Engineering built for the AI era.
The AI-powered SDLC for enterprises that demand cost, control, and governance. Five live components, scoped to your stack, deployed inside your VPC. Nine of 11 SDLC stages instrumented — your platform team owns the source by day twelve.
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AI rationale on PR
One substrate. Five levers. Zero slides.
Every component is curl-able, queryable, reproducible. Your CTO hits the API on day 4. Your platform team owns the source by day 12.
Cost Observability
Every AI call attributed by team, agent, and PR. One gateway, four surfaces.
Governed Workflow
Hooks fire. MCP refuses out-of-scope. PRs auto-describe with cost and trace ID.
Plugin Distribution
Governance ships like Helm charts. Soft, medium, hard enforcement — one dial.
Failure Drill
Three live attacks. Zero reach the model. Every attempt audit-logged.
5-Engineer Case Study
Real engineers. Real PRs. Recipe open-sourced to your platform team.
Attribute every dollar of AI spend.
Most AI bills are "around $200K somewhere." One gateway tags every call by team, agent, and PR — queryable from CLI, API, Grafana, or Slack.
- Per-PR cost in days, not quarters.
- 50–90% off cacheable input via prompt caching.
- 50% off async work via the Batch API. 60–80% blended savings with tier-routing.
- $100–200k of shadow spend recovered annually.
Govern every agent action.
Optimism is not a control. Policy enforced at the substrate — not in the prompt. Hooks redact secrets. MCP catalogs scope tools. Plugins are signed. Every decision lands in an audit lake.
- Three live attacks blocked — secret-in-prompt, indirect injection, unapproved MCP.
- Soft / medium / hard enforcement on one dial.
- Audit lake in S3, forensics retrievable by trace ID in under a minute.
- <30 ms latency overhead across the full defense stack.
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Defend the ROI with your own data.
Synthetic benchmarks won't survive your CFO. Five engineers. Ten days. Real PRs through a scoped GitHub App. The deltas your board will ask about — and the recipe handed to your platform team.
- Real engineers, real PRs. No synthetic benchmark.
- Aggregate + per-dev dashboards, privacy-respecting.
- Open-sourced recipe on day 12 — github.com/moring/aidlc-stats.
- Year-2: same recipe, n=50, no consultants.
Three signers. One substrate.
Built for the room where the CTO, CISO, and CFO all have to say yes. Each gets a different answer from the same foundation.
Agentic productivity, governed.
Same agent UX your engineers use, governed underneath. $3.0–5.0M Y1 productivity on 200 engineers.
Tested controls, not claims.
Three live attacks blocked. Every attempt in your audit lake. Tested controls you can hand to auditors.
Per-PR attribution by day 4.
Caching, batch, and routing cut blended spend 50–90%. 40–80× Y1 ROI. Payback under two months.
The demo is the artifact. The substrate is the moat.
No PoC theater. We ship in three phases. On day 14, your platform team owns the source.
Cost layer goes live.
LLM gateway deployed in your cloud. Every call attributed by team, agent, and PR. CFO refreshes the dashboard on day four.
Engineers code in the substrate.
Five volunteers run real sprint work through the governed workflow. Hooks fire, MCPs scope, PRs auto-describe. Metrics roll up daily.
You own the moat.
Plugin source, registry, and GitHub-stats recipe handed over. Failure drill in front of the room: three attacks, all blocked.
$5–10M defended five different ways.
200 engineers. Engineering-only. No business-vertical claims. Floor holds even if four of five levers underperform.
Book your workshop
After 14 days the substrate is in your cloud. Your CFO has a dashboard. Your CISO has forensics. Your CTO has a number.
The cost lever pays back in under two months. The other four are upside.