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Workshop Formats

Every workshop is hands-on, taught against your codebase, and covers context persistence, multi-agent orchestration, and CI/CD integration for AI-generated code.

Half-Day Remote $6,000

Four hours of hands-on enablement for up to 10 engineers. Your team learns core AI coding workflow patterns and applies them to your codebase during the session.

Outcome: Each engineer leaves with a working AI coding workflow they can use Monday morning.

Full-Day Remote $10,000

Seven hours for up to 15 engineers. Morning: structured labs on all three pillars. Afternoon: your team builds a working prototype using real business problems.

Includes everything in the half-day, plus a prototype build on your codebase, team workflow documentation, and a recorded session for internal reference.

Outcome: Standardized AI coding workflows, a working prototype, and documentation your team can share across the org.

Two-Day On-Site Immersion Custom

Two days embedded with your team. Day one: structured labs on AI coding patterns. Day two: hackathon on your real use cases with hands-on pairing against your production codebase.

Includes deep integration with your existing CI/CD pipeline, pairing sessions, and a post-workshop adoption roadmap. Custom pricing based on team size and scope.

Outcome: Battle-tested AI coding workflows integrated into your actual development environment.

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What an Engagement Looks Like

Every engagement starts with an assessment of your team's AI coding maturity across three pillars. From there, we scope a hands-on workshop tailored to your stack, your codebase, and the gaps that matter most.

Assessment

We start by understanding where your team stands today: how they use AI tools, what context they provide, how code flows through your pipeline, and where the gaps are.

Hands-On Workshop

Your engineers work on their own codebase during the session. They leave with working AI coding workflows — not slides, not theory. Remote or on-site, half-day to multi-day, scoped to your team's needs.

Ongoing Advisory

After the workshop, we stay engaged to help your team embed the patterns into daily practice. Adoption without follow-through is just a training exercise.

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What Your Team Learns

Three pillars that take AI coding from experimental usage to production-grade engineering.

1. Context Persistence (Pre-Generation)

AI tools produce better code when they understand your codebase, conventions, and constraints. Your team learns to build context that persists across sessions and contributors.

  • CLAUDE.md and rules files that encode your team's standards
  • Progressive disclosure for large codebases
  • Repository organization that AI tools can navigate

2. Multi-Agent Orchestration (Generation)

Single-prompt coding hits a ceiling fast. Your team learns patterns for decomposing work across multiple AI agents working in parallel.

  • Spec-driven development: write the spec, let agents implement
  • Task decomposition and parallel agent coordination
  • Multi-file generation workflows that stay consistent

3. CI/CD Integration (Post-Generation)

AI-generated code that passes a vibe check in a PR but fails in production is worse than no AI at all. Your team learns the quality gates that keep your pipeline trustworthy.

  • Testing patterns for AI-generated code
  • Security and dependency analysis guardrails
  • Code review workflows for AI-assisted PRs
  • Quality gates that catch what AI gets wrong

What Your Team Walks Away With

  • A repeatable AI coding workflow tailored to your stack
  • Context files (CLAUDE.md, rules) configured for your codebase
  • A GitHub repository with code, docs, and tests from the session
  • Recorded demos for internal review and onboarding
  • A pattern library that reduces build time on future projects
  • Post-workshop readiness assessment (full-day and on-site formats)

Who This Is For

  • Tech leads and engineering managers adopting AI coding tools for their teams
  • Teams using AI tools inconsistently and needing standardized workflows
  • Engineering organizations that need CI/CD integration for AI-generated code
  • Consulting and services firms where AI adoption drives delivery speed

Teams with existing CI/CD pipelines and code review practices see the fastest results.

What Teams Are Saying

"The amount of useful content is remarkable. Our team is already using the work from the hackathon with customers."

— CEO, workshop customer

Paul built Stelligent from zero to nearly 100 enterprise customers, AWS Premier Partner status, and a $25M exit — by helping engineering teams adopt the discipline of Continuous Integration. This workshop applies that same playbook to AI-native development.

The patterns taught in this workshop are drawn from the open-source ai-development-patterns framework (400+ stars) — battle-tested by the community before they reach your team.

What You Keep After the Engagement

  • Access to the workshop pattern library and all code samples
  • Recorded session for team onboarding and reference
  • Context files (CLAUDE.md, rules) configured during the workshop
  • Updates when new AI coding patterns and tool integrations ship

What This Workshop Does Not Promise

  • Production-ready software in a single session
  • Replacement of your existing SDLC controls
  • Deployment to regulated environments without further work

This workshop builds skills, patterns, and prototypes. Your team still follows your normal processes for security, data governance, and production deployment.

Who You'll Work With

Paul Duvall wrote the book on Continuous Integration. Literally. His Jolt Award-winning Continuous Integration: Improving Software Quality and Reducing Risk (Martin Fowler Signature Series) defined the discipline for a generation of engineers. Now he is building the playbook for CI/CD in the age of AI-generated code.

  • CI/CD Pioneer: Authored the foundational book on Continuous Integration
  • Company Builder: Co-founded Stelligent, scaling to nearly 100 enterprise customers, AWS Premier Partner status, and $10M+ annual revenue before a $25M exit
  • AWS Engineering Leader: Led DevSecOps and Security Innovation teams at AWS (2021-2024)
  • AWS Hero (2016-2021): Recognized for contributions to the cloud community
  • AI Coding Practitioner: Three years of daily hands-on experience with AI coding workflows, building production patterns for context persistence, multi-agent orchestration, and CI/CD integration