Practical AI, proven on one small pilot before you scale.
Most AI projects fail because the tool arrives before the problem is defined. We work the other way round: find the one workflow costing you time, and prove the fix on a pilot small enough to walk away from.
Ten questions, about three minutes. No email needed to see your score.
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Define
Name the bottleneck and the number it moves.
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Pilot
Build the smallest thing that could move it.
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Measure
Compare against the baseline we took first.
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Decide
Scale it, adjust it, or stop.
Two lanes we know well
Customer service
First replies drafted from your own past answers, approved by a person before they send.
- Draft-and-approve, not an unsupervised bot
- Your accounts, your keys, minimum data
Lead response
New enquiries acknowledged straight away, sorted by fit, and routed to a person.
- Follow-up stops depending on memory
- Scoring you can read and argue with
Problem first, tool second
Sometimes the answer isn't AI. We'll say so before you spend anything.
A person approves
AI drafts. Someone on your team checks it before a customer sees it.
Small and measured
One workflow, a fixed price, and a number agreed before we start.
Find out where you'd fall over — before you spend anything
Ten plain questions, built from the reasons AI projects actually fail. You get a readiness score and the biggest risk in your way. The score is yours whether or not you talk to us.
Start the scorecard →- Problem definition
- Is there a measurable bottleneck to aim at?
- Data & process
- Is the work written down and the data reachable?
- People & oversight
- Who owns it, and who catches a wrong answer?
- Scope & expectations
- Is the first project small enough?
Why we insist on starting small
Both figures below are somebody else's published research, linked so you can check it. Neither is our result — this practice is new.
Cause #1
of AI project failure is miscommunication about the problem to solve — the business misstates the problem, so the system optimises the wrong thing. RAND interviewed 65 experienced data scientists and ML engineers, and reports that more than 80% of AI projects fail.
95%
of organisations deploying generative AI saw no measurable P&L return. The 5% that did ran narrow, workflow-specific tools rather than broad general-purpose rollouts.
Questions worth asking
Do you have case studies?
Not yet — this is a new practice, and inventing them isn't an option. Ask on a call what we'd do with your bottleneck; that tells you more than a case study anyway.
How much does a pilot cost?
It depends on the workflow, so we scope it per engagement. You get a real number on the first call and a fixed price before any work starts.
Will this replace my staff?
No. We automate the repetitive part — drafting, copying, chasing — and your people keep making the decisions. If the goal is cutting headcount, we are the wrong firm.
What happens to our data?
It stays in accounts you own, and a workflow only gets the fields it actually needs. We'll tell you plainly which third-party services are involved and what they see.
Bring one bottleneck. Leave with a straight answer.
A 20-minute call, no deck. We'll say whether AI is the right tool for it — including when it isn't.
Not ready to talk? Take the scorecard first — no email needed to see your score.