Most enterprise AI fails after the demo

The demo is the easy part

Every large organisation now has a working AI demo. Almost none has an AI system that a business unit depends on, that internal audit has signed off, and that keeps running when the person who built it goes on holiday.

The gap between those two states is where the money goes and where most programmes stall. It is not a modelling gap. It is an integration, governance, evaluation and ownership gap, and it is wider inside a bank, a hospital or a utility than anywhere else.

Start from the workflow, not the technology

We don’t arrive with a platform and look for somewhere to put it. We start by finding the workflows that consume the most skilled hours (the reading, checking, extracting, comparing, calling and deciding that fills the day of thousands of people) and ask what each one looks like redesigned around a machine that can read, reason and act.

Some of those workflows should be automated end to end. Some should be augmented so one person does the work of four. Some should be left alone. The diagnostic exists to tell the three apart before anyone writes code.

Prove it on your data, with a number you can defend

A prototype without an evaluation harness is a demo. Before we take anything to production we build the test set, define what “good” means with the people who do the work today, and measure the system against it. That number goes in front of the steering committee, and it is what we are accountable to.

Build it to be owned

Every system we deploy is built to be run by your team: documented, monitored, governed, with humans in the loop wherever the cost of a wrong answer is high. Our forward-deployed engineers work inside your environment until it holds, then step back.

And build it twice

Each engagement leaves behind a system you own and a set of reusable parts we keep: evaluation tooling, guardrails, connectors, an agent runtime that has already survived one enterprise security review. That is how your second workflow costs less than your first, and how a consulting company becomes a software company.

Not an industry. A kind of problem.

Our founders have run technology, strategy and data inside banks, insurers and investment firms, and that depth shows in how quickly we can work in regulated environments. But the problems we solve, from document-heavy operations and customer conversations at scale to quality and control, internal operations and decision support, look the same from inside a hospital, a telco or a manufacturer. If it costs a lot of skilled hours and follows a pattern, we want to see it.

Diagnostic (2 to 3 weeks)

We sit with the people who do the work. Not the transformation office: the underwriters, the agents, the analysts, the engineers on the IT intake queue. We map what they actually do, time it, and price it.

Then we score every candidate workflow on three axes: how much it costs today, how feasible it is with current models on your data, and what happens when the system is wrong. The output is a ranked map, one chosen workflow, and a business case written for the person who signs.

You can take that map and act on it without us. Some clients do.

Prototype (3 to 4 weeks)

We build the system on your real data, inside your environment or a sandbox that mirrors it. In parallel we build the thing most teams skip: an evaluation harness. We define “good” with the people who do the job today, assemble a test set, and measure.

At the end there is a number, a demo the business owner can drive themselves, and a clear go / no-go. If it doesn’t clear the bar, we say so and you have lost four weeks, not a year.

Deployment (6 to 10 weeks)

This is where enterprise AI succeeds or fails, and it is where we put our senior people. Forward-deployed engineers work inside your environment on integration, identity, permissions, logging, monitoring, human-in-the-loop controls and the security review. Deployment strategists work with the business on rollout, training and the change in how the work is done.

We deploy where your controls need it: your cloud, our managed environment, or on-premise.

Handover & accelerator (ongoing)

Your team runs the system. We leave runbooks, dashboards, an evaluation suite you can rerun as models change, and named people who know how it was built.

We keep the parts that will be useful again (connectors, guardrails, evaluation tooling, the agent runtime) and bring them to your next workflow at a fraction of the first one’s cost. That is the deal: you get a system; we get software.

What we ask of you

One accountable business owner. Access to the people who do the work. Real data, under your controls, from week one. A decision-maker who will attend three meetings: the workflow choice, the go / no-go, and the production sign-off.