Corporate AI program
AI for the Software Development Lifecycle (SDLC)
For: Developers, tech leads, QA engineers, DevOps and engineering managers.
In short
AI for SDLC training shows engineering teams how to use AI coding agents and assistants — Claude Code, GitHub Copilot, Cursor and OpenAI Codex — across the whole software lifecycle: requirements, design, coding, testing, code review, DevOps and documentation. The focus is real productivity gains without sacrificing code quality or security.
What your team will be able to do
- Use AI coding agents effectively on large, real codebases
- Write specs and context files that make agents reliable
- Generate and maintain tests, docs and migrations with AI
- Set team standards for AI code review and security
- Measure productivity impact with sensible engineering metrics
Capstone
Each squad takes a real backlog item from your codebase and delivers it end-to-end with AI agents — spec, code, tests, review and docs — then compares effort to the baseline.
Tools & stack
Curriculum
Program modules
A typical outline — every program is tailored to your team's roles, tools, data policies and use cases after a scoping call.
- 01
AI coding tools landscape
- Assistants vs agents: Copilot, Cursor, Claude Code, Codex
- Choosing tools for your stack and policies
- Enterprise setup, privacy and licensing
- 02
Agentic coding workflows
- Spec-driven development and plan-then-execute
- Context files (CLAUDE.md, AGENTS.md, rules)
- Working with legacy and monorepo codebases
- 03
Quality & testing
- Test generation and TDD with agents
- AI-assisted code review
- Refactoring and migrations at scale
- 04
DevOps & docs
- CI/CD with AI: pipelines, IaC, incident triage
- Docs, ADRs and onboarding guides
- MCP servers for internal tools
- 05
Governance & metrics
- Security, secrets and licensing risk
- Team conventions and guardrails
- Measuring outcomes: cycle time, quality, satisfaction
FAQs
AI for SDLC: frequently asked questions
Still have questions? Talk to our team — we reply within one working day.
Which AI coding tools do you train on?
Claude Code, GitHub Copilot, Cursor, OpenAI Codex, Windsurf and Gemini CLI — or whatever your organization has approved. The workflows we teach transfer across tools.
Will AI coding tools reduce our code quality?
Not when used well. A large part of the program covers specs, tests, review and guardrails so AI speeds teams up while keeping quality and security standards.
Can you train on our actual codebase?
Yes, and we recommend it. With your approval, labs run on your repositories inside your environment so teams learn on real complexity.
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Next step
Bring AI for SDLC to your team.
Tell us who you want to upskill and what outcome you need. You'll get a tailored proposal — curriculum, format, trainers and pricing — within 48 hours.