AI Implementation
We build the opportunity you decided to pursue — integrated with the systems you already run, tested against real work rather than happy-path examples, and handed over with documentation your team can maintain.
Getting an AI feature to work once, on a clean example, in a notebook, is the easy part. The hard part is everything after: the edge cases, the integration with the software your team already lives in, the moment the model returns something confidently wrong, and the question of who fixes it when the customer is waiting.
Most stalled AI projects are stuck exactly there. The demo worked, and then nobody could make it survive contact with real operations. It did not have an owner, a review step, a test set, a recovery path, or a clear answer to what happens when the input is messy.
Our implementation work starts with the operating job, not the model. We define the task, place the human review where it belongs, connect the surrounding tools, and make sure the system has enough logging and documentation to be owned after launch.
Use cases
The right build is usually close to work your team already does. These examples show the kind of production systems we scope after the use case is clear.
What you get
Deployed and running against real work, not a proof of concept.
It works inside the tools your team already uses, rather than adding another tab.
Real cases with known-good answers, so you can tell whether a change made it better or worse.
Clear points where a person reviews, corrects, or overrides — sized to the cost of being wrong.
Visibility into what it is doing and alerting when behaviour drifts.
Written so your team can maintain it without us.
Deliverables
You own it. No lock-in to us.
Readiness
Process
We agree exactly what it does, what it does not, and how we will know it works — in writing, before building.
Working software in short increments, visible to you throughout. No reveal at the end.
Tested against real cases from your business, including the awkward ones.
Into production with monitoring in place, and a period of watching it closely.
Documentation, training, and a clear answer to who maintains it.
Timeline
Implementation time depends on the task, the systems involved, the review rules, and how much testing the risk requires. A small internal assistant may be measured in days or a few weeks. A workflow that touches customer communication, financial records, or several systems needs more care. We do not set a date until the written scope is clear.
Pricing
Builds are fixed-quote against a written scope agreed before work begins. Scope changes are re-quoted rather than absorbed silently.
Not sure this is the right starting point? The free AI readiness scorecard takes a few minutes, and an AI opportunity assessment is the longer version of the same question: what is worth building first.
Plenty of builds turn out to be mostly plumbing rather than models. When that is the case we say so and scope it as AI workflow automation instead.
Questions
Next step
Bring the use case. We will tell you on the first call whether it is ready for a build or needs more definition.