Services · AI Training & Agents

Put AI to work in your engineering team

Training, agent creation and custom skills for engineering workflows — delivered by automation engineers who run their own practice on these tools every day.

What we offer

From first prompt to production agents

Most AI training is generic. Ours is taught by engineers who use these tools on live industrial projects — the examples are PLCs, drives and commissioning logs, not marketing copy.

01

Hands-on AI training for engineers

From useful prompting to fully agentic workflows — taught on your real plant use-cases, not toy demos. Your engineers leave with working setups, not slideware.

WorkshopsReal use-cases
02

Agent creation

Autonomous helpers for the repetitive engineering work nobody enjoys: code review, documentation, data crunching, report generation — built, tested and handed over.

Claude CodeAutomation
03

Custom skills & MCP servers

Connect AI to the tools you already use. We built an MCP server that drives TIA Portal — exporting and importing blocks, compiling projects, reading diagnostics — and we can do the same for your toolchain.

MCPTIA PortalIntegrations
04

Workflow integration

Memory, hooks, CI pipelines, knowledge bases — AI wired into how your team already works, so it keeps getting used after the workshop ends.

HooksCIKnowledge bases

Plant wiki & RAG

Your manuals become a knowledge base the AI actually cites

Every plant has the same library: OEM manuals nobody opens, electrical drawings in five revisions, and PLC programs whose comments are the only documentation that's true. We teach your team to turn that pile into a linked knowledge wiki — and wire it to the LLM with retrieval-augmented generation (RAG).

The difference is trust: instead of a model guessing from training data, it answers from your documents and shows the source — page, drawing number, code block. All of it on-prem, inside your security boundary, and maintained by your own people after we leave.

Knowledge wikiRAGCited answersOn-prem

Hands-on SCL with a coding harness

Working SCL from one prompt — because the rules live in the harness

Ask a bare chatbot for SCL and you get code that looks right and fails in TIA: wrong encoding, timers buried inside CASE branches, naming that matches nobody's library. The fix isn't better prompting — it's a harness where the domain rules are encoded once, as skills, and enforced on every generation.

In the hands-on sessions your engineers build that harness for your own library: UTF-8 BOM handling, stateful-call rules, your block headers and naming conventions. Then they watch one prompt come back as a block that compiles and imports clean, first pass — repeatably, on their machines, after we're gone.

SkillsCoding harnessYour library rulesImport-clean SCL

We run our own engineering practice on these tools daily — agents review our SCL, our wiki writes itself from session logs, and our MCP server talks straight to TIA Portal. This is not theory.

How it runs

Four steps, no lock-in

01

Audit

Half a day with your team to find the workflows where AI actually pays — and the ones where it doesn’t.

02

Build

We set up the agents, skills and integrations on your machines, with your data, inside your security boundary.

03

Train

Hands-on sessions where your engineers drive — until the tools are theirs, not ours.

04

Support

Follow-up as the workflows bed in, plus updates as the AI tooling landscape moves.

Your team, with superpowers

Tell us what your engineers spend their week on — we'll show you which parts an agent should be doing by Friday.

Book a training session