About our Artificial Intelligence practice

True Ai Insights started in 2021 when two machine-learning engineers got tired of watching promising AI prototypes die on the vine. They had spent years at large consultancies building proof-of-concept models that impressed boardrooms but never reached production. The pattern was always the same: a flashy demo, a round of applause, then months of stalling as nobody knew how to integrate the model into day-to-day operations.

So they left and founded a company with a single rule: nothing ships unless it runs in the client's own environment, on real data, with real users. That rule still governs every project we take on.

Founders discussing a project plan at a whiteboard

Our mission

We want to make AI ordinary. Not in the sense of boring, but in the sense of reliable: something a warehouse manager or a finance analyst uses without thinking about it, the way they use a spreadsheet today. The technology should disappear into the workflow.

That means we spend as much time on data pipelines, monitoring dashboards and user training as we do on the models themselves. A model with 95 % accuracy is worthless if nobody trusts it, and trust comes from transparency. Every system we deliver includes clear explanations of why it made a given prediction, written in language the end user actually understands.

We measure success by adoption rate, not by benchmark scores. If the people who are supposed to use the tool are still doing things the old way three months after launch, we have failed, regardless of how elegant the algorithm is.

What we stand for

  1. Transparency over polish

    We share our working assumptions, our error rates and the limitations of every model. If a simpler rule-based approach would serve you better than machine learning, we say so, even though it means a smaller contract for us.

  2. Ownership belongs to you

    Code, trained weights, documentation: it is all yours from day one. We do not create vendor lock-in by keeping source code on our servers. You can walk away at any point and still have a working system.

  3. Small teams, direct access

    Projects are staffed with two to four people. The person who writes the code is the person who presents the results. There is no layer of project managers translating between you and the engineers.

  4. Responsible data handling

    We follow UK GDPR requirements strictly, anonymise training data wherever possible and run bias audits on classification models before deployment. We have turned down projects where the data collection method was ethically questionable.

The people behind the work

Our core team is based in Upper Jastdon, England. We also collaborate with a small network of specialist contractors for niche domains like medical imaging and geospatial analysis.

Portrait of Daniel Hargreaves, co-founder

Daniel Hargreaves

Co-founder and lead engineer. Previously at a FTSE 100 insurer building fraud-detection pipelines. Holds an MSc in Statistical Learning from the University of Edinburgh.

Portrait of Priya Menon, co-founder

Priya Menon

Co-founder and data architect. Spent five years at a logistics company designing real-time demand-forecasting systems. Holds a PhD in Computer Science from Imperial College London.

Portrait of Marcus Oyelaran, senior data scientist

Marcus Oyelaran

Senior data scientist specialising in natural language processing. Built the document-intelligence pipeline that handles over 60,000 invoices a month for one of our clients.

Get in touch

We are always happy to have a conversation about whether AI fits your situation.

Email: [email protected]

Phone: +44 331 278 7293

Address: United Kingdom, England, Upper Jastdon, OC3 3AM, 2 Cherry Tree Close