Document Analysis

Last quarter, a finance team spent 160 hours manually entering supplier invoices into their ERP.

Not because they couldn't afford software. Because their existing OCR tools kept misreading line items, mixing up GL codes, and requiring constant human verification.

The problem wasn't the volume of documents. It was that traditional OCR can't reason about what it's reading.

What Document Analysis Actually Does

We turn unstructured documents into structured data you can act on.

PDFs, Word docs, scanned images, handwritten forms - the system extracts what matters and organises it into clean JSON or markdown. No manual data entry. No transcription errors. No 40-hour work weeks spent on administrative tasks.

The difference is intelligence. Multi-modal LLMs don't just recognise text. They understand context, infer relationships, and handle variations in formatting without breaking.

How It Works

The architecture combines Gemini 3.5 for visual document processing with Claude 3.5 Sonnet for precise extraction and reasoning.

The pipeline:

1. Document ingestion - Upload via API, email, or file drop
2. Visual processing - Gemini analyses document structure and layout
3. Intelligent extraction - Claude extracts fields, validates data, and applies business logic
4. Structured output - Returns JSON with high-confidence extractions and flagged exceptions
5. Human review - Only edge cases route to human verification

The system runs on serverless compute, which means it scales automatically during month-end processing spikes without paying for idle infrastructure.

We use RAG (Retrieval Augmented Generation) to inject context. The LLM sees your historical data patterns - previous GL code mappings, vendor formats, approval hierarchies - so extraction accuracy improves over time.

Real-World Results

A mid-market professional services firm was processing 400-500 supplier invoices per month. Their AP team spent two full days each week on data entry alone.

We built a document analysis pipeline that:

• Extracts invoice metadata (vendor, date, amount, line items)
• Matches GL codes based on historical patterns and item descriptions
• Routes exceptions (new vendors, unusual amounts) to human review
• Auto-posts approved invoices directly to their ERP

The outcome:

• 95% automation rate
• Manual effort reduced from 16 hours/week to 2 hours/week
• Processing time cut from 3-5 days to same-day
• Error rate dropped from 8% to under 1%

The team now focuses on exception handling and vendor relationship management instead of keyboard work.

What Makes This Different

Speed without sacrificing accuracy

Traditional OCR tools optimise for speed. We optimise for correctness. The system achieves 98%+ extraction accuracy on complex documents because it can reason about what it's reading.

Works with messy real-world documents

Invoices with hand-written notes. Scanned contracts with coffee stains. Engineering drawings with mixed text and diagrams. The multi-modal approach handles variability that breaks template-based systems.

You only pay for what you process

Serverless architecture means no minimum monthly fees and no infrastructure overhead. Process 50 documents one month and 5,000 the next - the cost scales linearly.

Your data stays yours

Processing happens in your cloud tenant. Documents never leave your security boundary. All code and infrastructure belong to you from day one.

Common Use Cases

Invoice and receipt processing Extract line items, amounts, tax, and vendor details. Map to GL codes automatically.

Contract analysis Pull key terms, obligations, and renewal dates from legal agreements. Flag non-standard clauses.

Medical records digitisation Convert handwritten patient forms and historical records into structured EMR data.

Technical specification extraction Parse engineering drawings, parts lists, and technical documentation into searchable databases.

Insurance claims processing Extract claim details, supporting documents, and policy information for automated routing.

Technical Stack

• Gemini 3.5 - High-throughput visual document processing
• Claude 3.5 Sonnet - Precise text extraction and logical reasoning
• Serverless compute - Auto-scaling for variable workloads
• RAG - Context-aware extraction using historical patterns

What You Get

A production-ready system that integrates with your existing workflows. The output is structured JSON you can pipe directly into your ERP, CRM, or database.

Exception handling routes edge cases to human review with confidence scores and highlighted fields. Your team verifies ambiguous extractions - the system handles everything else.

All infrastructure lives in your cloud tenant. No vendor lock-in. No ongoing platform fees. Complete control over your data and deployment.

Getting Started

Document analysis works best when there's repetitive manual processing that follows predictable patterns.

If your team spends more than 10 hours per week transcribing documents, you're likely spending $30k-50k annually on work that can be automated.

Schedule a discovery call to discuss your specific document processing needs. We'll map your current workflow, identify automation opportunities, and provide a clear cost-benefit analysis before any development begins.