AI-Driven Contract Redaction for a Transport & Infrastructure Organisation

Hudson and Hayes worked with a large transport and infrastructure organisation that was dealing with a new regulatory requirement.

Any contract worth more than £5 million now had to be redacted before being published.

On paper, that sounds straightforward. In practice, it wasn’t.

The organisation had already tested several tools, but none of them really worked at scale.

Some were AI-based, but still left metadata behind.

Others only handled basic PII and couldn’t cope with the organisation’s very specific redaction rules.

Manual tools gave more control, but were slow and impractical for large documents.

Most contracts were 500–1,000 pages long, and redacting a single document could take up to four hours. That simply wasn’t sustainable.

They needed something that was:

• Accurate and compliant
• Flexible enough to handle complex, custom redactions
• Fast enough to be used in real operations

How we approached it

We started by slowing things down before speeding them up.

Rather than jumping straight into a build, we ran a short design sprint with the client. The goal was to properly understand the problem before touching any code.

Together, we:

• Clarified what “good” actually looked like
• Agreed what success would be measured on
• Tested early ideas against real contracts

Within one to two weeks, we had a working prototype that stakeholders could use and react to. That early feedback shaped everything that followed.

Once the direction was clear, we designed the target architecture and built the solution in short, practical sprints, staying close to the client throughout.

The platform was developed using:

• React on the front end
• Python on the back end
• Azure Foundry as the platform
• Large Language Models to handle intelligent, context-aware redaction

Regular demos and feedback meant the tool improved quickly and stayed grounded in real-world use, not assumptions.

What changed

The solution was deployed inside the client’s environment and immediately made a difference.

Redaction time dropped from hours to minutes — typically around five minutes per contract.

At the same time, the organisation:

• Met its regulatory obligations with confidence
• Freed up significant internal time and effort
• Built momentum for further AI work across the business

What started as a compliance problem ended up becoming a practical example of how AI could genuinely improve how work gets done.

Why this mattered

This wasn’t about “adding AI” for the sake of it.

It worked because the solution was:

• Designed around real constraints
• Built with the people who would actually use it
• Focused on speed, accuracy, and trust

And that’s what turned a frustrating, manual process into something simple, fast, and repeatable.