Automating the work that repeats, and AI features inside your product that earn their place.
Two halves of the same discipline. First, automation aimed at the work that repeats — intake, classification, routing, summarising, reporting — where the task takes real hours and has a clear right answer, with the judgement calls left to your team. Second, AI as a feature of your product rather than a badge on it: assistants that answer from your own content, search that understands intent, extraction that turns documents into structured data. We are equally willing to tell you when a problem does not need AI.
What happens after you say yes, in order. Every step produces something you can see rather than a status update.
We look for tasks that repeat, consume skilled attention, and produce a predictable kind of output. That is where automation pays back. Projects that start from a decision to use AI and then hunt for somewhere to put it are the ones that quietly fail.
How long the task takes today, honestly, and how often it happens. Without that number there is no way to tell afterwards whether the thing worked — and "it feels faster" is not a result you can take to a board.
If the rules can be written down, we write them down. A conditional is cheaper to run, impossible to misinterpret and needs no monitoring. A surprising share of what gets proposed as AI is a form and three rules wearing a costume, and we will say so.
One task, end to end, with human review on the output. This is where the return actually is: you get the speed without owning the failure mode. Widening comes after it has proved itself, not before.
Tested on your actual documents and messages, not a demo set. We measure how often it is right, what it costs per run, and what happens at the edges — because a system that is 90% right on clean inputs and 40% right on real ones is worse than none.
Quality drifts as inputs change and nothing warns you. We set up monitoring, a fallback for when a provider is unavailable or changes underneath you, and cost controls. Then we agree who owns it — an automation nobody is responsible for degrades quietly until people learn to work around it.
We choose per project rather than fitting every problem to the same tools. These are what we work across most.
Your data stays where it is. We build around the systems that already hold it rather than asking you to move it somewhere new, and where information cannot leave your environment we can run models inside it. If you already use a particular provider we work with that; if you have no preference we will choose on capability and cost for your task, and tell you what the trade-off is.
Projects where ai automation was part of the engagement.
Tell us what you are trying to build and we will scope it with you.