One reliable customer record across systems
We reconcile the customer records your workflows depend on. Matching rules find exact duplicates, AI suggests less obvious candidates, and your team decides which records should actually be joined.
How we put it to work
Step 1 of 3
Agree what a good record contains.
We sample source lists and define required fields, ownership, and formats. Source identifiers are retained so proposed changes can be traced and reversed.
Your teamConfirms which system owns each field and what must be retained.
Step 2 of 3
Review likely matches.
AI helps compare spelling variations and incomplete records. Shared addresses or similar names alone do not authorize a merge; conflicts are shown side by side.
Your teamConfirms identity and decides which values are correct.
Step 3 of 3
Apply and monitor approved changes.
We test a small batch, keep an export of the original records, and synchronize approved changes through supported connections. Failed writes stay in a retry queue.
Your teamApproves the batch and checks the resulting records.
What your first engagement includes
- 01Review your setupConfirm existing features, data access, and system compatibility.
- 02Configure one workflowConnect the tools, define approvals, and handle exceptions.
- 03Test and hand overPilot on selected work, train your staff, and provide operating instructions.
