Cody
Full-stack software engineering
Builds the extraction, confidence and review workflow around your documents.
Document automation · UK
Paperwork in. Useful data out.
Invoices, purchase orders, drawings and forms turned into structured, reviewable data without asking your team to re-key every line.
Your docstested before a build is proposed
Flaggedlow-confidence fields go to a person
Traceableevery extraction keeps its source
Start with the real blocker.
Four signs the current setup is costing time, enquiries or confidence:
01
Someone on your team is typing data from documents all day
Invoices arrive by email, purchase orders come in as PDFs, specs get sent as attachments. Someone has to read them and type the data into your system. It's slow, it's boring, and it's full of mistakes.
02
You deal with stacks of paperwork that nobody has time to process
Delivery notes pile up, supplier invoices wait weeks to be logged, job specs sit in inboxes. The backlog grows because there aren't enough hours in the day.
03
Data entry errors are costing you money
A wrong digit on a PO, a missed line on an invoice, a spec misread. Small mistakes lead to wrong orders, payment disputes, and wasted materials.
04
You can't find information when you need it
Documents scattered across email, shared drives, and filing cabinets. When a customer rings about an order, finding the right paperwork takes longer than it should.
A clear scope, written down.
The exact mix depends on the problem, but these are the practical parts we can own:
01
Document audit
We review your document types and data flows
02
Invoice and PO extraction
AI reads invoices and purchase orders and pulls out line items, totals, and dates
03
Technical spec reading
Extract materials, dimensions, tolerances, and requirements from specs and drawings
04
Delivery note processing
Capture goods-in data automatically
05
System integration
Extracted data flows straight into your accounting, ERP, or job management system
06
Validation and review
Confidence scoring and human-in-the-loop checks for edge cases
Also included
- Searchable document archive, Every processed document indexed and searchable
- Training and handover, Your team will understand the system and trust the output
- Ongoing support, We monitor accuracy and improve the system over time
Who works on it
The Document Processing bench for this job.
You still deal directly with Chris and Kay. These named AI specialists support the research, build and checking behind the work, with human approval around client, commercial and release decisions.
Jon
Integrations & APIs
Moves approved data into the right finance, CRM or operational system.
Ruth
Risk & compliance review
Flags sensitive-data and compliance questions for human decisions.
Ethan
QA & release confidence
Tests poor scans, missing fields, unusual layouts and low-confidence cases.
A shape you can plan around.
Step 1
Audit.
We review your documents, data flows, and current pain points.
Step 2
Build.
We train the AI on your document types and connect it to your systems.
Step 3
Test.
We run real documents through the system with your team to verify accuracy.
Step 4
Deploy.
Go live with monitoring, accuracy tracking, and ongoing refinement.
Know the cost and first priorities.
Document processing projects start from £3,000 for a focused pipeline handling one document type. Complex setups covering multiple document types with system integrations typically range from £5,000–£20,000.
From £3,000
Single document type
- One pipeline (e.g. invoices, quotes, drawings, packing lists)
- Trained on your real documents, not generic samples
- Outputs straight into your CRM, Sheets, MES or ERP
- Accuracy benchmarked before go-live
From £8,000
Multi-document setup
- 2-4 document types with separate pipelines
- System integrations included
- Manual review queue for edge cases
- 90-day refinement window after launch
If your project’s smaller than this, tell us, we’ll point you to a freelancer we trust. Better that than the wrong fit.
A faster document processing enquiry
Let your AI prepare the useful first draft.
It can read this service page, research your business and ask only what it still needs to scope document processing. You approve the enquiry before anything is sent.
ChatGPT
Claude
Gemini
Perplexity
Grok
Copy it, open your AI, and paste it in.
Things people ask before starting.
01What types of documents can AI read?
Invoices, purchase orders, delivery notes, technical specifications, engineering drawings, material certs, quotations, and more. If it's a structured or semi-structured document, AI can extract data from it.
02How accurate is the data extraction?
Accuracy depends on the documents, the source data and what counts as a correct match. We test against a representative sample before promising a threshold. Every extraction includes a confidence score, and anything below the agreed threshold is flagged for a human check.
03Can it handle handwritten documents or poor scans?
Modern AI handles most scanned documents well. Handwriting recognition is improving but works best with printed or typed documents. We'll assess your specific documents during the audit and be honest about what will and won't work.
04Where does the extracted data go?
Wherever you need it, Sage, Xero, your ERP, a spreadsheet, your job management system. We connect the extraction pipeline to your existing tools so data flows automatically.
05Can it read technical drawings and engineering specs?
Yes. AI can extract data from technical drawings, CAD exports, material specifications, and engineering documents. This is especially valuable for manufacturing and fabrication businesses dealing with large volumes of specs.
06How long does it take to set up?
A focused pipeline for one document type can be live in 2–3 weeks. More complex setups covering multiple document types typically take 4–8 weeks.
07Is our data safe?
Yes. Documents are processed securely, data stays in the UK where possible, and we follow GDPR best practices. We never use your data to train third-party models.
08What happens when the AI gets something wrong?
Every extraction includes a confidence score. Low-confidence results get flagged for human review, your team checks and corrects, and the system learns from the feedback. Over time, it gets better and better.
09Can you give me a real example?
We built an invoice verification system for a manufacturing client. Supplier invoices arrive as PDFs, the AI reads every line item and automatically matches it against the original purchase order using a 3-pass matching algorithm. Discrepancies in quantity, price, or totals are flagged instantly. What used to take 15-30 minutes per invoice now takes seconds. We also built a system that reads technical cutting list PDFs and automatically generates paint notes for the powder coating supplier, extracting hundreds of profile specifications, categorising them, calculating linear metres, and flagging items that shouldn't be coated.
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