Document Automation — Extract, Classify, Validate and Route Without Retyping
For UK operators paying skilled people to retype data from PDFs, forms, contracts, and reports. We build bespoke document automation pipelines that extract, classify, validate, and route — with structured data landing exactly where your operation needs it.
45–60 min · Free · No pitch
Document automation is a bespoke AI pipeline that takes incoming documents — PDFs, scans, photos, attachments — and turns them into structured data your operation can use, with extraction, classification, validation, and routing handled end-to-end. For UK mid-market operators it typically replaces 15–40 hours per week of retyping at £0.05–£0.50 per document against £2–£15 of human-handling cost. Builds ship in 3–14 weeks depending on scope.
What this can run.
Examples — not a feature list. Yours is shaped by the bottlenecks the discovery call surfaces.
Extract structured data
Pull the right values from any document — invoices, contracts, claims, EPCs, forms. Modern vision-language models read PDFs, scans, photos, and even handwritten paperwork.
Classify against your taxonomy
Decide what type of document each one is — against YOUR categories, your exceptions, your edge cases. Routes to the right schema, workflow, and team.
Validate against your rules
Does the invoice total match the line items? Does the contract reference an active customer? Is the EPC date inside the validity window? Validation logic encoded as part of the build.
Route + write back
File or action the document and write structured data back into Xero, QuickBooks, Sage, Salesforce, HubSpot, monday.com, or your bespoke case database — wherever your operation actually uses it.
Vision-language reading
GPT-4 Vision and Claude with document support read documents directly — typically better accuracy on real-world UK paperwork than legacy OCR. Model choice is part of the build, never a vendor lock-in.
Multi-source ingestion
Email inboxes, shared drives (SharePoint, Google Drive, Dropbox), customer upload portals, scanner/MFP integrations, mobile-app photo capture — all into the same pipeline.
What document automation actually does
Every UK mid-market operation eventually accumulates a flow of inbound documents that someone has to read, classify, and retype. Invoices into the accounting system. Contracts into the case-management database. EPC reports into the compliance ledger. Customer forms into the CRM. Photos and field reports into the project record. The cost isn't the documents — it's the human hours spent on the part that should be invisible.
A bespoke document automation pipeline handles the four stages end-to-end:
- Extraction — pulling the right values out of each document. Modern vision-language models read PDFs, scans, photos, and even handwritten forms with high accuracy.
- Classification — deciding what type of document each one is, often the harder problem when documents arrive without metadata. Goes to which schema, which workflow, which team.
- Validation — checking the extracted data against your business rules. Does the invoice total match the line items? Does the contract reference an active customer? Is the EPC date inside the validity window?
- Routing — filing or actioning the document and writing the structured data back into your CRM, accounting system, case-management database, or wherever your operation actually uses it.
Where document automation fits
- Accountancy and finance — invoices, expense receipts, bank statements, VAT returns, tax forms. The volume is high, the schemas are stable, the cost of retyping is measurable in hours per week.
- Legal and professional services — contracts, deeds, witness statements, case files, KYC documents. Classification against your taxonomy matters more than extraction accuracy on individual fields.
- Property, EPC and compliance — EPC reports, air permeability tests, energy assessments, gas safety certificates, planning documents. Often involves matching extracted data against a national register.
- Insurance and claims — claim forms, supporting documentation, medical records, third-party correspondence. Extraction plus validation against policy rules.
- HR and onboarding — right-to-work documents, qualification certificates, signed contracts, expense claims, timesheets.
- Construction and trades — site reports, completion certificates, material delivery notes, RAMS documents.
How it integrates with your existing stack
- Document sources: email inboxes (Microsoft 365, Google Workspace), shared drives (SharePoint, Google Drive, Dropbox), customer-facing upload portals, scanner/MFP integrations, mobile-app photo capture.
- Vision / language models: GPT-4 Vision and successors, Anthropic Claude with document support, specialist OCR engines where regulatory or accuracy needs demand it. Model choice is part of the build, not a vendor lock-in.
- Destinations: Xero, QuickBooks, Sage, Salesforce, HubSpot, monday.com, bespoke case-management systems, custom Postgres schemas — whatever your operation uses as its source of truth.
- Review surface: a bespoke operator-facing app where your team reviews low-confidence extractions, corrects edge cases, and approves before the data hits production. The review tool is part of the build, not an afterthought.
- Audit and retention: every document, every extraction, every decision logged with timestamp and confidence score. Retention policy and access controls scoped to your DPO's requirements.
What it costs
- Focused single-document-type pipeline (one document type, one destination system, one workflow): low five figures. Typical 3–5 weeks.
- Multi-document-type pipeline with classification, validation rules, and CRM/accounting integration: mid five figures. Typical 6–10 weeks.
- Operations-wide document automation across multiple departments with shared infrastructure: low six figures. Typical 10–14 weeks; ongoing retainer for new document types as the operation evolves.
Ongoing per-document costs are £0.05–£0.50 depending on length and complexity — against £2–£15 of human-handling cost per document for typical UK mid-market workflows. The 2026 cost guide has the full breakdown.
Live UK builds
- Thermova — bulk EPC matching against the national register, turning a day of manual portfolio lookups into minutes with Jaccard string-similarity scoring and match-confidence indicators. RdSAP 10 corrected-EPC workflow integrating Pulse air permeability testing with ATTMA Level 1 lodgement.
- TS Plastering — Telegram-to-website AI pipeline. Photos sent to a Telegram bot, GPT-4 Vision extracts scope/materials/finish quality, GPT-4 Turbo composes the full case study, result lands on the website in a "pending review" state for ~5 minutes of operator approval. Cost per published case study: £0.10–£0.30.
