AI-Driven SDLC Consulting
AI can make software delivery dramatically faster — but only if it's introduced with discipline. Loose prompting without a written spec produces code nobody fully trusts, and speed that doesn't survive contact with production. We help engineering teams move from ad hoc AI use to a structured, spec-driven approach — evaluating frameworks like GitHub's Spec Kit, OpenSpec, AWS's Kiro, and Atlassian's Rovo Dev against your stack — paired with agile ways of working fitted to how your team actually delivers.
Why this needs discipline
AI coding agents can scaffold, refactor, and ship code in minutes — but only as well as the instructions and guardrails behind them. Used without discipline, that speed produces code nobody fully trusts and technical debt that outpaces the productivity gains. Used with a written spec, a fitting framework, and real review checkpoints, it doesn't.
The spec-driven landscape
A handful of frameworks have emerged to bring discipline to AI-assisted development. We're framework-agnostic — we help you evaluate which one fits your stack and team, then roll it out properly.
Spec Kit
GitHub
An open-source toolkit for spec-driven development with AI coding agents.
OpenSpec
Open source
A lightweight spec-driven framework that keeps AI coding assistants working from a written spec instead of guessing.
Kiro
AWS
An agentic AI IDE built around a spec-driven workflow, from Amazon Web Services.
Rovo Dev
Atlassian
An agentic AI coding agent integrated with Jira and Bitbucket, available in the CLI and as a code reviewer.
Areas of focus
- Spec-Driven Development (SDD) adoption — templates, review checkpoints, and team habits
- Framework evaluation and rollout — Spec Kit, OpenSpec, Kiro, Rovo Dev, and others, matched to your stack
- Grounding AI coding agents in specifications rather than terse prompts
- Deriving test coverage and QA checklists directly from acceptance criteria
- Auditing existing AI-assisted workflows for quality, coverage, and governance gaps
What you can expect
- A disciplined AI SDLC instead of ad hoc, tool-by-tool experimentation
- Fewer production incidents caused by AI-generated code that only handles the happy path
- Faster, more confident code review, because there's an explicit spec to check against
- Specs that double as living documentation for the next engineer — human or AI