AI Automation

AI that does the work, not just the demo

Practical AI automation built into the systems you already run — document processing, intelligent workflows, and the parts of a job that never needed a person.

Discuss your AI project

What we build

AI automation for one specific problem at a time

Most businesses do not need an AI strategy

They need one specific problem solved. Documents read and categorised without somebody opening each one. Enquiries triaged before they reach a person. A search that understands what was meant rather than only what was typed.

Those are narrow problems with measurable answers, and they are where AI currently earns its money. The large ambitious version — an assistant that does everything, trained on everything — is where budgets go to die.

What we have actually built

An AI-driven job search that matches candidates to roles on meaning rather than keyword overlap. Automated workflows where AI handles the judgement step that previously stopped the process and waited for a person. And AI built into project and workflow management, so a business running four disconnected tools has one system that understands what is happening across all of them.

That last one is the pattern we see most. The problem is rarely a shortage of software — it is that none of it talks to the rest, and the person holding it together is doing so from memory.

How we choose the approach

Not every problem wants a language model. Classification, extraction, optical character recognition and plain rules all have their place, and they are usually cheaper, faster and more predictable than a model that reasons.

Where a language model is right, we use whichever fits the requirement, the cost profile and the data-handling constraints — OpenAI, Anthropic, or an open-source model running on your own infrastructure.

And when the answer is no

We will tell you if AI is the wrong tool. For a good number of automation problems, a well-written script is more reliable, cheaper to run and easier to debug — and recommending that costs us a project and earns us the next three.

The AI projects that pay for themselves are small and specific, not large and ambitious.
DIGIDMN

See what is included

40to 70%

Typical time saved on the manual task being replaced

4to 12 weeks

Typical delivery for an AI automation project

1

Working prototype before the production build starts

100%

Fixed price — scoped before the build begins

1

Problem definition

What is manual now, what AI will take over, and how we will know whether it worked.

2

Approach selection

Language model, classification, extraction, OCR or plain rules — chosen for the problem, not for the brochure.

3

Prototype

A working version tested against your real data before the production build is quoted.

4

Production build

Built, integrated with your existing systems, and deployed.

5

Validation

Output checked against results you already know are correct, and the edge cases handled.

6

Monitoring & handover

Output monitoring, error logging, and documentation of how the system decides.

Discuss your AI project

What if AI is not the right solution for our problem?

We will tell you, and it happens often. For a good number of automation problems a well-written script is more reliable, cheaper to run and far easier to debug when something changes.

We would rather lose a project than build something that does not work. An AI system that produces plausible-looking wrong answers is worse than the manual process it replaced, because at least somebody was checking.

How much does an AI automation project cost?

It depends on the problem and how clean the data behind it is. We quote a fixed price for a defined scope, and the prototype stage means the larger number is only committed once something has been shown to work.

Replacing one specific manual process is a different job from a system spanning several workflows with human review built in. Anyone quoting before seeing the data is guessing.

Tell us what somebody is doing by hand and we will tell you whether AI is the right answer and what it would take.

Do you use OpenAI or ChatGPT in your builds?

Sometimes. We use OpenAI’s API, Anthropic’s API, and open-source models, chosen by what the project needs — accuracy, cost per call, response time, and where the data is allowed to go.

Some projects use no language model at all. Document extraction and classification are often better served by smaller, cheaper and more predictable approaches.

Can you use AI on our proprietary data?

Yes, and how depends on how sensitive it is. Options run from API calls structured so nothing is retained, through to an open-source model running entirely on your own infrastructure so no data leaves it.

Data handling is agreed in writing before any project starts. If the answer is that your data cannot go to a third-party provider, that constrains the approach rather than ruling out the project.

How do we know the AI is getting it right?

Validation against results you already know are correct, before it goes live. Then ongoing output monitoring, so accuracy drift is visible rather than discovered.

Where the cost of a wrong answer is high, we build in human review — the AI does the work and a person confirms it, which is usually still a large saving over doing the whole thing by hand. Fully automated is not always the goal.

Can AI work with the systems we already have?

Usually, and that is generally where the value is. AI that sits alongside your existing tools and reads from them is more useful than a separate product somebody has to remember to open.

Most of these projects involve some systems integration — the AI needs data from somewhere and its output has to go somewhere. Where the underlying process is repetitive but does not need judgement, business process automation is often the cheaper answer.

Ready to talk?

Tell us what you are doing manually and we will tell you if AI can help

A free scoping call and an honest assessment. Fixed price if we proceed — and a straight answer if AI is not the right tool.

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