Nobody buys AI. They buy twenty minutes back.

The useful question isn't whether a model is involved. It's which task in your week takes twenty minutes and happens two hundred times a month, and whether software can take most of it.

In practice that means document work: reading a PDF and pulling twelve fields out of it, deciding which of four hundred applications a person should look at first, finding the one clause in a contract archive that answers a question.

We build these as ordinary software features with a model inside, not as chatbots bolted onto the side. They live in the system your team already uses, and they show their working.

Where it pays off

Document extraction

OCR plus structured output from scans and PDFs, with a confidence score per field and a human reviewing only what's uncertain.

Review triage

Rank a queue so the cases that need judgement reach a person first, and the routine ones move on their own.

Search that understands

Semantic search across your own documents and records, with citations back to the source.

Classification & routing

Get the right item to the right team without someone reading every one first.

Drafting assistance

First drafts of routine correspondence, produced inside the workflow with your templates and data.

Data cleanup

Normalising years of inconsistent records at a scale that isn't worth doing by hand.

How we build it

Every AI feature we ship has four things around it: an evaluation set so we can tell whether a change made it better, a confidence threshold, a fallback path when the model is unsure, and a human step for anything consequential.

We start with the cheapest model that clears the bar and only move up if the evaluation says so. Cost per document matters when you're processing forty thousand a year.

Drop an architecture diagram

When we'd tell you not to

If the volume is low — under a few hundred documents a month, a person is cheaper and better. If the inputs are wildly inconsistent, fix the intake first. If the process itself is unclear, automating it just makes the confusion faster. And if being wrong once is unacceptable, the human stays in the loop and the savings are smaller than you'd hope.

Questions we get asked

Do you train models on our data?

No. We use commercial APIs with training disabled, or self-hosted models where the data can't leave your infrastructure. Which one is a decision we make with you in week one.

What happens when it gets something wrong?

Low-confidence results go to a review queue rather than through. Every extraction keeps a link to the source page so a person can check it in seconds.

How long until we see something working?

A working prototype on your real documents inside three weeks. That's usually enough to tell whether the full build is worth it.

What does it cost to run?

Document extraction typically lands between $0.02 and $0.10 per page depending on length and model. We report it per document so you can see it.

Got a process that eats hours every week?

Tell us what it is. We'll tell you whether AI is the right tool — including when it isn't.

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