Not a pilot project that impresses the board and then quietly stops. Specific processes, measurable before and after.
Most AI projects fail the same way: a tool is chosen first and a use case is found for it afterwards. We work in the opposite direction — find the processes where time is actually being spent, then establish whether current tooling helps.
Sometimes the answer is that it does not, and we say so. A well-written rule or a fixed data-entry form beats a language model for a great many tasks, and costs nothing to run.
Assessment through to adoption.
Identifying where time goes and which parts are genuinely automatable.
Reading, classifying and extracting from invoices, forms and correspondence.
Handling repeat questions, with a clear escalation path to a person.
Getting your data into a state where any of this is possible — usually the real work.
Choosing between business-tier cloud services and self-hosted models on data-handling grounds.
Connecting to the systems you already run, rather than adding another silo.
Practical training on what these tools do well, what they do badly, and how to check output.
Usage policy, data handling rules and an approval path — before a problem, not after.
An agreed before-and-after metric so the result is a number, not an impression.
Consistently in high-volume, repetitive, language-heavy work: reading and routing documents, drafting standard correspondence, answering repeat support questions, and summarising long material. Where judgement or accountability matters, it assists rather than replaces.
It depends entirely on the deployment. Data sent to a consumer service is not under your control. We design around business-tier services with contractual data handling, or self-hosted models where the data genuinely cannot leave, and we say clearly which applies.
For most business applications, no. What you need is someone who understands your processes and someone who understands the tooling. Bespoke model training is a different and much larger undertaking.
We agree the measure before starting — handling time, error rate, volume processed per person. If the number does not move, the project has not worked, and that should be visible rather than argued about.
A short assessment tells you which of your processes are worth automating — and which are not.