From raw data to AI in production.
From deciding where AI earns its place, to building it properly, to helping your team run it once we step back.
One stack, four layers.
Most of what we do sits somewhere on this stack: a reliable data foundation at the base, decision-making on top of it, AI built into the workflow, and the adoption that makes any of it matter. Select a layer.
AI enablement & adoption
The hard part of AI is rarely the model; it is whether anyone uses it. We help you find where AI genuinely fits, redesign the workflow around it, and bring your people with it, so the work survives the launch. Strategy that ends in something shipped, not a slide pack.
- Leadership has made AI a priority, but pilots stall before they reach production.
- There is appetite and budget, but no clear view of where AI actually earns its place.
- Something has been built, but the team has not adopted it and the workflow has not changed.
Our team has taken AI tools from proof of concept into daily use, by designing the rollout and the workflow around them, not just the model.
These examples reflect work our founders have led across their careers.
AI products & automation
We build AI systems with a clear job to do: connecting siloed information, replacing manual processes, and making existing workflows faster and more reliable. We also know when AI is not the right answer. That happens more often than most people expect.
- A manual process is consuming hours of skilled time every week and could be automated.
- A ChatGPT proof of concept needs to become a reliable, production-grade system.
- The team needs help separating what is feasible from what is marketing before committing to a build.
Our team built a generative AI tool for an international research organisation, connecting thousands of R&D projects so researchers find relevant work in seconds rather than days.
Analytics & data science
Analytics is only useful when it changes a decision someone was about to make. We build dashboards people actually open, models that predict what happens next, and experiments that prove what works rather than what feels right.
- Leadership makes decisions on gut feeling because the reporting does not tell them anything new.
- There is a hypothesis worth testing but no framework for running a proper experiment.
- Existing ML models were built as a proof of concept and need to be rebuilt for production.
Our team designed and ran A/B pricing trials for a global airline: £5m immediate revenue uplift, projected £75m–£100m annual impact, calibrated across 40m+ passenger journeys.
Data platforms & engineering
We design and build cloud data platforms that bring scattered data into one place, model it properly, and make it available to the people and systems that need it. Everything we deliver is designed so your team can run it without us.
- Data spread across disconnected systems with no single source of truth.
- A legacy data warehouse that is expensive, brittle, or poorly understood.
- Analysts spending more time cleaning data than analysing it.
Our team built a cloud data platform for a major NHS integrated care board: Snowflake-based, 25+ clinical and operational dashboards, serving over 3 million patients.
Your data as a product.
Most companies treat their data as a cost. If you sit between suppliers and customers, it can be a product: the sales, stock, and performance insight your suppliers would pay to see. We design, build, and run that product for you: a free insight tier to every supplier as the front door and paid tiers behind it, delivered on the platform work described above. It is where an engagement can end up, not where it starts. The honest route in is the two-week diagnostic, which tells you whether your data could earn its keep before anyone builds anything.
It is Partner-stage work, the 5,895m summit of the trail, reached one honest step at a time.
Start with a diagnostic →Every engagement starts at base camp.
We do not ask you to sign a six-month contract before we understand the problem. We understand it, design the approach, and get your sign-off before we build anything.
Base camp
A short, no-obligation conversation about what you are trying to do and whether we are the right fit. No charge.
Start at base camp →Discover
A fixed two-week diagnostic, £4,500. You get a scored, honest view of where your data and AI actually stand, a prioritised roadmap including whether your data could earn its keep, and the financial case for each recommendation, including the ones where the right answer is not to build. If you go on to a build with us, the fee comes off your first invoice. If we are not the right team for what comes next, we will say so.
Start with a diagnostic →Build
Once the design is signed off, we build it. The founders review every deliverable. You get a working system, documentation, and a handover.
Scope a build →Partner
A monthly allocation of senior time for maintenance, iteration, and new work. Reviewed quarterly. If it stops making sense, we will say so.
Talk about ongoing work →Not every engagement follows the same path. Some teams need a proof of concept before a full build. Some start with a data platform and come back for AI on top of it. We design the engagement around what you need.
The diagnostic
The questions worth asking.
The diagnostic maps what your data can already answer, and what it would take to answer the rest. Questions like these tell us where to look:
Which parts of the business look busy but lose money?
Why it matters
Busy and profitable are different claims, and only one of them is in your reports. The other is in your data.
Which parts of the business look busy but lose money?
Why it matters
Busy and profitable are different claims, and only one of them is in your reports. The other is in your data.
What work exists only because two systems do not talk to each other?
Why it matters
Nobody chose that work; it accumulated. Naming it is usually the fastest route to hours back.
Which decisions are being made too late to change the outcome, and what would it take to make them in time?
Why it matters
A report that arrives after the decision is decoration. Most reporting fails quietly on timing, not accuracy, and the fix is rarely more dashboards.
Which five-minute tasks add up to full-time salaries?
Why it matters
Nobody audits a five-minute task. At volume it is a salary, and volume is exactly what systems are for.
Which customers show the early signs of leaving, and which are worth the effort to keep?
Why it matters
By the time a customer tells you they are leaving, the data has usually been saying it for months. The gap between those two moments is where retention work actually pays.
What insight do you give away for free today that could be a paid tier tomorrow?
Why it matters
If suppliers or partners already act on numbers you send them, you have a product; it is just unpriced. Naming it is the first step towards charging for it.
How we decide what to build.
Every recommendation we make has to pass one piece of arithmetic: what the build costs in hours against what it returns in hours or money. If a system takes fifty hours to build and saves three hundred a year, we will tell you to build it. If it takes thousands of hours to save a few dozen, we will tell you not to, whoever is asking. We estimate before we start, we log what it actually took, and we show you both numbers. That is the whole method.
Insight has to reach someone.
Most analytics does not fail on the analysis. It fails because nobody owns the insight and it never reaches the person who can act on it. Dashboards that sit unopened are not a data problem; they are a distribution problem. So we build reporting that arrives: pushed to the right person, shaped for their role, in time to change the day. And before we take analytics work we ask two questions: who owns this inside your business, and how does the answer reach the people who act? If neither has an answer, we will say so before you spend anything.
A few things we hold to.
The people you meet are the people who do the work.
The founders lead every engagement and review every deliverable before it reaches you.
We would rather say no than overcommit.
If something is outside our expertise or the timeline is unrealistic, we will tell you.
Your team runs it after we leave.
Everything we build comes with documentation and a proper handover.
No surprises on the invoice.
We agree how pricing works before anything starts. If the scope changes, we have the conversation first.
Show us the problem. We will give you the honest answer.
No pitch, no pressure. Just a conversation about what you need and whether we can help.
Talk through your use case