- Renew Energies — mobile field-survey app syncs site images back to the CRM in real time; EPC checks happen automatically at enquiry time, document data feeding the rest of the operation.
The full set is collected in the document automation case studies.
Before you book
- Custom AI Systems — the parent capability page covering document intelligence, anomaly detection, predictive scoring, and vision systems.
- AI for UK solicitors — sector-specific patterns for legal documents and case-file workflows.
- AI for UK accountants — sector-specific patterns for invoices, expense automation, and ledger work.
The before picture.
Stacks of inbound documents — invoices, contracts, EPCs, claim forms, certificates — pile up because no SaaS knows your taxonomy.
Your most expensive people spend hours retyping data from PDFs into spreadsheets, accounting systems, and case-management databases.
Off-the-shelf OCR gives you a text blob. Someone still has to read the blob, decide what's relevant, and key the right fields into the right system.
When a document doesn't match the standard schema (handwritten note, unusual format, edge-case content), it falls out of the pipeline entirely and someone handles it manually — every time.
The after picture.
Documents arrive, get extracted, classified, validated, and routed — automatically — against YOUR taxonomy and YOUR business rules.
Modern vision-language models read PDFs, scans, photos, and handwritten forms with 95%+ field-level accuracy on well-structured documents.
Structured data lands directly in your accounting, CRM, case-management or bespoke database. No spreadsheet middleman.
Low-confidence extractions route to a human review queue rather than auto-filing. The exceptions get flagged; the routine ~95% just flows.
Why bespoke beats off-the-shelf OCR / document AI.
OCR gives you a text blob. You still need a human to read it, decide what matters, and key the right fields into the right system.
A bespoke pipeline knows your document types, your fields, your validation rules, and where each finished document needs to go.
Why it matters: OCR is one component. The work between text-blob and useful-data is the part that costs hours.
Generic document AI is trained on the vendor's notion of a "standard invoice" or "standard contract".
A bespoke build trains on YOUR taxonomy — your categories, your exceptions, your edge cases — and routes accordingly.
Why it matters: If a SaaS classification scheme had matched your operation, you'd be using it already.
Edge-case documents (handwritten, unusual format, ambiguous category) fall out of the pipeline and someone handles them manually.
Low-confidence extractions route to a human review queue with the AI's reasoning trace attached — exceptions get human attention; the routine 95% just flows.
Why it matters: Automating 100% is a fantasy. Automating 95% with a clean review surface for the 5% is the durable answer.
How this gets built
The capability pages below describe the actual build patterns we use to deliver this. Pick the one that matches the part of your operation you want to fix.
Custom AI Systems (parent) →
The parent capability page covering document intelligence, anomaly detection, predictive scoring, vision systems.
AI Customer Service →
Sister use case — action-taking customer service often handles document submission flows.
AI CRM →
Document automation typically writes the extracted data back into CRM, deal, or case records.
AI Systems Integration (pillar) →
The integration pillar — wiring document automation into your existing case-management, accounting, and CRM systems.
AI Workflow Automation (pillar) →
The broader workflow pillar — automating the cognitive work consuming your team.
AI Implementation (pillar) →
The full implementation pillar — discovery, build, integration, deployment.
Common questions
Document automation is the work of taking incoming documents — PDFs, scanned forms, photos, emails with attachments — and turning them into structured data that the rest of your operation can use, without anyone retyping. The four stages are: extraction (pulling the right values out of each document), classification (deciding what type of document each one is and where it belongs), validation (checking the extracted data against your business rules), and routing (filing or actioning the document and writing the data back into your systems).
OCR (optical character recognition) is one component of document automation — the part that converts a scan into text. Off-the-shelf OCR is generic: it gives you a text blob. Document automation is the layer above OCR that knows your business: which document types you handle, which fields matter on each, which validation rules apply, where each finished document needs to go, and how to handle ambiguous cases. Modern document automation also uses vision-language models (e.g. GPT-4 Vision) which read documents directly without needing a separate OCR pass — usually with better accuracy on real-world UK paperwork.
Anything with a recognisable structure or recurring pattern: invoices, contracts, purchase orders, claim forms, EPC reports, deeds, witness statements, financial statements, KYC documents, tax forms, application forms, delivery notes, certificates, expense receipts, internal forms with bespoke schemas. Handwritten forms work in most cases (modern vision models read most legible handwriting well). Highly unstructured free-text documents need careful scoping but are usually feasible.
On well-structured documents (invoices, standardised forms, EPC reports), 95%+ field-level accuracy is achievable and exceeded routinely. The right framing isn't "is the AI perfect" — it's "is the AI more accurate than the human typing the data into a spreadsheet, and is the cost lower." For the right document types, both answers are yes by a wide margin. Low-confidence extractions get routed to a human review queue rather than auto-filed.
A focused build for one document type with one downstream system ships in 3–5 weeks. A multi-document-type pipeline with classification, validation rules, and integration into your case-management or accounting system sits in the 6–10 week range. Operations-wide document automation across multiple departments runs 10–14 weeks. Discovery surfaces this before any commitment.
A focused single-document-type pipeline is low five figures all-in for the build. A multi-document, multi-system pipeline sits in the mid-five-figure range. Operations-wide document automation runs into low six figures. Ongoing per-document costs are typically £0.05–£0.50 depending on complexity and length — against £2–£15 of human-handling cost per document for typical mid-market workflows.
Still have a question? Book a discovery call — direct line to me, Dean.
Every build is bespoke. Every build starts the same way.
A 45–60 minute discovery call. We map the bottleneck, scope the build, and tell you what it would cost — including whether it's the right shape at all.
Book a Discovery Call