AI Transformation
Why companies bring us in.
The reasons have changed. Writing software is faster than it has ever been, which moved the hard part to deciding what to build, proving it works, and running it safely once real users arrive. These are the problems companies actually call about.
01
Getting a pilot into production
Most AI work stalls after the demo. The gap is rarely the model — it is integration with the systems that hold your data, permissions, what happens when it fails, and a cost per transaction that survives real volume.
Integration with systems of recordCost per transaction modellingFailure and fallback pathsLoad and latency testing02
Putting agents to work
Agents that do something rather than chat about it: triaging tickets, preparing quotes, reconciling records, chasing exceptions. The engineering is in what tools they may call, what they are allowed to touch, and knowing when to hand back to a person.
Tool and API accessScoped permissionsHuman handoffAn audit trail of actions takenOur own complete product · Agent platform
Codexta
Codexta is our own enterprise agent platform, doing exactly the above: tools load per task so a banking action cannot run inside a support conversation, scope is a strict allow-list rather than a blocklist, every action is written to an immutable audit log, and it hands back to a person when no guideline covers the request. Run it embedded in your product or self-hosted behind your firewall — or we build the same patterns into a system of your own.
03
Making your own data usable
Retrieval is easy to demo and hard to trust. Most of the work sits upstream of the model: what data exists, who is allowed to see it, and whether it is correct in the first place.
Retrieval pipelinesPermission-aware searchData quality and deduplicationA defensible source of truth04
Proving it works, and keeps working
You cannot unit-test a model. Quality means evals that encode what good looks like, regression when a prompt or model version changes, and monitoring for drift — because AI systems degrade quietly rather than crashing.
Eval suitesPrompt and model versioningDrift monitoringHuman review loops05
Keeping it lawful
In Europe this is now as much a compliance question as a technical one. What the system decides, on whose data, stored where, and whether you can show your work to whoever audits you next.
EU AI Act readinessGDPR and data residencyAccess controlDecision logging06
Modernising what you already run
Migrations that were uneconomic are viable again, because reading and translating an old codebase is no longer the expensive part. Deciding what deserves to survive the move still is.
Legacy assessmentIncremental migrationDocumenting undocumented systemsRunning old and new side by side07
Automating the work nobody wrote down
The processes that resisted automation were the ones living in inboxes, PDFs and spreadsheets — too unstructured for the previous generation of tools. Those are now reachable.
Document extractionEmail and ticket triageBack-office workflowsException handling08
Leaving your team able to run it
A system your team cannot operate is a dependency, not an asset. Handover is part of the work: the people who will own it should have built alongside us, and be trained for what comes after.
Runbooks and observabilityPairing and code reviewAcademy trainingA clean handover
How an engagement runs
Three phases.
01
Assess
We map the real workflows and the data behind them, then rank opportunities by payoff against effort. Some honestly come back as “not yet”.
02
Pilot
One narrow use case, built properly, with evals and a cost model — so what you get is a decision, not an impression.
03
Embed
Into the systems people already use, with the access control and audit trail that keep it there. Your team is trained to run it.
Sometimes the answer is no.
Plenty of problems are better solved by fixing a process, a form or a report than by adding a model to them. If an assessment comes back that way we will say so — it is cheaper for you and it is the reason the recommendations we do make are worth something.