Data Capture & Structuring
A scanned page is a picture. We turn it into data.
Turning documents, forms and records into clean structured data your systems can actually use — validated against your rules, not just typed in.
Digitized is not the same as usable
Scanning gets you an image. You still cannot total a column, filter by date, or push it into your accounting system. The gap between "we have a PDF of it" and "our software can use it" is where most digitization projects quietly stall.
- A digital archive you can read but cannot query or report on
- Data arriving in twelve different formats from twelve sources
- Errors found downstream, months later, in a reconciliation
- Volume spikes your team cannot absorb without hiring
What we handle
Structured extraction
Fields pulled from invoices, claim forms, applications, surveys and registers into whatever schema your system expects.
Validation against your rules
A date that cannot be in the future. A total that must equal the sum of its lines. An ID that must exist in your master list. Caught at capture, not in an audit.
Cleaning and deduplication
Merging duplicate customer records, normalising addresses and names, standardising units and formats across sources.
Legacy migration
Getting data out of an old system that has no export worth using, and into the new one with its integrity intact.
AI-assisted, human-verified
Models do the first pass at volume; trained reviewers handle the exceptions and the low-confidence cases. This is how the accuracy and the throughput both stay high.
How an engagement runs
- 01
Define the target
We agree the exact output schema and the validation rules before anything is processed. Most data problems are specification problems.
- 02
Sample and calibrate
A sample batch establishes the real accuracy rate and surfaces the edge cases your documents contain — the ones nobody remembers until they appear.
- 03
Process at volume
AI extraction at scale, with every low-confidence field routed to a trained human reviewer. Two-pass verification on anything financial or regulated.
- 04
Deliver and reconcile
Data delivered in your format, with an accuracy report and a reconciliation you can check against source totals.
What you actually receive
- Data in your schema — CSV, JSON, SQL or a direct API load
- A written validation-rule specification, agreed up front
- Field-level accuracy reporting per batch
- Exception log: every record that needed a human decision
- Reconciliation against source control totals
- Signed confidentiality terms covering every person on the project
Next step
Have a data capture problem you want a second opinion on?
Send us the messy version. We do not need a written brief to have a useful first conversation.