputs
- Merged master records
- Deduplication audit log with reversible merge history
- Human review queue for ambiguous matches
- Duplicate rate report by system and record type
Works with
Prefer a fully custom build instead of an off-the-shelf integration? We scope both options during your free consultation — most jobs like this one work fine on standard connectors, but higher-volume or non-standard systems sometimes need bespoke API work, reflected in the complex tier.
Where this goes wrong if you get it wrong
- Auto-merging two records with a high name and address similarity but a different tax ID or registration number is the classic failure mode — two genuinely different entities (a franchise location and its parent company, two businesses with a similar name) can look like duplicates to a naive fuzzy match, so critical identifying fields need to hard-block auto-merge on any mismatch.
- Survivorship rules need to be explicit per field, not a blanket 'most recent wins' — a vendor's payment terms might be correct in an older record while their contact details are more current in a newer one, and merging on 'most recent' alone can silently overwrite the correct payment terms with stale or wrong data.
- Deduplication run against live transactional systems needs to preserve every historical foreign-key reference (invoices, orders, activity history tied to the record being merged away) or the merge breaks reporting and audit trails even though the master record itself looks correct.
- A merge should be reversible for a defined window after it happens — even well-tuned matching produces occasional false positives, and without a rollback path a wrongly merged pair means manually reconstructing two records from combined data, which is far more work than the deduplication was meant to save.
Frequently asked questions
How do you avoid merging two records that just happen to look similar?
Critical identifying fields like tax ID or registration number hard-block automatic merging on any mismatch — those cases always go to a human reviewer rather than being merged on name and address similarity alone.
Can we control which record's data wins when two duplicates are merged?
Yes, survivorship rules are configured per field, so you can specify that payment terms always come from your ERP as the system of record while contact details come from whichever record was updated most recently.
What happens to historical transactions tied to a record that gets merged away?
They're re-pointed to the surviving record automatically as part of the merge, so historical invoices, orders, or activity remain intact and attributable.
Is a merge reversible if it turns out to be wrong?
Yes, every merge is logged with the pre-merge state preserved for a defined window, so it can be rolled back if a reviewer later determines it was a false match.