One customer,one record.
We find the duplicate customers, members and suppliers hiding across your systems and resolve them into one record everyone can trust.
01Who it is for
The problems we are usually called about.
Organisations whose customers, members, patients or suppliers appear in several systems under slightly different names.
The same person, five times
Each system has its own record, so nobody can say how many customers there really are.
Mail, calls and bills sent twice
Duplicates cost money and annoy the people on the receiving end.
Mergers and new systems
Every acquisition or replacement system adds another overlapping set of records.
Matches nobody can explain
An existing tool links records, but cannot say why, so its results are not trusted.
02What we do
Matching you can explain.
Matching
Records linked across systems despite typos, missing fields and changed names.
Golden records
One best version of each customer, built from the most reliable source for each field.
Review queues
Uncertain matches sent to a person, with the evidence side by side.
Ongoing master data
New records matched as they arrive, so the problem does not grow back.
03How it runs
Step by step.
- 01
Scoping call
We look at which systems hold the records and what a wrong match would cost you.
- 02
Profile and measure
We measure the duplicate rate and data quality on a real sample, so the size of the problem is known.
- 03
Build the matching
Rules and scoring are tuned to your data, with clear thresholds for automatic and manual decisions.
- 04
Review queue
Uncertain cases go to your reviewers with the evidence side by side.
- 05
Run continuously
New and changed records are matched as they arrive, and the single record is fed back to your systems.
04What you get
Delivered, documented, yours.
A measured duplicate rate
Before and after, on your own data.
Match reasons you can read
Field by field, for every link the system makes.
A full decision log
Automated and manual decisions recorded for audit.
Tools we work with
Your stack, not ours
From our work
Member identity resolution
We matched 100,000 health-insurance member records from enrolment, claims and third-party files into single identities. Uncertain matches go to a reviewer, and every decision is logged.
Questions
What clients ask first.
How accurate is the matching?
We measure it on your own data before anything is merged, and report precision and the share of records sent for review. You set where the thresholds sit.
What happens when the system is unsure?
The case goes to a person. They see both records, the fields that agree and disagree, and the reason it was flagged.
Can a wrong merge be undone?
Yes. Every decision is logged and source records are kept, so a link can be reversed.
Does this replace our CRM?
No. Your systems stay. The single record sits alongside them and can be fed back into each one.
Is it suitable for regulated data?
Yes. We build inside your environment, and every automated and manual decision is recorded for audit.
Tell us what you run.
Thirty minutes with an engineer. You leave with a clear scope, a timeline and a recommended approach.
