Hours to under a minute:
AI-powered property document
processing at scale.
Thousands of property legal documents. A manual review process that couldn't keep up. We automated it end-to-end — and benchmarked every decision along the way.
| Client | A Property Tech business |
| Sector | Property Tech / Document AI |
| Service | End-to-End AI Pipeline Development |
| Volume | Thousands of property legal documents |
| Outcome | Hours of manual review → under one minute per document |
| Status | Live — fully automated, distributed processing |
Key Highlights
Property transactions generate paperwork. A lot of it. For the property tech business behind this engagement, thousands of property documents were moving through a manual review process — each one requiring a trained professional to read, extract key information, and produce structured outputs. It was slow, expensive, and didn't scale.
The documents themselves were the hard part. Property legal PDFs are not clean — they contain dense legal language, spatially complex layouts, nested sections, and tables that don't behave like tables in a spreadsheet. Off-the-shelf document AI tools weren't cutting it. The nuance of property law terminology meant generic NER models missed too much.
The engagement started with a decision: build in-house, or buy from a cloud provider? We didn't assume either answer. We built, benchmarked, and let the data decide.
Ingest. Build. Scale.
Taming complex legal PDFs.
The pipeline begins with PDF ingestion — but property legal documents are spatially complex, with nested sections, irregular tables, and formatting that defeats naive text extraction. We built a bespoke ingestion layer covering spatial layout analysis, table detection, and text restructuring — converting chaotic PDF layouts into clean, processable document representations before any AI model touches them.
Domain-specific models, built and benchmarked in-house.
We built NER and summarisation models in-house, trained specifically on the vocabulary and structure of property legal documents. Then we benchmarked them honestly against AWS and Azure AI services — not to confirm our own work was better, but to inform the right build-vs-buy decision for the client with actual data. For domain-specific tasks, our models matched or exceeded the third-party services.
Distributed processing — thousands of documents, under a minute each.
The production system uses distributed processing to handle document volumes that would take weeks of manual review. Each document is ingested, analysed, extracted, and structured in under a minute — automatically, without any manual handoff. Serverless AWS Lambda containers handle inference at scale, keeping costs efficient as volumes grow.
What changed.
| Before Ferrous Labs | After Ferrous Labs |
|---|---|
| Manual document review — hours per document, per trained professional | Fully automated pipeline — each document processed in under a minute |
| Process didn't scale with document volumes | Distributed architecture scales to thousands of documents without manual intervention |
| Generic AI tools failed on domain-specific legal language | In-house models matched or exceeded third-party services on domain-specific tasks |
| Build-vs-buy decisions made on assumption, not data | Reusable document AI architecture applicable across legal document types |
We didn't assume third-party AI was the answer. We built in-house, benchmarked honestly, and let the data decide. That rigour is what clients deserve — and it's how you build something that actually holds up.Ferrous Labs engineering note
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