Service

AI Services

Applied AI and automation on the work you actually do — document processing, support triage, internal search — with a clear-eyed view of where it works and where it will embarrass you.

The unglamorous version of AI

There is a lot of noise. Underneath it, a small number of things genuinely work right now, and they are mostly boring: reading documents, sorting messages, drafting text a human then approves, and finding things in a pile of files nobody has been able to search since 2019.

That is where we start, because that is where the return is.

What we build

Document processing. Invoices, forms, contracts, applications — extracted into structured data, with the confidence score visible and anything uncertain sent to a person.

Support triage. Inbound email and tickets classified, routed, and given a drafted first reply that a human sends or discards.

Retrieval over your own knowledge. Ask a question, get an answer with a citation into the actual source document. If it cannot cite it, it says it does not know.

Integration. Into the systems you already run, so it is not another tab nobody opens.

How we keep it honest

Every AI feature we ship gets an evaluation set — real examples, with known right answers — and we measure against it before launch and after every change. Anything with a real consequence gets a human in the loop by design, not as a temporary safety measure that quietly gets removed.

If, after we look at your process, we think AI is the wrong tool and a boring script would do it more reliably for a tenth of the price — that is what we will tell you. It happens more often than the industry likes to admit.

Frequently asked

Where does AI actually pay off?

High-volume, low-stakes, language-shaped work. Sorting and routing inbound email. Extracting fields from invoices and forms. Drafting first-pass replies. Search across documents nobody can find. Anywhere a human is doing the same shallow judgement two hundred times a week.

Where does it not?

Anywhere a confident wrong answer is expensive and nobody is checking. If you cannot afford it to be wrong 3% of the time and you are not putting a human in the loop, do not automate it — we will say so.

Will our data be used to train someone's model?

Not if we build it correctly. We use providers and configurations that contractually exclude your data from training, and for genuinely sensitive work we can run models on infrastructure we control.

How do you stop it making things up?

Ground it in your actual documents and make it cite them, constrain what it is allowed to answer, and measure it against a real test set before it goes anywhere near a customer. An AI feature with no evaluation is a demo, not a product.

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