Spend less time sorting duplicate and conflicting records.
We build a workflow that matches inconsistent records and applies agreed corrections, using AI where names and context need interpretation. Uncertain matches stay visible for review.
When it helps
When comparing names, resolving duplicates and correcting conflicting details keeps taking time from the work those records support.
The same customer appears under different names.
Abbreviations, spelling differences and missing details leave your team checking whether separate entries belong together.
- Standardize agreed formats and use reliable identifiers where available.
- Use AI to suggest matches when wording and context need interpretation.
- Show uncertain suggestions with the original records for review.
The records match. The details disagree.
Different sources give different answers, and deciding which address, name or status to keep becomes another manual task.
- Agree which source to trust for each field and type of update.
- Apply approved corrections and hold unresolved conflicts for a decision.
- Keep a record of where values came from and what changed.
Every new batch brings the same cleanup work.
New customer, supplier or product lists arrive with familiar inconsistencies. The next import restarts a job your team has already done.
- Build a repeatable process around the agreed sources and record types.
- Send checked matches and corrections to the chosen destination.
- Flag failed or incomplete runs so missing work is visible.
Suggested matches still need too much checking.
Similar names can belong to different people or businesses. Your team cannot rely on a match just because it looks plausible.
- Compare proposed matches with examples your team has confirmed.
- Check both incorrect matches and duplicates the workflow misses.
- Agree when records can proceed, need review or should remain unmatched.
A working process for cleaner, more consistent records.
We agree the records, matching criteria and permitted changes before building. Clear rules handle straightforward cases; AI helps interpret the differences those rules cannot reliably resolve.
Match, check and apply agreed corrections.
- Review of selected sources, record types, identifiers and which values to trust.
- Matching and cleanup rules, with AI interpretation for the agreed cases that need it.
- A working flow for candidate matches, review decisions and approved outputs or updates.
- Evaluation using representative examples, including false matches, missed matches and conflicting details.
- Checks for repeated input and failed runs, a change history, recovery arrangements and handover.
Cleaner records, with decisions you can trace.
- A workflow connected to the agreed sources and destination, with review handling in place.
- Documented matching criteria, cleanup rules and approval limits for changing records.
- Evaluation results and a record of applied changes, unresolved conflicts and unmatched entries.
- Instructions for checking runs, reviewing exceptions, recovering changes and maintaining the process.
Beyond the agreed records and workflow.
Any further work is optional and agreed separately.
- Cleaning all historical business data or replacing company-wide data management systems.
- Full platform migrations or changes to records outside the agreed sources and approval rules.
- Ongoing operation, routine exception handling and maintenance after the agreed handover.
How it works
We handle the implementation and checks. Your input stays focused on what the records mean, which information to trust and the decisions that need business approval.
Agree the records and matching rules
We start with your existing systems, examples and requirements. Together we agree the sources, permitted corrections, review rules, completion criteria, price and timing.
Build the workflow and review path
We connect the agreed sources, implement matching and cleanup, and make uncertain cases available for review. We prepare the change records and recovery steps before live updates.
Check matches before applying changes
We compare results with confirmed examples and check errors, missed matches and failed runs. We refine the process within scope and explain what can proceed automatically and what still needs review.
Apply approved changes and hand over
We activate the agreed workflow or deliver checked outputs, then verify the results. Your team receives the rules, change history and operating instructions, with clear ownership of reviews and future runs.
Which records keep needing another check?
An informal 30-minute call to discuss the records taking up your team's time and see whether Record Matching and Data Cleanup Workflow is the right fit.