Extraction on the bulk
Structured data pulled from documents with a confidence score attached to every field, not just the document.
Data Entry
RunAutomate the ninety percent. Staff the ten that matters.
Document processing and data operations where extraction handles the volume and trained reviewers handle everything below the confidence threshold.
Typical stack
- Claude
- Azure Document Intelligence
- PostgreSQL
- Python
- NetSuite
- SAP
Written for: An operations manager whose team spends its week re-typing documents between systems.
Who it is for
Is this built for a business like mine?Built for businesses where documents are the bottleneck.
Where paper and PDFs stand between an event happening and a system knowing about it.
What we see
Applications, statements and claims arrive as unstructured files and a qualified person reads every one.
What we build
Extraction with per-field confidence, review of anything uncertain, and a lineage record for every figure.
The detail that matters
The confidence threshold is yours to set, because it is a risk decision rather than a technical one.
What we see
Rate confirmations, BOLs and PODs are re-keyed into the TMS by hand.
What we build
Extraction into your TMS on its terms, with exceptions surfaced rather than silently corrected.
The detail that matters
We work with the legacy interface you have.
What we see
Purchase orders arrive as PDFs and part numbers are typed by hand all day.
What we build
Extraction validated against your live catalogue, with anything uncertain held.
The detail that matters
Validated against real stock. A plausible SKU is not accepted.
What we see
Intake forms are completed on paper and entered twice.
What we build
Digitisation and entry to the boundary your data operates under.
The detail that matters
Handled inside your boundary where the data cannot leave it.
What we see
Tenant screening documents are collected and checked by hand, one applicant at a time.
What we build
Document collection, extraction and verification against your screening criteria.
The detail that matters
Criteria applied identically to every applicant, which is also a fairness property.
And who it is not for
If your documents are already structured — clean CSVs, an API, consistent templates — you need an integration, not a data desk. We will scope that instead.
The problem
Do they understand what is actually going wrong?Skilled people are spending their week re-typing.
Every business accumulates a layer of transcription work: information arrives in one format and is needed in another, and a person bridges the gap. It is invisible on the org chart, it grows with the business, and it is done by people who were hired for something else.
The same information is entered into two or three systems.
Backlogs form whenever someone is on leave.
The error rate is unknown because nothing is checked systematically.
The work is done by people whose time is worth considerably more.
What we build
What exactly would I be buying?A threshold, not an either-or.
Full automation fails on the messy tail and full manual entry is unaffordable. The workable design is a confidence threshold: extraction handles what it is certain about, people handle the rest, and the line between them is a number you own.
Review below the line
Anything under threshold reaches a trained reviewer with the source document beside it.
Validated against your data
Extracted values checked against your real catalogue, account list or reference data — so a plausible wrong value fails.
A measured error rate
Sampled and reported, so accuracy is a number you can hold us to rather than an assumption.
How it works
How does this actually function?Where the threshold sits, and who moves it.
The threshold is a business decision about the cost of an error versus the cost of a review. It is yours, it is visible, and it moves on evidence.
Document
Extract
Score
Auto-commit or review
Below threshold → humanValidate
Deliver
Confidence is per field. A document with one uncertain figure gets one field reviewed, not the whole page re-keyed.
Reviewed corrections feed back, so the categories that fail most often improve first.
Accuracy is sampled independently of confidence, because a system confident and wrong is the failure that matters.
What changes
What is different afterwards?What is different afterwards.
Your team stops transcribing
The bulk is handled automatically and the exceptions by reviewers, so skilled staff return to the work they were hired for.
Hours returned from transcription work.
Accuracy becomes visible
Sampled and reported rather than assumed, which is usually the first time the real error rate has been known.
Sampled accuracy rate, reported weekly.
Volume stops causing backlogs
Capacity flexes with document volume, so leave and seasonality stop creating queues.
Turnaround time held under volume changes.
How we deliver
How does this start, and what do I get at each step?Six stages, and every one has an exit.
You can stop after any stage with something useful in hand. That is the point of naming the artefacts rather than the activities.
- 01
Document study
Real samples across the full messy range, not the clean examples. The tail is where the cost is.
- Document type inventory
- Volume and variation analysis
- Automatable share, quantified
- 02
Threshold design
What a wrong field costs, and therefore where the confidence line belongs — set per field, not per document.
- Field-level threshold policy
- Error cost analysis
- Validation rule set
- 03
Extraction build
Extraction, scoring and validation against your reference data.
- Extraction pipeline
- Validation against your data
- Baseline accuracy measured
- 04
Reviewer training
Your documents, your terminology, your edge cases.
- Trained review team
- Review guidelines
- Quality rubric
- 05
Parallel run
The new path runs alongside the existing one and the outputs are compared, which is the only honest way to measure accuracy.
- Parallel run comparison
- Accuracy against current process
- Threshold adjusted on evidence
- 06
Run
Full volume with sampled quality assurance and weekly reporting.
- Weekly accuracy report
- Turnaround metrics
- Exception trend review
Who runs the desk
How does this start, and what do I get at each step?Every desk ships with a lead and a QA function.
A pool of agents with no accountable owner is how outsourced quality degrades — slowly, invisibly, and then all at once. Four roles exist on every account, and the smallest engagement gets all four.
- 01
Agents
Trained on your product, your tone and your escalation boundaries, and assessed before they touch live work. Named and consistent on dedicated plans.
- 02
Team Lead
Accountable for the queue rather than working in it — coverage, SLA, escalations and the shift handover. One person you can name when something goes wrong.
- 03
QA
Samples completed work against a written rubric, independently of the lead. Disagreements between agents are treated as a defect in the knowledge base, not in the agent.
- 04
Account lead
Runs the weekly report and the standing review where the knowledge base actually changes. The person who tells you the number went the wrong way.
QA sampling rate and the review cadence are agreed during onboarding and reported weekly against target — including the weeks it was missed.
Questions
But what about the thing that worries me?The questions people actually ask.
How accurate is it?
We measure it during the parallel run rather than quote a figure, because accuracy depends entirely on your document quality. The parallel run also measures your current manual error rate, which is the comparison that actually matters and is usually higher than expected.
Who sets the confidence threshold?
You do. It is a trade between review cost and error cost, and only you know what an error costs in your business. We show the curve and recommend a starting point.
What about sensitive documents?
Handled to your requirements, including processing inside your infrastructure where data cannot leave it. That constrains the architecture, so it is agreed during the document study rather than after.
Can you work in our systems?
Yes — writing into your ERP, TMS or database directly. Where the interface is a file drop or a legacy endpoint, we work with that rather than requiring you to modernise first.
What happens to documents that fail extraction?
They go to a reviewer with the source alongside, and the failure is categorised. Recurring failure categories drive the next round of improvement rather than being absorbed as permanent manual work.
The other half
The threshold is only honest if someone works below it.
A vendor selling only extraction software has every reason to set the confidence threshold low, because everything below it becomes your problem. We staff the review queue, which means the threshold is set where the error cost says it should be — our incentive and yours point the same way.
AI DevelopmentStart
What is the next step?Tell us the volume, we will tell you the shape.
Send us a week of real numbers — calls, chats, tickets, documents — and we will come back with a coverage model and what it would cost.
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