An international AI research centre, shipping production systems.

IT Researches was established as a small computer-research practice working at the intersection of academia and enterprise IT. The early work was unglamorous: knowledge-based systems for utilities, optimisation models for logistics, statistical forecasters for retail. The pattern was set early — pick the simplest model that beats the baseline, ship it into a workflow that actually uses it, and stay long enough to retrain it when the world changes.

Over the years we have grown into a multidisciplinary team across research, engineering and consultancy. Our work today spans artificial intelligence software, big-data engineering, computer vision, predictive analytics, sentiment analysis, knowledge systems and the bespoke applied research that does not fit a category. The scale has changed; the principles have not.

We work primarily out of the United Kingdom, with practitioners and partners across Europe, the Middle East, Africa and Asia. Most of our delivery is hybrid — a small core team on site with the client and a larger group working remotely against shared environments — and we have invested in the operating discipline that makes hybrid delivery work.

Vendor-neutrality is one of those phrases that almost every advisory firm claims and almost none lives. We mean it structurally. We do not resell software. We do not earn margin on hardware. We have no commercial agreements that would cause us to recommend one platform over another for reasons unrelated to your problem.

In practice this means that when we run a vendor-selection exercise, the criteria are written before the longlist; that when we recommend buying instead of building, we are reducing the size of the engagement we could otherwise have proposed; and that when we recommend stopping a project because the data does not justify continuing, we are recommending against our own commercial interest. Clients tell us this discipline is itself a deliverable.

It also means we are happy to deliver into your existing investments rather than displacing them. Most of our engagements extend an existing data platform, an existing CRM, an existing planning tool, an existing model library — because that is almost always the right answer.

“Independence is a structure, not a sentiment. If our economics depended on the answer, we could not be trusted to give it.”

Every engagement begins with a written hypothesis: the decision the work is meant to improve, the metric that will be used to judge it, the data that will be used to learn from, and the criteria that will trigger a stop. The hypothesis is revisited every fortnight and rewritten when the evidence demands it. This single discipline has rescued more projects than any single technical decision we have ever made.

We work in mixed teams in which our practitioners and yours share the same standup, the same code base and the same on-call rotation. Knowledge concentration is treated as an operational risk; ownership transfer to your team is part of every plan. The intent — held lightly but seriously — is that you can run the system without us when the engagement ends.

Documentation is treated as a deliverable, not as an afterthought. Every model carries a model card; every pipeline carries a runbook; every decision worth defending is captured in a written architectural note. Three years later, when the original team has moved on and a regulator wants to know why the system behaves the way it does, the trail is intact.

Our client base spans manufacturing, retail and consumer goods, financial services, telecommunications, healthcare and life sciences, energy and utilities, logistics and supply chain, the public sector, and professional services. The common thread is not the industry — it is the problem shape. We do our best work where there is a recurring decision, made at scale, where better information would meaningfully improve the outcome and where data exists or can be made to exist.

Within each industry we have a set of recurring engagements: defect detection and predictive maintenance in manufacturing; demand and assortment work in retail; risk, fraud and document automation in financial services; clinical operations and imaging support in healthcare; capacity, churn and operations in telecommunications; routing, scheduling and inventory in logistics; case management and document workflows in the public sector. The list is illustrative, not exhaustive.

We are equally happy contributing a focused workstream to an existing programme as owning an end-to-end delivery. The shape of the engagement matters less than the clarity of the outcome we are aiming at.

We are headquartered in the United Kingdom and most of our clients are headquartered in Europe, the Middle East, Africa or the Indian Ocean rim. Engagements regularly span multiple time zones, and our delivery model — small on-site cores supported by larger remote teams — has been refined over many years to make that work without the friction it usually introduces.

We carry the engineering culture you would expect from a British research firm: careful, evidence-driven, conservative about claims, generous about credit, honest about doubts. Clients tell us this is the part of the experience that distinguishes us most reliably from larger firms.

You should expect to be told, in writing, what we believe is achievable, what it will cost, how long it will take, and what evidence will be used to judge it. You should expect to receive that assessment promptly, in plain language and with the workings shown. You should expect us to recommend against our own engagement when the case for it is weak, and you should expect us to push back — politely, persistently — when we believe a different course is in your interest.

You should expect the engineers and researchers you meet to be the engineers and researchers you work with. You should expect a small core team that grows or shrinks with the work, not a pyramid that bills three layers above the people doing the work. You should expect that the system you receive at the end of the engagement is one you can operate, modify and outgrow.

Above all, you should expect the conversation to be useful from the very first call.