Help your team use AI with senior-engineer habits.
We train engineering teams to use AI assistants inside their real delivery flow: planning, coding, testing, review, security, and release. The result is a shared operating model your team can keep using after the workshop.
Buying the tool is the easy part.
The hard part is changing how engineers frame work, gather context, test changes, and explain decisions in review. We coach those habits against your real codebase.
Tool access without standards
Developers adopt assistants at different speeds. Some build useful habits, others paste generated changes into review and hope the pipeline catches the risk.
Prompting detached from the repo
Generic prompting advice breaks down fast. Teams need patterns for their services, test suites, deployment rules, data models, and review culture.
Managers cannot see adoption quality
Usage counts do not show whether AI improves delivery. Leaders need signals tied to cycle time, review load, defect rate, and test coverage.
Practical habits for AI-assisted engineering.
We skip generic prompt theater. The training centers on how your team ships software: tickets, repos, tests, CI/CD, review, incidents, and customer risk.
AI coding workflow
How to move from task framing to context gathering, implementation, self-review, tests, and pull request notes without hiding risk from reviewers.
Context and prompt patterns
Repo-aware prompts, acceptance criteria, constraints, examples, and review prompts your team can reuse across features and incidents.
Testing with AI in the loop
Use AI to propose edge cases, build fixtures, extend integration tests, and challenge weak assumptions before a human review starts.
Secure usage habits
Handling secrets, customer data, third-party code, dependency suggestions, generated shell commands, and permissions in coding agents.
Team playbooks
Shared conventions for prompts, PR descriptions, review expectations, branch rules, and the situations where a human must take the wheel.
Adoption metrics
Track quality through delivery signals: PR size, review rework, escaped defects, pipeline failures, lead time, and incidents after release.
Training works best when it touches the code people ship.
We can run a tool-agnostic workshop, but the strongest results come from using your delivery process as the classroom. Engineers practice on familiar services, familiar failure modes, and familiar review expectations.
- Repo-specific exercises
- Pull request coaching
- Testing and review checklists
- Leadership adoption metrics
Workshops, training, and coaching sprints.
Leadership workshop
A focused session for CTOs, engineering managers, and platform leads. We define the operating model, risks, metrics, and rollout path.
Developer training
Hands-on sessions using your code patterns or representative examples. Developers leave with repeatable workflows and examples they can reuse.
Coaching sprint
We pair with teams over real work, tune the workflow, strengthen tests, and turn lessons into a team playbook.
Give your team a practical AI-assisted development playbook.
Tell us how your team works today. We will shape the training around your stack, your review process, and the risks you need developers to handle well.