R&D to Production · Technology
Taking an AI workflow from notebook to production
A promising AI experiment was hardened into a reliable internal capability with clear contracts and safeguards.
Result: A critical workflow stabilised, and far less time lost to firefighting.
These engagements are published in anonymized form. Client names, identifying details and commercially sensitive figures are withheld at our clients’ request.
Context
A small team had built an AI workflow that genuinely worked, but only in the hands of the person who wrote it.
The problem
The workflow lived entirely in notebooks with fragile integrations, no observability, and no path to stabilise it or expose it as a dependable internal service.
What we designed
Refactored the prototype into modular services with explicit contracts, added observability and fallback logic, and defined a staged rollout from internal testing through to controlled production.
How it was implemented
Fallback behavior was designed first, so that a single model or integration misbehaving degraded the workflow instead of stopping it.
Result
Failure incidents and debugging time dropped, and the workflow began supporting real operations without blocking when one dependency failed.
Why the approach mattered
The gap between a working prototype and a dependable system is mostly error handling, contracts and observability. That work is unglamorous, and it is the entire difference.
Capabilities involved
If this resembles the situation you are in, we can set out how we would approach it and what the work would involve.