How do we know it is not making things up?

Because we measure it on your real documents before it goes anywhere near production, and we show you the number. Anything it is unsure about goes to a person.

Does it see our private data?

It processes your data to do the job, and your data is not used to train anybody's model. We tell you exactly which provider handles what before you commit to anything.

What does it cost to run every month?

We give you the figure during the pilot, based on your real volume, before you decide whether to go further.

Give AI one useful job.

Practical AI

AI becomes useful when the job, accuracy, failure path, and cost are clear. We start with one repeated task and prove it against real work before scaling it.

This is one of the things a Custom Software build can include, so it is scoped and priced there rather than on its own. See Custom Software and what it costs

What is breaking

Your staff spend hours reading documents just to pull out a few numbers
Someone sorts through inbound WhatsApp and email by hand every morning
You tried ChatGPT, it worked once, and then it did not
Every AI thing you have been shown is a toy with no job to do

What changes for you

Key information is extracted from documents for a person to verify
Inbound messages are sorted and routed to the right owner
First drafts are prepared for staff to review and send
Accuracy is measured on your own examples
Uncertain cases are sent to a person for a decision

Ideal for

Teams handling high volumes of semi-structured input, documents, emails, applications, claims, where the rules are too fuzzy for pure automation but too repetitive for senior staff.

Might not be the right fit

Teams looking for a chatbot with no clear job to do. AI without a job to do is a science project, not a system.

How we go from pilot to production

Pilot

We pick one high-volume task. We run AI against a sample of real data and measure accuracy before we commit to a build.

Evaluate

We design an eval suite specific to your data. You see the failure cases before we scale.

Build

The build usually takes 4 to 8 weeks, with logging, fallback, and a human review path for edge cases.

Tune

Cost, latency, accuracy. We tune the model choice and prompts as your data and volume evolve.

FAQ

Because we measure it on your real documents before it goes anywhere near production, and we show you the number. Anything it is unsure about goes to a person.
It processes your data to do the job, and your data is not used to train anybody's model. We tell you exactly which provider handles what before you commit to anything.
We give you the figure during the pilot, based on your real volume, before you decide whether to go further.

Which of these sounds like your business?

Same work, different companies. Open the one you recognise and see what it takes to remove it.

Which part of this is your problem?

Two or three sentences is enough. We will tell you what the first useful phase looks like before you commit to a build.

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