AI and Automation
We integrate AI models into your existing products and processes and automate repetitive work, starting from where it measurably pays off.
Talk to us about this serviceAI is a component, not a solution. Its value depends on which job it takes over, at what error rate, and at what cost. So the work starts with the process, not the model.
Where to start
The best candidates for automation share three traits: the work repeats often, inputs and outputs are well defined, and the cost of an error is contained. Invoice and document reading, support ticket triage, content summarisation, data cleanup, and reporting usually fit that description. Where mistakes are expensive, automation does not decide on its own — it puts a prepared recommendation in front of a person.
Measure with a pilot
Whether a process can be automated is settled by measurement, not by a demo. We build a narrow pilot, run it against real data, and look at three numbers: accuracy, cost per unit of work, and end-to-end time. If the pilot misses the mark, not continuing is a legitimate outcome, and we say so plainly.
What production actually demands
The gap between a demo and production is error handling. In a live automation, timeouts, provider outages, rate limits, unexpected input formats, and cost spikes are ordinary events. Queues, retries, backoff, and circuit breakers are therefore built in from the start. Every output is stored with its input so it can be audited later.
Keeping quality from drifting
As models and prompts change, output quality shifts. To see that, an evaluation set is prepared at the start of the project: real examples with expected results. Every change runs against it, so any claim of improvement rests on a measurement.
Data and privacy
Which data reaches which provider, how long it is retained, and who can access it are defined on day one. For flows involving personal data, masking, field-level filtering, and where necessary models running on your own infrastructure are evaluated.
What you end up with
- A working automation for the chosen process
- Cost and latency measurements for the model calls
- A human approval path for errors and edge cases
- An evaluation set that tracks output quality
- A written definition of the data flow and retention limits
Technologies we use
- Claude and OpenAI APIs
- Gemini API
- Ollama for models running on your own infrastructure
- Retrieval (RAG) architectures
- Vector databases
- Queue and workflow engines
- Python
- TypeScript
How the work runs
- 01
Find the candidates
Repetitive, rule-shaped, high-volume work is listed, each item with an estimate of what automating it saves.
- 02
Pilot
One process is automated narrowly and measured against real data.
- 03
Evaluate
Accuracy, cost, and latency are measured; the pilot result decides whether it continues.
- 04
Into production
The automation is wired into existing systems, with failure handling and a human approval path.
- 05
Monitor
Output quality and cost are tracked continuously and the evaluation set is kept current.
Frequently asked questions
Will our data be used to train models?+
No. Provider selection and contract settings are configured so the data is not used for training. Which data goes where is defined in writing before the project starts.
What happens when the model is wrong?+
The chance of a wrong answer never reaches zero, so the design assumes it: human approval on critical steps, backing off on low confidence, and a record of every output.
How do you keep cost under control?+
Model calls are metered and cost is reported per unit of work. Caching, smaller models, and prompt tuning are applied against those measurements.
Can we run models on our own servers?+
Where privacy or regulation requires it, open models running on your own infrastructure are evaluated. The decision compares accuracy, cost, and operational load.
Let’s build your next project together
Tell us your idea and we’ll design the right solution for you.
Get in touch