APX Doppelgänger
Every record has one twin too many.
Doppelgänger is a deduplication system built for large datasets. It finds the duplicates your CRM cannot see, at a scale native tools give up on, and merges them without losing history.
Native dedupe was never built for your dataset.
CRM duplicate rules catch "Acme GmbH" twice. They do not catch "ACME Gmbh & Co. KG" from a trade-show import, the shell account your integration created, and the contact who exists three times because three tools disagree about her email. Past a few hundred thousand records, rule-based matching stops being a tool and becomes a liability.
And every duplicate is quietly expensive: split activity history, double-counted pipeline, sequences hitting the same person twice, and a support agent who cannot see the customer's real story. Doppelgänger exists because we kept solving this by hand, and the datasets kept getting bigger.
Deduplication that survives contact with real data.
Duplicates are not a cosmetic problem. They split histories, double-count pipeline and derail every automation that assumes one customer is one record.
Built for large data
Millions of records are the starting point, not the limit. Doppelgänger matches at a scale where native dedupe tools and spreadsheet heroics both give up.
Matching beyond exact
Fuzzy, phonetic and relationship-aware matching finds the twins that typos, imports and integrations have hidden from rule-based tools.
Merges you can trust
Survivorship rules keep the best of every field, and a full merge history means no record is ever lost and no merge is ever a mystery.
Preview before merge
Every run starts as a simulation: see exactly what would change, sample the matches, tune the thresholds, then commit.
Cross-object awareness
Accounts, contacts and leads are matched together, so merging a company never orphans its people, deals or tickets.
Continuous protection
After the big clean-up, Doppelgänger keeps watching imports and integrations, so the dataset never rots back.
For datasets that outgrew the rules.
Enterprise CRM owners
Six or seven digits of records, years of imports, multiple source systems, and a mandate to finally trust the count.
Ops teams mid-migration
Merge duplicate universes before they land in the new system, not after they have poisoned it.
Outbound & ABM teams
Stop burning sender reputation and ad spend on the same person under four different spellings.
From pipeline to product.
Proven on client data
The matching pipeline has cleaned millions of records in our data-quality engagements, tuned dataset by dataset.
Productized runs
Packaged clean-up runs with preview, survivorship rules and full merge history, operated by us on your extract.
Connected & continuous
Direct CRM connection with an ongoing watch mode, so one customer stays one record.
One customer, one record. Finally.
Doppelgänger comes from years of cleaning large CRM datasets by hand and by script. We are productizing that pipeline, waitlist members get their datasets in first.
