ShelterCode AI
01
Discovery
Domain workshops, bounded context mapping, and ADR checkpoints reviewed by senior engineers.
How We Work
We publish our delivery system in detail so technical buyers can evaluate our process, constraints, and safety controls before a call.
Every engagement has one primary objective: accelerate delivery without surrendering engineering judgment.
The AI-Assisted SDLC
A concrete operating model with explicit human ownership and measurable quality gates.
Human: Domain workshops, stakeholder interviews
AI: LLM-assisted bounded context mapping, competitive architecture analysis
Gate: ADRs reviewed by senior engineers
02
Build
AI accelerates boilerplate while humans own architecture decisions and review gates.
03
Assure
Quality gates target ≥90% coverage and ≥75% mutation score before release approval.
04
Ship
SLOs, rollback rehearsals, and launch runbooks are complete before production cutover.
Guardrails: How We Prevent AI Failure
Objections from technical leaders are legitimate. These controls are non-negotiable in our delivery process.
| Failure Mode | How We Prevent It |
|---|---|
| Hallucinated code | Every AI-generated function requires a human-authored test and reviewer approval. |
| Security vulnerabilities | AI-generated code passes automated SAST and dependency checks before human review. |
| Architecture drift | Pattern library constraints prevent unauthorized architectural patterns from entering the codebase. |
| Over-reliance on AI | PRs above a 70% AI-generated ratio trigger mandatory architecture review. |
| Prompt injection | LLM-facing flows use validation, filtering, and sandboxed execution by default. |
What AI Can't Do (And What We Do Instead)
- AI can’t learn your domain in one workshop. We start with senior-led discovery sessions.
- AI can’t decide architecture trade-offs. Humans own decision records and risk calls.
- AI can’t replace code review judgment. It can accelerate issue detection, not product strategy.
Want to pressure-test this approach against your roadmap?
Bring your current architecture, quality concerns, and timelines. We’ll map where AI helps and where it hurts.