AI-driven research, fully evidenced.
We advise on AI in regulated environments. We hold our own delivery to the same standard.
Our research team was keen to build AI into our client work. But they found the leading research platforms failed on two fronts.
Each was a black box: ask a question, get an answer, with no way to trace how it was reached - unsustainable when explainability is the point.
Worse, they produced low-quality output, frequently misreporting what research participants had said, leaving our experts to correct analyses by hand.
So we built our own.
Introducing Fleming.
Fleming is Nile's in-house AI research and delivery platform - secure, hosted in our own EU cloud environment, and built with explainability at the core. It reads and makes sense of an entire project corpus at once - survey responses, transcripts, documents, internal notes - surfacing patterns no single interview would reveal, with every finding traceable back to a specific source: who said it, where, and when.
That traceability is why our findings hold up in front of an executive committee, and it's how we deliver strategy-grade research in weeks rather than months.
Quality that’s measured, not assumed.
Fleming runs continuous evaluation. An automated judge grades every answer the platform produces for accuracy, grounding in source material, and staying in scope - and the judge itself is validated against our human experts using industry-standard methods.
The bar is set by our Head of Research, who has spent the past year holding Fleming to the same standard as her team. The judge reaches near-perfect agreement with our experts (a Cohen's kappa of 0.811), and in every test to date it has never approved an answer they would have rejected. Where it errs, it errs on the side of caution.
Regulated work demands self-righting infrastructure, because models keep changing. When we upgraded Fleming's underlying model, the platform re-tested itself, found agreement had drifted, and recalibrated until it passed.
Judgement stays human.
Our researchers trust Fleming to do the groundwork - it lets them go deeper, more quickly, to a higher standard. But our experts make the final call, every time.
For one client, an Authorised Corporate Director, we ran a conversational survey across the whole business rather than a sample - 93% of colleagues took part, each response validated against Fleming's knowledge of the business - and the client had the evidence base to proceed with transformation within weeks. The judgement about what that evidence meant remained with people.
The same discipline runs through everything internal. Workbench, our second platform, puts Nile's organisational knowledge in every colleague's hands inside an environment we control - because advising clients on where AI should and shouldn't sit starts with living under the same constraints ourselves.
Why this should matter to you.
We don't ask clients to adopt practices we haven't already imposed on ourselves. If we can't explain it to a regulator, it doesn't ship. That principle shapes what we build for ourselves and what we design for clients.
When we say a diagnostic can run in four weeks, or that an evaluation harness can tell you whether an AI output is right, it's because we've built and calibrated exactly that - and we can show you the workings.