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INH System
Service
Document & Data Processing Automation
Best For
Teams manually re-typing data from invoices, forms, contracts or scanned documents into their business systems
Timeline
2 to 6 weeks per document pipeline
Process
Document Audit · Extraction Design · Pipeline Build · Validation & Review · Testing & Docs
Deliverables
Document audit and extraction plan, OCR and AI extraction pipeline, field mapping to your systems, confidence scoring and validation rules, human-review fallback workflow, documentation for your team
Summary

INH System automates document and data processing by extracting structured data from invoices, forms, contracts and scanned documents — validating accuracy and routing it directly into your CRM, ERP or spreadsheets. Anything the model can't read reliably is flagged for quick human review, so automation never comes at the cost of accuracy.

INH System · Document & Data Automation

PAPER
INTO
DATA.

Document and data processing automation that turns invoices, forms and contracts into structured data — no manual entry, no re-typing, no bottlenecks.

2–6 weeks
Typical timeline
No manual entry
Data extracted automatically
Audit-ready
Accuracy you can verify
Document extraction pipeline
EXTRACT
DATA
Invoices
Contracts
Forms
Receipts
ID Docs
Emails
All documents processing · No backlog
Extracted · Validated · Delivered
The manual data entry problem
✕ Re-typing data from PDFs and scans✕ Invoices entered by hand into the CRM or ERP✕ Contract terms buried in unstructured text✕ Data entry errors slip into your systems✕ Hours lost to manual document review✕ No searchable record of what was received
What we automate

Real document processing examples

These are the kinds of documents and data we process most commonly. If you have something different, we can still help.

Invoice processing
Email → OCR → ERP
Invoices extracted, validated and posted directly into your accounting system, with totals and tax fields held to a higher confidence threshold than reference numbers.
Contract data extraction
Upload → AI Extraction → CRM
Key terms, dates and parties pulled from contracts into structured records, with ambiguous clauses flagged for review rather than guessed at.
Form digitisation
Scan → OCR → Database
Paper forms converted into structured, searchable digital records, including handwritten fields routed to manual review when confidence is low.
ID and KYC verification
Upload → Extraction → Verification
Identity documents parsed and verified automatically during onboarding, cross-checked against expected formats for the document type.
Receipt and expense capture
Photo → OCR → Spreadsheet
Receipts logged and categorised without anyone filing a manual expense report, even when photographed at an angle or under poor lighting.
Email attachment processing
Inbox → Extraction → CRM
Attachments pulled from incoming email and logged automatically, with document type detected first so the right extraction rules apply.
Document processing
The outcome

Every document processed the moment it arrives. No backlog, no re-typing, no bottleneck.

Our Process

How we automate your documents.

Document Audit
Step 01
Document Audit
Our approach

How we approach document automation

01
Accuracy over speed
We validate every extraction before it reaches your systems. Wrong data moving fast is worse than no automation at all, particularly for financial fields (totals, tax amounts, account numbers) where a confident-looking wrong answer can cause real downstream problems if it isn't caught.
02
Built for your documents
We train extraction on your actual invoices, forms and contracts, not a generic template that breaks on real-world variation: different suppliers, different layouts, scanned copies with skew and noise, and the inconsistent formatting that shows up once you're processing hundreds of documents rather than the ten clean samples used in a demo.
03
Human review where it matters
Low-confidence extractions are flagged for a quick manual check instead of silently entering your systems wrong. The review queue is prioritised so the fields most likely to cause downstream problems get looked at first, rather than treating every flagged field as equally urgent.
04
Leave it documented
Every pipeline is documented (field mappings, confidence thresholds, escalation logic, known edge cases) so your team can understand, manage and extend it, adding a new document type or adjusting a threshold, without needing us every time.
05
Realistic about limits
OCR and extraction accuracy depends heavily on input quality. We tell you upfront which document types will need more manual review (poor scans, handwriting, unusual formats) rather than promising blanket accuracy figures that don't hold up once real documents start arriving.
Deliverables

What you receive

Document audit and extraction plan
OCR and AI extraction pipeline
Field mapping to your systems
Confidence scoring and validation rules
Human-review fallback workflow
Documentation for your team
Exception and escalation handling for low-confidence documents
Document data automation

Automated Extraction vs. Manual Data Entry

Automated Extraction
✓ Documents processed in seconds
✓ Consistent accuracy every time
✓ Data flows straight into your systems
✓ Low-confidence items flagged for review
✓ Scales without adding headcount
✓ Full audit trail of every document
Manual Data Entry
✕ Someone retypes every document
✕ Accuracy depends on who's typing
✕ Data sits in inboxes and folders
✕ Errors discovered after the fact
✕ More documents means more people
✕ No consistent record of what came in
FAQ

Common questions

Invoices, purchase orders, contracts, forms, receipts, ID documents and more, anything with a reasonably repeatable structure. Free-text or highly inconsistent documents can still be processed, but typically need a higher proportion of human review rather than full automation.

Accuracy depends on document quality and consistency: clean, digitally-generated documents extract more reliably than poor scans or handwritten forms. We validate extracted data and flag low-confidence fields for manual review rather than guessing, and we tune thresholds per field based on how costly an error in that field actually is.

Directly into the systems you already use, your CRM, ERP, accounting software or a spreadsheet, mapped to your existing fields and formatted the way those systems expect (date formats, currency, required versus optional fields).

They're flagged for quick human review instead of silently failing or entering incorrect data into your systems. The flagging is field-level where possible, so one uncertain field doesn't hold up an otherwise-clean document.

Most document pipelines take 2 to 6 weeks depending on document variety, how many systems need to be connected, and how much historical document variation needs to be accounted for during training and testing.

Yes, we use OCR to digitise scanned and photographed documents before extraction begins, and we account for common scan issues (skew, low contrast, partial pages) rather than assuming every input is a clean digital PDF.

Yes. Because the pipeline and its mappings are documented, adding a new document type is a scoped addition rather than a rebuild, though it still needs its own testing pass since a new layout or field set behaves differently from what the pipeline has already seen.