The London AI agency that builds, not just advises

An AI agency designs, builds and runs AI systems for businesses: workflow automations, AI agents and bespoke applications. Where a traditional software agency starts with a spec, an AI agency starts with a problem, usually a manual process that eats staff time. At Tom&Co we map your processes, score the opportunities by payback, then build the automations that remove the busywork. We've deployed over 120 workflows, and most first projects are live within four weeks.

A consultancy advises; an agency builds. Plenty of consultancies will hand you a strategy deck and leave you to implement it. We do the advisory work (our AI Opportunity Audit and Discovery sessions) but always with a build at the end of it. In our experience the strategy only gets sharp once something is live and real users are testing it, which is why we push to a working pilot within weeks rather than months. If the advisory side is what you need first, our AI consultancy starts with a fixed-scope audit and ends in working software.

The published evidence is mixed and we'd rather you knew that upfront. Across UK organisations, only around 6% report an enterprise-level EBIT impact of 5% or more from AI, and roughly 60% see no measurable enterprise impact at all. The difference is almost never the technology. It's choosing the wrong use case. That's why we start every engagement with an audit and only recommend building where we can see a clear payback. Across our deployed projects the average payback time is 3 to 4 months. If we can't find a use case that washes its face, we'll tell you.

More than most people expect. Data entry and re-keying between systems, reporting, translation, billing, scheduling, document digitisation and product photography are all things we've automated for clients. The test is simple: if a task is repeatable and currently needs a person to click through it, it's a candidate. Real examples: a 75% staff time saving on job creation for a specialist services firm, five new international websites launched off the back of automated translation, and a 90% reduction in product photography costs for a fashion brand.

Our first pilots typically go live in under four weeks. We deliberately scope the first build as a tight MVP so your team sees value quickly, then extend from there. Larger agentic systems with multiple integrations take longer, but we always structure the programme around an early win.

We scope and price after Discovery, once we know exactly what needs building. The structure is designed to keep your risk low: a short audit conversation costs nothing, the Discovery day is a fixed fee, and the pilot build is a defined scope with a defined price. Because we prioritise by payback, most clients recover the cost of their first project within 3 to 4 months.

An AI agent is software that pursues a goal on your behalf: it plans, uses tools, checks its own results and adjusts until the job is done. Agents are powerful but they aren't always the right answer. For many processes a simpler AI workflow, where each step runs in a fixed order, is cheaper and more predictable. We build both and will recommend whichever fits. If you want the detail, our guide to agentic AI vs generative AI covers it in plain English.

No. Most of our clients don't have a technical team. We handle the architecture, the models, the integrations and the ongoing maintenance. What we need from you is knowledge of your own processes: the people who actually do the work, in the room, telling us where the time goes. Some of our best projects have come from operations teams who knew exactly what was broken but not how to fix it.

Whichever fits the task. We work with Claude, GPT and Gemini, and with open-source models where data privacy or cost makes them the better choice. Model choice is an engineering decision we make per workflow, not a vendor allegiance, and we'll re-evaluate as models improve because they change fast.

We build with permissions, guardrails and human sign-off steps from the start, and we design around UK GDPR. Where data is sensitive we can keep processing within your own cloud environment or use models that don't train on your inputs. Every agent we deploy operates within defined parameters, with an audit trail of what it did and why.

No. Retail is one of the sectors we work in, but the AI work spans much wider: specialist services firms, creative agencies, professional services and trade suppliers are all in our case studies. Repetitive manual process looks remarkably similar across industries, and the automations that fix it transfer well.

Gartner expects over 40% of agentic AI projects to be cancelled by the end of 2027, and most failures share a cause: nobody mapped the process before building, or the use case was chosen for novelty rather than payback. Our answer is boring on purpose. Map first, score by payback, build a small pilot, measure it, then extend. If you've tried AI before and it didn't stick, that history is useful, and we'd want to hear it in the audit.