Most organisations have an AI proof-of-concept that dazzles in a demo and then stalls. We help you find the uses actually worth building, build them properly, and keep them working once they're handling real workloads and real scrutiny.
Built to fit the tools and platforms you already run
The numbers that explain why so much AI investment never turns into something that holds up in production.
We focus on the workflows where AI genuinely saves time, cost or risk — built to slot into how each team already works.
Score and prioritise leads on real signals, draft outreach, and keep the CRM clean — so reps spend time on the deals most likely to close.
Generate and adapt content at scale, personalise campaigns by segment, and turn analytics into the next action — without losing brand voice.
Triage and resolve routine tickets, surface the right answer to agents, and escalate cleanly — with a human owning anything that matters.
Automate the manual, repetitive process work — document handling, data entry, back-office checks — that quietly eats your team's week.
Speed up the close, improve forecasting, and flag anomalies early — with explainable outputs an auditor can actually accept.
Build the pipelines, retrieval and tooling that make AI reliable in production — and the monitoring that keeps it that way.
Outcome figures are illustrative ranges typical of the work — your numbers depend on your starting point, which is exactly what we establish in the discovery phase.
A low-commitment way to start, and a defined route from there. You're never signing up for a big build on day one.
A fast, fixed-fee diagnostic of how you work. We rank where AI genuinely pays off — and tell you plainly where it doesn't.
We design the solution and how it fits your stack, data and controls — scope, sequencing and the trade-offs, agreed up front.
We build it to survive real-world edge cases and wire it into the systems and workflows your team already uses.
The part most skip. We keep what's live working — watching for drift and quiet failures — and expand once it's earned trust.
A snapshot of the kind of work we do and the results it delivers. Swap these for your own as engagements complete.
[One-line summary of the problem, what you built, and how it changed the numbers. Replace with a real engagement.]
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[One-line summary of the problem, what you built, and how it changed the numbers. Replace with a real engagement.]
Placeholder case studies — we'll replace these with real, named engagements (and references) as they complete.
Every system we build is designed from day one to survive an audit, a stakeholder review, and the passage of time — not just a demo.
Every automated decision comes with a reason a human can read and a reviewer can accept. No black boxes you can't defend.
We watch live systems for the quiet failures — model drift, data changes, degrading accuracy — and alert before they cost you.
Inputs, outputs and decisions are logged end to end, so you can always show your working when someone asks.
The judgement calls that matter stay with your people. AI does the assembly; the decision stays accountable.
Built to run on the systems and controls you already trust — cloud or on-prem — not a black box we own.
Access, data handling and decision policies are designed in from the start, not bolted on after something goes wrong.
Plenty of people can stand up an AI prototype. Fewer can get it integrated, running reliably, and still working six months later.
Direct access to the engineers doing the actual work — not a sales team that hands you off once the contract's signed.
We're invested in the outcome, not billing hours. If something doesn't need AI, we'll tell you — it's cheaper to hear now.
We design for the day the data changes and the model starts being wrong, because that day always comes.
We pick the model and stack that fit your problem and budget — not whatever we happen to resell.
Years across engineering, data and consulting. No junior bench learning on your time and your bill.
Fixed scope, clear trade-offs, no scope creep dressed up as discovery. You always know what you're getting.
[Your name] is a software engineer; [partner name] is a data engineer. Between us that's more than ten years across tech, consulting and data — the mix this work needs, since most AI projects fail on the engineering and the business side, not the AI.
You work directly with the two of us — the people doing the actual build — not a sales team that hands you off once the contract's signed. Small on purpose, and planning to stay that way.
Fill out the form and we'll get back to you within one business day. No pitch — just an honest read on where AI fits and where it doesn't.