Production AI your team can run, built on your data.

Companies we've worked with

Established companies across fintech, HR tech, and travel have put our machine learning into production.

What went wrong last time, and what happens here

The complaints below are the ones we hear on first calls and read in the practitioner forums, in the words people use. Beside each one is how we work so it does not happen again.

• The last project

  The demo was impressive. Eighteen months later it is still a demo.

  This one A proof of concept that ends in a decision

  Four weeks, fixed price, and a written go or no-go with the measurements behind it. No-go is a real outcome, it has happened, and the client kept the report.

  How the first six weeks run
• The last project

  Nobody wrote down what it was supposed to return, so nobody can say whether it did.

  This one A baseline and a threshold before the first line of code

  We write down what the process does today, what number would count as success, and who signs it off. The system is then scored against that, before anyone draws a conclusion.

  92% of fraud blocked at a 0.7% false-positive rate, measured
• The last project

  It works about 80% of the time, so nobody trusts it, and once it made something up in front of a customer.

  This one Measured before it is trusted, and given less room

  Every build has an evaluation set of known inputs and expected outputs. We cut the system's autonomy until the variance left is small enough to accept, with a human sign-off on anything irreversible.

  How we make agents reliable
• The last project

  The proof of concept ran on hand-picked rows. Production met the real warehouse, and the budget doubled.

  This one The data is counted before the price is quoted

  The written scope names the tables, the labelled examples and who owns access. If cleaning the data is most of the job, the quote says so, and milestones are tied to the state of the data rather than to dates.

  Data engineering and AI readiness
• The last project

  The consultants left. Six months on, nobody in the building can change what they built.

  This one Your team in the room from week one

  Either your engineers build it with weekly architecture reviews, or we build it and hand over the code, the documentation and the retraining procedure, with training for the people who will run it.

  Fractional head of AI
• The last project

  The API bill grew with every user, and the data went somewhere the compliance team could not see.

  This one Your cloud, your hardware, your bill

  Deployed in your account, a private cloud or on your own servers, on a sovereign model you own. A fixed capability gets cheaper every year, a per-token frontier model does not, and nothing is trained on your data for anyone else.

  90x lower latency on the client's own CPU servers

The questions we put to every proposal, including our own, are written up as a checklist you can use before funding anything. Ten questions to ask before you fund an AI pilot

Where are you right now?

Four ways this usually starts. Pick the one that sounds like your week.

• We think this could work, but we need proof before we can get budget approval.

  A fixed-price proof of concept in 3 to 4 weeks, built on our infrastructure so your IT security queue is not on the critical path. It ends in a go or no-go with the measurements behind it.

  Validation phase

• We want you as the brain of the project, doing architecture and guiding our team.

  Weekly architecture and review sessions. Your engineers build it, we are accountable for the technical calls, and the knowledge stays with you.

  Fractional CDO and advisory

• The timeline has to work with the budget. We need something tangible with clear milestones.

  Two-week milestones with a working demo at each one. Deployed on AWS or GCP, with private VPC and on-premise where the data has to stay put.

  Development and implementation

• The board has approved an AI budget and we cannot say in one sentence what it is for.

  A written view on what to fund, defer, or stop, and what each would have to return. Due diligence on a programme, an acquisition, or a vendor demo.

  Strategic AI and data advisory

Why we build rather than configure

Three things a bespoke system does that a subscription cannot. Each one is measured on a client system and written up in full.

• <300ms Fast enough to sit inside the transaction

  A model tuned to one job runs in the request, not after it. The decision engine scores over 100,000 credit decisions a day inside 300 milliseconds.

  Real-time decision engine
• £3.2M Trained on your data, not the average

  Fraud patterns are specific to a business. A model built on one client's own transaction history prevented £3.2M of losses a year, while flagging 0.7% of legitimate transactions.

  Enterprise fraud detection
• Yours The model, the data and the bill stay with you

  Deployed on your cloud, your private VPC or your own hardware. No third party holds the data. A fixed capability gets 5 to 10 times cheaper a year; running the frontier model of the day gets 3 to 18 times dearer.

  AI security and deployment

What we build on

We work in the modern data and AI stack rather than a single vendor's version of it. AWS is the one formal partnership we hold; Google Cloud is our preferred platform and everything else here is a tool we use daily.

Naming a tool here means we have shipped production systems with it, not that we resell it. AWS Partner is a formal status; the rest are the things we reach for, and we will use yours where you already have one.

Before you book the call

Six questions we are asked before a first call, answered the way we answer them on it.