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Service 04
Applied AI
A language model solving a defined task, with a verifiable result — not a generic chatbot.
Context
AI pays off when it's pointed at a narrow, boring task: reading a document and extracting a field, classifying a request, drafting the first version of a standardised text, summarising volume nobody has time to read.
The hard part isn't calling the model — it's deciding what to do when it gets things wrong. Every piece of work here includes a way to measure accuracy and a human review path where mistakes are expensive. Without that it isn't automation, it's luck.
Signs this is your case
- Someone reads dozens of documents a day to pull out five pieces of information
- Triaging messages or tickets consumes the team before the actual support starts
- You tried an AI tool and it worked in the demo and failed at volume
What you're left with
- An automated task with accuracy measured on your real cases
- Human review where mistakes are expensive, automatic where they aren't
- Cost per run estimated before it goes to production
What it isn't
- A corporate chatbot that answers anything about the company
- A promise to replace an entire team
Describe the actual case. The technical answer comes in the conversation.
Other fronts
01Software developmentA web system, dashboard or internal tool built for your process — not for the market's average process.Details02Process automationTaking off your hands the repetitive work that today depends on someone remembering to do it.Details03Integrations and APIsMaking two systems that were never meant to talk exchange data reliably.Details05Maintenance and productionKeeping alive what already runs — including the system I didn't write.Details06Running the day-to-dayNot every demand becomes code. A routine that needs an owner and a technical decision that needs a second opinion are mine too.Details