We own the outcome.

Why AI and data projects stall

Most failures are definition failures.

Two groups work hard, in good faith, on different understandings of the same request. Neither is wrong, and nobody notices until the delivery date arrives.

Nobody wrote down what success looks like, so both sides are right and the project goes quiet. It happens often enough to show up in surveys: in S&P Global’s 2025 enterprise survey, 42% of companies had abandoned most of their AI initiatives before production, up from 17% a year earlier.

The usual fix is written-down knowledge. When Anthropic gave its own analytics agent a set of plain markdown notes on how its analysts answer questions, accuracy on its evals went from 21% to consistently above 95%. We close that gap first, then own what gets built across it.

How the work happens

Five steps, in order.

Each one depends on the one before it. You cannot build against a standard nobody has written down, and you cannot prove a result you never measured.

1. Agree the business objective and the success criteria. We write down what the problem actually is and what "working" will mean in numbers, before anything gets built. Most projects skip this and find out six months later.
2. Validate what actually needs to be built. Sometimes that takes a week of looking at the data and the workflow. Sometimes the finding is that the thing you asked for is not the thing you need, which is cheaper to learn now than after a quarter of engineering.
3. Deliver it. When the path is clear, we build: the pipeline, the agent, the evaluation, the semantic layer. We stay on it until it runs in production.
4. Measure against the targets we agreed. We instrument the outcome and check it holds to the standard from step one, segment by segment, so an average cannot hide the cases that matter.
5. Hand it to your team, then look for where else it applies. Your engineers learn to run and extend it, and we step back. The exit is a passing measurement and a team that owns the loop, not an expiring contract.

• The unit Committed capacity, held for you and planned around. Not a timesheet, and not a fixed-scope statement of work that stops being true in month two.
• The contract Every engagement opens with success criteria in writing. What we will achieve, by when, and how we will both know.
• The ownership Where building is the right answer, we build it. Where your team can build it and needs a second opinion on direction, we work as advisors.

What we take on

AI systems and data pipelines.

This is where our experience is, so it is the only work we take on. Delivery or advisory, depending on what the problem needs.

• An AI agent or LLM feature that demos well and is not trusted in production
• Data pipelines that run every night and still produce numbers nobody believes
• An analytics or semantic layer that answers technical questions and fumbles business ones
• Evaluation: the system shipped, and nobody can say whether it is getting answers right
• Standing up or reshaping a data and AI team, from first hires to operating model

Fit

Who this works for.

Track record

Built and run, not advised from outside.