AI Training Built From Production.
Not Theory.
124 skills. 46 specialized agents. 900+ logged sessions of real implementation work. We didn't study AI — we built with it. Now we teach what we learned.
Most AI Training Doesn't Stick
Your team attended the workshop. They got the certificate. Three weeks later, they're back to copy-pasting into ChatGPT. The problem isn't motivation — it's that the training wasn't built from real work.
What you've seen before
- A one-day workshop with slides about 'the future of AI'
- Prompt engineering tips copied from Twitter threads
- Theoretical frameworks with no production evidence
- Vendor-locked training that only works with one platform
What we actually deliver
- 124 production skills extracted from real project work
- Methods battle-tested across 900+ logged sessions
- Platform-agnostic patterns that work with any LLM provider
- Your team builds something real during training, not toy examples
Training Tracks
Each track is built from patterns we use daily across production projects. Your team learns by building, not watching.
Structured Prompting
Move beyond 'write me a prompt' to systematic instruction design. Pre-code invariables, INTENT gates, forced artifacts — the patterns that make AI output reliable, not lucky.
Multi-Agent Architecture
Design agent systems where specialized roles collaborate. Orchestrators, reviewers, domain specialists — the same architecture we run across 190+ active projects.
Memory & Context Management
AI forgets everything between sessions. We teach structured memory systems — session logs, knowledge graphs, checkpoint recovery — so your agents build on yesterday's work.
Tool-Use Patterns
Agents that can read files, query databases, call APIs, and verify their own output. Not demos — production patterns with error handling and fallback chains.
Domain Adaptation
Turn a general-purpose model into a domain specialist. Skill files, evidence gates, adapter patterns — the same method we use to deploy agents across finance, legal, and engineering.
Verification & Quality Gates
The gap between 'the AI said it's done' and 'it's actually done.' Adversarial review, experiential testing, silent degradation scanning — trust but verify, systematically.
Built From Evidence, Not Slides
Every technique we teach comes from logged, verified production work. We can show you the session where we discovered it and the projects where it's still running.
Tell Us About Your Team
We'll design a training program around your team's actual work, not generic scenarios.