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Proof Points
Proof, Not Promises.
Every engagement below solved a real, specific problem for a real organisation, not a theoretical one.
Risk & Compliance Control Frameworks
Closing a Multi-Million Dollar Remediation Gap
Major Insurer
A significant gap between approved product changes and what was actually live in the system was costing millions in customer remediation. We built the workflow and control that closed it.
Key Result:
50% of all remediations addressed at root cause, for an ~$125k build.
Regulatory Response & Evidence Management
Zero Solution, One Month to a Regulatory Deadline
Major Retail Bank
With no solution in place and weeks left before a new regulatory deadline, other suppliers said it couldn't be done in time. We built and shipped it anyway.
Key Result:
Delivered in under a month; later retained as the bank's preferred long-term solution.
Customer Complaint & Case Management
Teaching GenAI to Read Thousands of Pages of Regulation
Large Banking Institution
High-volume, highly regulated complaint handling was slow and manual. We built a GenAI system that checks every complaint against thousands of pages of regulation, automatically.
Key Result:
Faster, consistent, audit-ready responses, with a human-in-the-loop retained.
Regulatory Response & Evidence Management
Industrialising Years of Regulatory Paper Trail
National Bank
Up to 80 regulatory information requests a year, some spanning 7+ years of records, were being handled manually with no central system. We industrialised the entire process.
Key Result:
A working MVP delivered in 2 months, built to handle concurrent requests.
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The Testing Fallacy: Why GenAI Readiness Stalls
Most organisations approaching GenAI deployment assume their biggest challenge is the model. It is not. The bottleneck is the test data, and the three most common approaches to solving it each carry a fundamental flaw that only becomes visible after deployment.
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The Lab-to-Production Gap: Why GenAI Agents Fail When It Matters Most
A GenAI agent that performs well in controlled evaluation and fails in production is not a model failure. It is a data failure. The gap between the structured, predictable datasets used in lab environments and the messy, ambiguous reality of real customer communication is where most GenAI initiatives quietly break down.
This piece sets out the architecture of that gap, why the standard approaches to closing it are insufficient, and the question every organisation should be asking before their GenAI system goes live in a high-accountability environment.
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The Complexity of GenAI Test Data
The highest-value GenAI solutions in regulated B2C enterprises are the ones that process unstructured PII data - customer communications, complaints, case notes, voice recordings. That is precisely where the AI testing problem is most acute.
Real-world unstructured customer data cannot legally or practically be used to build and test these solutions. Masking and anonymisation address only part of the problem. The data you need either does not exist at sufficient scale, cannot be found across fragmented systems, or cannot be used at all. This piece makes the case — through three specific contentions — for why regulated enterprises must be able to generate synthetic-but-realistic unstructured PII data on demand, and what is at stake if they cannot.
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Testing AI Agents: The Reliability Problem That Cannot Be Deferred
A December 2025 paper on Measuring Agents in Production surveyed GenAI projects across industries and reached two conclusions that should concern any organisation deploying AI in high-accountability environments: determining the reliability of AI agents remains unsolved, and generating the gold-standard unstructured test data needed to measure that reliability is described as "nearly infeasible."
This video sets out a direct response to both findings. Agent reliability must be solved — the operational and regulatory exposure of deploying agents whose reliability cannot be demonstrated is not a sustainable position. And while generating gold-standard unstructured test data is genuinely difficult, it is not impossible. This piece explains what a rigorous approach looks like, and why the distinction between "nearly infeasible" and "not possible" matters significantly for organisations under compliance pressure.
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