How We Run AI Projects

The problem

Fee earners spend hours each week reading contracts, invoices and court bundles to pull out the same handful of fields. The work is slow, error-prone and holds up deals.

Our approach

A multi-agent pipeline for extraction, classification, validation and approval routing, built through the ADLC with full audit logging and human review for low-confidence cases.

How we measure success

• Minutes per document, before and after
• Field-level accuracy against a human-checked sample
• Share of documents sent to human review

The problem

A firm with a growing set of models has no single inventory, no risk classification and no monitoring, and needs to show regulators and the board that AI is under control.

Our approach

A model inventory, risk-based classification against the EU AI Act, model cards, bias and drift monitoring, incident procedures and board reporting templates.

How we measure success

• Share of production models documented and risk-classified
• Time to answer a regulator or auditor request
• Open governance actions per quarter

The problem

An engineering team has tried AI coding tools ad hoc but cannot tell whether they help, and worries about quality and security.

Our approach

AI pull request review, test generation, documentation pipelines and deployment risk scoring, introduced one practice at a time with a dashboard against the team’s own baseline.

How we measure success

• Pull request turnaround time
• Bugs escaping to production
• Deployment frequency