Solution 01 · A model pointed at one job, with a number attached.
Language models applied to a specific task with a measured success rate, not a chatbot added to a homepage. Retrieval over your own documents, extraction from messy inputs, classification and routing, and agents that take real actions in real systems.
How it works
How an AI system we build handles one item.
Signs
If two or more of these are true, this is probably where the problem lives.
Approach
We start by finding the judgement call. A model is worth adding only where a person currently makes a decision that is repetitive and follows rules. Everything else is better served by ordinary code, and we will say so when that is the case.
Then we measure. Before anything reaches production we build a test set from your real data and score against it, so accuracy is a number you can see. That set becomes the regression test for every later change.
Finally we add a gate. Every system we ship can stop and ask a person when its confidence is low, and those corrections go back into the test set, so the same doubt costs less to resolve next time.
Deliverables
No percentages here. What a change is worth depends on your volumes and your costs, and we would rather work that out with you against your own numbers.
In practice
Also
A free 30-minute audit: we map one process with you and tell you whether it is worth doing, including when it is not.