We don't specialise in an industry. We specialise in a kind of problem.

The pattern: documents arrive, a trained person reads them, pulls out what matters, checks it against rules and history, and makes or recommends a decision. Multiply by thousands a month.

Today’s models read these documents better than most people expect and worse than a good analyst on the hard cases. The engineering problem is knowing which is which: routing the routine, flagging the uncertain, and keeping a full trail of why. That is what we build.

The pattern: a large team has the same conversation, with variations, thousands of times a day. Quality is checked by listening to a sample. The best agents and the average agents produce very different results.

Voice and chat agents can now hold these conversations, and, often the larger prize, every human conversation can now be reviewed, not sampled. We build both, with the containment, escalation and monitoring an enterprise needs before it lets a machine speak to a customer.

The pattern: a second team checks the work of a first team. Because checking everything is unaffordable, they check a sample and hope the sample is representative.

AI changes the economics: it becomes possible to review every case, route the doubtful ones to a person, and produce a defensible record for the auditor. The system’s own accuracy becomes a measured, reported number, which is what a control function needs before it trusts it.

The pattern: requests arrive through tickets, email and chat; someone has to find the context in three systems, apply a policy and respond. Most of the cost is the senior person’s attention, not the task itself.

These workflows are often the fastest route to a first production system because the data is internal, the risk of a wrong answer is manageable, and the people affected are your own. We build them with the same rigour as customer-facing systems, because the second use case is usually customer-facing.

The pattern: the data exists, the decision is repeated, and the constraint is how many good analysts you have. The best of them are answering the same questions in slightly different forms every week.

We build systems that do the analyst’s first draft (pulling the data, running the models, drafting the recommendation and its reasoning) so the expert reviews and decides instead of assembling. Where the decision has real money on it, we build the evaluation and the controls before the interface.

Most of what we do fits one of the shapes above. Some of the most interesting work doesn’t. If a workflow in your organisation costs a lot of skilled hours and follows a pattern, we’d like to see it. The diagnostic exists precisely for the cases where nobody is sure yet.