Mandate 02
AI Transformation
From the first workshop to production systems that hold up under load, governance and audit. We build the cases that pay for themselves, and we say so plainly when a case does not.
The honest version
Most AI pilots never reach production. The model is rarely the reason.
They stall because nobody defined, before the build started, the accuracy at which the system would actually remove the manual step rather than add a review step on top of it. A pilot that hits 85 percent when the process needs 97 percent is not a promising start. It is a more expensive version of the old process, and it quietly dies in a steering committee six months later. So we set that threshold first, in writing, with the people who own the process. Then we decide whether to build.
How it runs
Workshop, proof, rollout, scale.
- 01
Workshop
Two days with the people who own the process. We map candidate use cases against value, data readiness and the accuracy threshold each one would have to clear. Most leave with fewer candidates than they arrived with, which is the point.
- 02
Proof of concept
The strongest candidate, built against real held out data, measured against the threshold. A clear go or no go, with the number in front of you.
- 03
Rollout
Production engineering: integration into the systems that hold the work, monitoring, fallback paths, audit trail, and the change management around the people whose job it alters.
- 04
Scale
The next use cases run faster because the platform, the data access patterns and the governance already exist.
Where it has paid off
Use cases we have taken into production.
- Customer service routing and triage
- Document extraction and data entry
- Contract and payment automation
- Dynamic pricing
- Compliance and risk screening
- Content generation at scale
- Marketing and lead generation
- Automated project reporting
- HR help desk
- Enhanced analytics and forecasting
- Business system integration
- New AI based revenue streams
Governance
Built to survive an audit.
European companies do not get to treat AI as an experiment on the side. Anything touching personal data, pricing or customer communication needs a documented basis, a retention position, an audit trail and a human escalation path. We build those in from the first production release rather than retrofitting them when legal asks, because retrofitting them is how a working system gets switched off.
Start with six weeks.
A discovery gives you a target architecture, a costed and sequenced plan, and a recommendation you can defend to your board. If the answer is that you should not run the programme, you will get that answer too.
Or write to hi@colayer.io