Service — AI & automation
AI systems that do real work.
Custom agents, assistants, and workflow automation applied to actual business processes. We take over the repetitive work so your team can do the rest.
01Approach
01
n8n workflow automation
We build and run automations on n8n, connecting the tools you already use — CRMs, mail, spreadsheets, internal APIs — into processes that run on their own.
02
Custom AI agents
Beyond simple chatbots: agents that read your documents, query your data, and carry out multi-step tasks with clear boundaries and logging.
03
LLM integration
We integrate models from OpenAI, Anthropic, and Google into your internal tools and customer products, with prompts, evaluation, and cost control handled properly.
02Questions
The questions that come before a contract.
- What is actually worth automating with AI?
- Work that is repetitive, text- or document-heavy, and produces an output someone can check: intake and classification, extraction from documents into structured fields, drafting replies, routing tickets, searching your internal documents. If nobody can tell whether the output was right, it is not a good first candidate.
- How do you deal with models getting things wrong?
- We scope tasks so the output is verifiable, keep a human approval step wherever a mistake is expensive, and measure accuracy against a labelled sample of your real cases before anything reaches production. Every model call is logged with its input and output, so a bad result can be traced instead of guessed at.
- Where does our data go?
- Where you decide. We can run against a commercial API with a no-training agreement and a chosen region, or against open models on your own infrastructure when the data cannot leave. Either way we minimise what is sent, keep secrets out of prompts, and document exactly which fields go where.
- What does it cost to run, and are we locked to one model?
- We measure cost per run on real data before rollout, so you see the monthly figure before committing to it, and we tune prompts, caching and model size against that number. Calls go through a single internal interface, so swapping provider or model later is a configuration change and a re-run of the evaluation set, not a rebuild.
- Do we need to hire ML engineers to keep it running?
- No. What we hand over is ordinary software: prompts and configuration in your repository, an evaluation set that tells you when quality drops, dashboards for cost and failures, and a runbook. Your existing developers maintain it the way they maintain any other service.
- How do these projects start?
- With one narrow process and a measured baseline, not a platform. We agree what the automation has to beat — handling time, error rate, backlog — build it for that one process, measure it against the baseline, and only then choose the second one.