Practical AI · Built in Melbourne
Document Processing that actually reads
Turn 500 PDFs into structured data without the data-entry team. Built for Australian businesses where accuracy and audit trail aren't optional.
Why most document workflows stay manual
The volume isn't the problem. The variability is. Off-the-shelf OCR catches the easy fields and surfaces nothing on the rest. Here's what Australian SMEs tell us they keep running into.
Hours lost to manual data entry.
Forty-hour weeks where two admin staff retype fields from PDFs that nobody can confidently parse with a script.
Inconsistent extraction with off-the-shelf tools.
Generic OCR hits 70% on clean documents and falls off a cliff on scans, handwriting, or layout drift. The 30% gap is where errors live.
Compliance risk from messy paper-to-digital workflows.
When extraction is uneven, audits surface gaps you didn't know existed — and the people who'd notice are the ones whose time you can't get.
How we build it
Document processing pipelines look easy from the outside — PDF in, structured data out. The work lives in the middle. We design a multi-stage pipeline using LangGraph to orchestrate extraction, classification, and validation steps as discrete nodes. Claude API does the heavy reading — it handles variable layouts, mixed scan quality, and table reconstruction better than rule-based extractors. We layer custom validators that score each field's confidence and route low-confidence extractions to a human reviewer before downstream systems see them. Outputs land in your existing systems — Salesforce, HubSpot, or a custom database — through dedicated mapping functions, not generic connectors. The full pipeline runs on Vercel infrastructure or in your own AWS or Azure tenancy if data residency demands it. Every document carries an audit trail: who saw it, what was extracted, what got revised, and when.
Tools we lean on: LangGraph · Claude API · PyMuPDF · SurrealDB · FastAPI · Salesforce · Vercel
Pipeline shape · Document Processing
- 01Intake
- 02Classify
- 03Extract
- 04Validate
- 05Sync
What the pipeline does, end to end
Six capabilities every document pipeline we ship includes by default.
Multi-format intake.
PDFs, scanned images, Word documents, Excel spreadsheets — and email attachments routed straight from Outlook or Gmail. The pipeline doesn't care about the wrapper.
Field-level confidence scoring.
Every extracted field comes with a 0-100 confidence score. Below threshold, the document routes to human review before it touches your downstream systems.
Human-in-loop review queue.
A simple web UI for your team to approve, correct, or reject low-confidence extractions. Two clicks per field. Designed for non-technical reviewers.
Custom taxonomy mapping.
Your fields, your category names, your codes. We map the pipeline output to your schema — not a generic vendor schema you have to translate later.
Integration with your existing systems.
Salesforce, SharePoint, HubSpot, custom databases, Xero, MYOB — pick what you already run. We build a dedicated mapping function rather than duct-taping a generic connector.
Audit trail and compliance logs.
Every document, every extraction, every revision logged with timestamp and operator. Exportable for audits. Designed with Australian Privacy Act and sector-specific compliance in mind.
Packages start at three sizes
Most clients land on Scale. We re-quote against your actual document volume after the audit.
Automate
From $2,000 AUD
Single document type. One extraction stage. Output to spreadsheet or one downstream system.
- Multi-format intake
- Up to 1 document type
- Confidence scoring
- Email-based review
Scale
From $5,000 AUD
Multi-document, multi-stage pipeline. Custom taxonomy mapping. Integration with one core system.
- Up to 5 document types
- Custom taxonomy
- 1 system integration
- Human-in-loop review queue
- Quarterly accuracy tuning
Transform
From $10,000 AUD
Full enterprise pipeline. Multiple integrations. Audit trails and compliance logs.
- Unlimited document types
- Multi-system integration
- Compliance logging
- Dedicated review UI
- Ongoing pipeline maintenance
Real-world scenario · 2025
500 PDFs, 40 hours, no overtime
A Victorian government department asked us to take a manual document-review process — 500+ technical building reports per quarter, handled by two reviewers on a 40-hour cycle — and turn it into a pipeline that did the same work in two hours, with at least 95% accuracy on the structured fields.
We built a 7-stage LangGraph pipeline. Claude handled extraction across 143+ fields per document. SurrealDB held the audit trail. The output landed in two Salesforce objects — Building and Item — through dedicated mapping functions. Low-confidence extractions routed to a human review queue; the reviewers retained authority over every record before it synced.
The team's time shifted from data entry to judgment work — flagging edge cases, reviewing low-confidence calls, supervising the pipeline. The pipeline runs on a recurring schedule now. The two reviewers still work the process — they review three documents per hour instead of typing one.
Read the full case studyPDFs processed per quarter
Hours of reviewer time per cycle
Field-level accuracy on structured data
Questions clients ask before they book the call
How accurate is this really?
Field-level accuracy on the structured fields lands at 95%+ for clean documents and stays above 90% on most scans we've seen. We measure it directly. Every project includes a one-week calibration period where the validators get tuned against your actual document corpus, not a generic benchmark. If a particular field type can't reach 90%, we tell you upfront — and we route it to human review by default.
What if my documents are handwritten or scanned poorly?
Handwriting is the hardest case. Modern vision-language models — Claude included — handle clear handwriting reliably and degrade gracefully on harder samples. We always pre-process scans (deskew, denoise, contrast adjustment) before extraction. For documents we can't reach a confidence threshold on, the pipeline holds them in the human review queue rather than guessing. Better to flag than to inject a wrong value into your downstream systems.
Where does my data live? Australia, or overseas?
Your choice. The default deployment runs on Vercel infrastructure in Sydney — data stays in Australia. If you need stricter sovereignty, we deploy the pipeline to your own AWS or Azure tenancy in your region. The Claude API call is the one external dependency: Anthropic offers Claude through AWS Bedrock in the Sydney region, which we use for clients with explicit data residency requirements. We document the data flow upfront so your privacy officer doesn't have surprises.
Can you integrate with our system?
Almost certainly. We've integrated with Salesforce, HubSpot, SharePoint, Xero, MYOB, custom-built databases, and a long tail of internal systems. The integration approach is always a dedicated mapping function — we don't rely on generic connectors that break the moment your schema drifts. If your system has an API or a database we can read, we can integrate. The discovery call covers this in the first 15 minutes.
How long does a typical project take?
Document Processing projects on the Scale tier ship in 4-6 weeks from kickoff. Automate-tier projects ship in 2-3 weeks. Transform-tier projects (full enterprise pipelines, multiple integrations, compliance logging) typically run 8-12 weeks. The first two weeks are always discovery + sample document analysis — nobody writes pipeline code until the document corpus is understood.
Free 30-min audit · No prep required
See if your processes qualify.
Book a free 30-minute audit. We'll walk through your document workflows, name the highest-value candidates, and tell you whether AI is the right fit — or not.