AI engineering
Get the productivity benefits of AI without losing control of your engineering system.
We help software organizations integrate coding agents and AI development tools into the SDLC with the architecture, review, testing, and governance needed for production work.
Talk through your situationThe problem
Code generation is accelerating. Engineering controls are not.
Claude Code, Codex, Cursor, GitHub Copilot, and internal agents can change how teams deliver software. They also increase code volume, review pressure, security questions, and architectural drift. Tool access alone does not create durable productivity. The engineering system around the tools has to change as well.
What we evaluate
- Current development and review workflows
- Tool usage, data handling, and security exposure
- Testing, quality, and approval controls
- Architecture decision practices
- Metrics for throughput, quality, and rework
What an engagement can include
- AI engineering readiness review
- Tool and workflow recommendations
- Engineering AI policy
- Code review and testing strategy
- Secure SDLC integration
- Agentic workflow and architecture guidance
What leadership should expect
- A governed path to broader AI tool use
- Faster delivery without hidden quality loss
- Clear standards for engineers and reviewers
- Better visibility into value and risk
- Architecture that can absorb higher delivery velocity
Who this is for
Talk with Axis SentinelCTOs, VP Engineering leaders, engineering directors, and platform teams moving from individual AI experiments to an organization-wide engineering capability.
Not sure whether the situation calls for an assessment, a focused review, or ongoing guidance? We can work that out in the first conversation.