Outcome ownership for technology projects
We own the outcome.
We agree the problem and the success criteria with you, work out what the solution actually has to be, build it, and measure it against the standard we set together. Then we hand it over and step back.
Why 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.
Leadership expects
- A business question answered
- A decision they can defend
- A number they trust
the gap
Engineering delivers
- Pipelines and services that run clean
- A platform, a model, an integration
- Infrastructure that works
Nobody wrote down what success looks like, so both sides are right and the project goes quiet. We close that gap first, then own what gets built across it.
42%
of enterprises abandoned most of their AI initiatives during 2025, against 17% the year before. S&P Global Market Intelligence1
21% → 95%
accuracy on Anthropic's own analytics agent, on their internal evals, after adding a skills layer. Same model, same data. Anthropic2
How the work happens
Five steps, in order.
Each one depends on the one before it. You cannot execute against a standard nobody has written down, and you cannot prove a result you never instrumented.
- Define the problem and the success criteria, together. Not a brief handed across a table. We write down what the problem actually is and what "working" will mean in numbers, before anything gets built. Most projects fail here and find out six months later.
- Work out what the solution needs to be. Sometimes that is a week of investigation. 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.
- Where the solution is already clear, we execute. No discovery theatre for its own sake. When the path is defined, we build against it and own the delivery rather than handing you a recommendation and leaving.
- Build the measurement, then validate against it. We instrument the outcome, check it holds to the standard we agreed, and only then onboard your team and phase ourselves out. The exit is a passing measurement, not an expiring contract.
- Find who else it works for. A result that helps one team and stops there has under-delivered. We identify where the same pattern applies elsewhere in your organisation and make sure those people know it exists.
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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.
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The contract
Every engagement opens with success criteria in writing. What we will achieve, by when, and how we will both know.
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The ownership
We own the outcome, not just the advice. Where execution is the right answer, we execute. Where your team should hold the work, we get them there and leave.
What we take on
Technology projects where the outcome is contested.
Data and AI is where we are deepest, but the failure pattern is not specific to it. If the argument in the room is about what "done" means, the work fits.
- An AI or agent initiative stuck between a convincing demo and production
- A platform that answers technical questions correctly and business questions badly
- A measurement problem: the thing shipped, and nobody can say whether it is working
- A migration or rebuild where the definition of done keeps moving
- A team or operating model that cannot ship the thing it was assembled to ship
- A project that has been deprioritised twice and is still on the roadmap
Fit
Who this works for.
A good fit
- There is a specific outcome you can name and have not hit
- You have a team, and the gap is not simply headcount
- Someone senior will commit to what success means
- Funded or profitable, growth-stage through large
Not a fit
- You want bodies against a backlog
- Nobody will put success criteria in writing
- The outcome is already being delivered well
- You need a vendor to carry the blame
Track record
Built and run, not advised from outside.
Context engineering · data infrastructure company
A context-engineering product taken from zero to customers
Found the problem worth solving and narrowed it, made the assumptions explicit, iterated directly with customers, and built the team infrastructure that kept it improving after launch. Ran the LLM experimentation loops behind it, and changed how the internal analytics team understood its own work in an AI-first setting.
Production agentic system · live, own capital
An options decision engine trading real money daily
A deterministic rules engine over five brokerage accounts: position reconciliation from broker statements, cost-basis gating that accounts for premium already collected, roll thresholds driven by remaining extrinsic value, and an earnings filter that suppresses entries inside a blackout window. It produces a daily plan, flags what breaks its own rules, and has been running unattended on a schedule. The stakes are real money, which is a stricter reviewer than a staging environment.
Sources
- S&P Global Market Intelligence, Voice of the Enterprise: AI & Machine Learning, 2025 enterprise AI survey. spglobal.com
- Anthropic, “How Anthropic enables self-service data analytics with Claude,” June 2026. claude.com