AI Services
Document Processing & OCR
One of the more reliable AI applications available, because the output is checkable against the source document.
Document processing turns invoices, forms and scanned PDFs into structured data automatically. The Nexclick builds extraction with a confidence threshold and a review queue, so uncertain results reach a person rather than entering your systems as quietly wrong numbers that nobody checks.
Is this you?
What usually prompts the call
- Someone types invoice data into your accounting system every week.
- You receive orders as PDFs and re-key them into the order system.
- Forms arrive scanned and someone transcribes them into a database.
- A backlog of paper records needs digitising and nobody has the time.
What we do
The actual deliverables
Things that appear on an invoice, not adjectives.
- Assess the documents realistically
- Quality, consistency and variety of layouts. Clean digital PDFs extract very reliably; poor scans of handwritten forms do not, and we will say which you have.
- Define the fields and their rules
- What to extract, what format it must be in, and what constitutes a valid value. Validation catches a large share of extraction errors before they reach anyone.
- Build extraction with confidence scoring
- Every field extracted with a confidence value, so uncertain results can be routed differently from confident ones rather than treated identically.
- Review queue for low confidence
- Anything below the threshold goes to a person with the document alongside it. This is what makes the whole thing safe to trust.
- Cross-validation against your own data
- Supplier names, order numbers and totals checked against records you already hold. Catches errors confidence scoring alone misses.
- Integration into the destination system
- Extracted data written into accounting, ERP or your database directly, because a spreadsheet of extracted values has moved the typing rather than removed it.
- Accuracy measurement, reported
- Field-level accuracy measured against a manually verified sample, so you know what the system actually achieves rather than what a vendor claimed.
Comparison
What extracts reliably, and what does not
Accuracy is determined by the documents, not by the technology. Check your own document mix against this before anyone quotes you an accuracy figure.
| Document type | Reliability | Notes |
|---|---|---|
| Digital PDF invoices, consistent supplier | Very high | Near-perfect with validation |
| Digital PDF invoices, many suppliers | High | Layout variety handled well by modern models |
| Structured forms, typed | Very high | Fixed positions make this straightforward |
| Clean scans of printed documents | High | Good scan quality is the deciding factor |
| Tables inside PDFs | Moderate to high | Column alignment is the usual failure |
| Photographs of documents on a phone | Moderate | Lighting, angle and creases all degrade it |
| Faxed or heavily compressed scans | Low to moderate | Degraded source; expect a review queue |
| Handwritten block capitals | Moderate | Better than expected, still needs review |
| Handwritten cursive | Low | Do not plan a process around this |
| Documents in mixed languages | Moderate | Depends on the languages involved |
| Documents with stamps or annotations over text | Low | Obscured text cannot be recovered |
How it works
Step by step, with timeframes
Timeframes are typical rather than guaranteed, and they assume we get account access and approvals when we ask.
- 01Week 1–2
Sample and assess
A representative sample of real documents, including the messy ones. Determines achievable accuracy before anything is quoted.
- 02Week 2–5
Build extraction and validation
Field extraction, validation rules and confidence thresholds, tested against a manually verified set.
- 03Week 5–7
Review queue and integration
Human review interface and writing into the destination system, with an audit trail per document.
- 04Week 7–11
Run and tune
Live with all output reviewed initially, then thresholds tuned as accuracy per field becomes clear.
What you get
Reporting and ownership
- Measured field-level accuracy against a manually verified sample, reported honestly.
- A confidence threshold and review queue, so uncertain values never enter your systems silently.
- Validation and cross-checking against records you already hold.
- Direct integration into the destination system, not a spreadsheet of extracted values.
- A per-document audit trail linking every value back to the source page.
Tools and platforms
- Commercial document AI and OCR APIs
- Multimodal LLM APIs for complex layouts
- Validation and cross-referencing rules
- Review queue interface
- Accounting, ERP and database integrations
Timeline
How long this actually takes
Seven to eleven weeks depending on document variety. Accuracy is the honest conversation: clean digital PDFs with consistent layouts extract extremely reliably. Photographs of creased delivery notes and handwritten forms do not, and no vendor claim changes that. We assess a real sample before quoting so the expectation is set on evidence. The review queue is what makes the system safe regardless — a value that goes in wrong and unnoticed is worse than one that waits for a person.
Pricing model
Fixed-price project
Fixed price after a sample assessment, since document quality determines the effort. Per-document API costs are yours directly and modelled at your volume.
Questions
Document Processing & OCR questions
How accurate is this really?
It depends entirely on your documents, which is why we assess a real sample before quoting. Clean digital PDFs with validation reach very high field-level accuracy. Photographs of creased handwritten forms do not, and any vendor quoting a headline accuracy figure without seeing your documents is quoting a best case.
What happens to values it is unsure about?
They go to a review queue with the source document displayed alongside, for a person to confirm or correct. That is the whole safety mechanism. A wrong value entering your accounting system silently is considerably worse than one waiting thirty seconds for a human.
Can it handle invoices from hundreds of different suppliers?
Yes. Modern document AI handles layout variety far better than the template-based systems of a few years ago, which needed configuring per supplier. Consistency helps and is no longer a requirement.
Does the data have to leave our systems?
It goes to a document AI provider for processing unless we deploy something self-hosted. Which provider, where they process it, and their retention terms are documented as part of the build — and it matters for confidential or personal data, so it gets scoped explicitly.
Is this worth it for our volume?
The arithmetic is straightforward: minutes per document times volume against build and per-document cost. A few dozen documents a month rarely justifies it. Several hundred usually does, and thousands certainly does. We do that calculation before quoting.
How does this relate to a knowledge base?
This extracts structured data — amounts, dates, references — into a system. A RAG knowledge base makes document content searchable and answerable in natural language. Different outputs, and they frequently share the same extraction step where documents are scanned.
Last reviewed 28 July 2026.
Tell us what you are trying to fix
A 20-minute call, no pitch deck. The Nexclick will tell you what we would do, roughly what it costs, and whether we are the right people for it.