AI at Nile.
We advise on AI in regulated environments, so we built and governed our own first. Our own discovery platform, our own assistant platform, both held to a standard we can show you. It is also why our teams are smaller and our answers arrive sooner.
WHY WE BUILT ITOff-the-shelf AI failed us on two fronts
Our research team wanted AI in our client work, so they evaluated the leading platforms. Each was a black box: ask a question, get an answer, with no way to trace how it was reached. That is unsustainable when explainability is the point.
Worse, they produced low-quality output, frequently misreporting what research participants had said, and left our experts correcting analyses by hand.
So we built our own.
Fleming: AI that shows its working
Fleming is our in-house AI discovery platform. It runs discovery on a single project or across a whole estate, reading survey responses, transcripts, documents and internal notes together and surfacing patterns no single interview would reveal. Every finding traces 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. It is also how we get to strategy-grade insight in days rather than months.
It runs on infrastructure we control, within a managed cloud environment with EU-based data processing, so client material can be used by AI without breaching a data processing agreement. Audited and explainable, not a black box.
Quality that’s measured, not assumed
A platform that reads your whole business is only as good as its worst answer, so we grade every one. Fleming runs continuous evaluation: an automated judge scores each answer for accuracy, for grounding in source material, and for staying in scope. The judge itself is validated against our human experts using industry-standard methods.
The bar is set by Dr Alexa Haynes, our Head of Research, who has spent the past year holding Fleming to the same standard as her team.
And because models keep changing, regulated work needs infrastructure that rights itself. When we upgraded Fleming's underlying model, the platform re-tested itself, found that agreement had drifted, and recalibrated until it passed.
0.811
Cohen's kappa between automated judge and our human experts - near-perfect agreement
1.000
AI Judge precision. It errs towards rejecting good answers, not approving bad ones
93%
Response rate in a client’s whole-business survey, enabled by Fleming
THE LINE WE HOLDJudgement stays human
Our researchers trust Fleming with the groundwork. It lets them go deeper and faster, and it lets more junior researchers produce work closer to the standard of our PhD-led team, meaning we can field smaller teams who can get to the insights much quicker.
The final call is a person's, every time.
For 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 a complete evidence base to proceed with transformation inside weeks. What that evidence meant was decided by people.
WORKBENCHAnd the same discipline in how we run
Workbench is our AI assistant platform, built in-house by our Innovation and Enablement team and used across the business every day: drafting proposals and statements of work, synthesising research, project reporting, operational admin.
It brings frontier language models together with a growing library of purpose-built assistants and live connections into the systems we already run the business on, so the capability sits inside real workflows rather than in a separate place nobody opens twice.
It follows the same order we use on client work. Start with the business outcome and the human experience of it, then choose the technology that delivers it, rather than the other way round. Build capability our own team can sustain and extend, rather than a dependency on a vendor. Treat governance, data privacy and explainability as design requirements, not as things bolted on at the end.
The latest iteration introduces modular, reusable skills, so good practice gets written down once and applied consistently everywhere. That is the same capability-building approach we recommend to clients standing up their own AI functions.
Why this matters to you
We are operators, not commentators.
We have a point of view and we have published it. Our GenAI Strategy Playbook sets out the five layers we work through with leadership teams, from the outcomes a business must actually produce to what it knows that a competitor with public data and off-the-shelf AI cannot replicate. We have honed it with industry partners.
We built our own tools. We changed how we work around them. And we keep reviewing and rebuilding our own operating model, because the models keep changing and so does what is worth doing with them.
So 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 is because we have built and calibrated exactly that. We can show you the workings.