AI Implementation

Most AI projects fail for a boring reason: they start with the technology and look for a problem afterwards. The ones that work start with a specific, repetitive, well-defined task that is currently costing somebody hours.

Where it genuinely helps

Document handling. Extracting fields from invoices, forms, contracts or emails where the format varies enough that rules alone will not do.

Classification. Sorting incoming items into the right queue — support tickets, enquiries, categorised records.

Drafting. First-pass text that a person then edits. Useful where volume is high and quality review is already part of the process.

Data extraction and summarising. Turning unstructured text into structured records.

Where it does not

AI is a poor fit for anything requiring guaranteed accuracy with no review step, for low-volume tasks where setup costs more than the manual work, and for decisions where being wrong has real consequences and nobody is checking.

We will tell you when the honest answer is “just automate this with a rule”.

How we approach it

1. Find the task. One repetitive process, clearly bounded, with a measurable current cost.
2. Measure the baseline. How long does it take today, and how often is it wrong?
3. Build the smallest version. Often a script plus an API call, not a platform.
4. Review in the loop. Human confirmation until accuracy is proven, then progressively less.
5. Report. Did it actually save the time it promised?

What you get

• Automation of the specific process, not a general “AI strategy”
• Logs of what the system decided, so failures are traceable
• A human review step wherever accuracy is not guaranteed
• An honest assessment of whether it was worth doing

On cost

Model inference is metered, and costs vary widely with volume and approach. We will give you an expected running cost per item before you commit, because an automation that saves two hours and costs more than two hours is not an improvement.