Train your team to use AI like a system, not a chatbot.
Two formats — a 90-minute primer for leadership alignment, or a full-day on-site build where every participant ships a working agent on your own data by 5pm.
DESIGNED & DELIVERED BY PAUL SAVE Fast, high-density introduction to state-of-the-art AI workflows.
For teams that want a leadership-aligned intro before committing to a deeper engagement.
60 minutes content + 30 minutes Q&A. Pre-session calibration based on a short team AI-usage survey. Pick 3 topics from the menu below, with a 4th held in reserve.
- A documented set of prompt patterns calibrated to your domain
- A working 'clarity agent' you can re-use for any prompt
- A shared mental model of where AI risk lives in your stack
- Concrete next-step recommendations tailored to your tools
Stop watching demos. Ship a working agent on your own data by 5pm.
For teams ready to put hands on the keyboard. Every participant leaves with something running.
Morning — dry run on public data. Same workflow your team will run on real data, scaffolded with a known-good dataset so access is never the blocker. Afternoon — your own platform. Real internal problem, with Paul embedded for live debugging.
- A working agent deployed against your own data, not a sandbox
- The exact prompt, skill, or MCP configuration that produced it
- A documented runbook to extend and operate the system after Paul is gone
- A red-team report on the risks unique to your deployment context
Calibrated to your team, in advance.
Every session is shaped around your team's current AI usage, tools, and bottlenecks — captured in a short pre-session survey. The five topics below scale in depth from the Primer to the Build.
Better Prompting for Better Solutions
Beyond 'good enough' — the techniques that turn useful prompts into rubric-graded, repeatable systems. Leave with prompt templates calibrated to your work and a one-shot 'clarity agent' that improves any prompt your team writes.
Skills and MCP
The bridge between 'helpful chatbot' and 'useful system.' What Skills and MCP servers are, when to introduce them, and — critically — when not to. In the full-day Build, your team ships their own.
Security Posture: ISO 27001 / SOC 2-aligned AI Workflows
Where the real AI-related risks live — sensitive data, implicit decisions, untraceable reasoning. How to use AI itself to review architectures, surface compliance gaps, and enforce standards — without slowing velocity.
Multi-Agent Orchestration
Single-model answers are brittle on ambiguous, high-stakes problems. The mental model of AI as a team of collaborators — sequential, parallel, convergent — and where it materially outperforms a single chat.
Memory & Context Management
AI is stateless. Real work isn't. How context windows fail silently, what compaction loses, and the design patterns that maintain coherence across long-running engagements and handoffs.
Paul Save has been working in data science and machine learning since 2016 — full-time on AI Operations since founding CDSI in 2024.
Past Kaggle competitor across multiple machine learning competitions, including a top-10% finish (Bronze Medal) on an NLP classification challenge. 20+ conference presentations on AI, ML, and product topics — selected venues include Microsoft MLADS, Microsoft MVP Global Summit, AWS Initiate, Central 1 Momentum, and ProductCamps in Vancouver, Seattle, and Portland.
Shipped Microsoft Teams platform integrations that cut partner onboarding from 3 weeks to 1 day. Built a 0-to-1 multi-tenant ML platform on AWS at Central 1 Credit Union, with churn models exceeding 90% accuracy and 70% precision across Canadian credit unions.
Currently building iD8 — a multi-agent AI development platform with 50+ MCP tools, state-of-the-art memory systems, and production-grade governance primitives. Author of MarkCrawl, an open-source web crawler that ranked #1 in a published 7-tool benchmark for speed, extraction quality, and cost.
Expert on ISO/IEC JTC 1/SC 42 Artificial Intelligence — contributed to international standards on AI governance and safe deployment.
Start with a few lines on the problem.
Project context, rough scope, what you've tried. I reply within two business days. If it's a fit, we book a scoping call. If not, I'll point you at someone who's a better match.