Service · Flagship engagement
Coach your team to ship faster with AI — while catching more of the bugs AI introduces.
CloudFunnel helps engineering teams put a real operating model around the AI tools they already use — gated spec → plan → implement, plus cross-model review that catches more of what one-pass AI misses. Your engineers own it when we leave.
The tools are commoditized. The operating model isn't.
Many teams now have Copilot, Cursor, or Claude Code. Far fewer have standardized how the team actually builds with them — the workflow, the review gates, and the quality bar that accounts for AI-generated code. So you get inconsistent output, shallow reviews, and a quiet worry about what's slipping through to production. The licenses aren't the problem. The missing operating model is.
The core idea
One AI writes the code. A different AI reviews it.
A model reviewing its own output has the same blind spots that produced it. A different model, reviewing cold against the spec with a hostile checklist, can catch what single-pass AI often misses — especially security and correctness issues. In our own codebase, we've seen it catch a cross-tenant data-access bug that first-pass implementation missed — caught before merge.
Spec → plan → implement, gated
Intent is made explicit before code, with a review at each step — so errors are caught before AI generates against the wrong target.
Cross-model adversarial review
The implementer model and the reviewer model differ. The reviewer hunts security, tenant isolation, contract drift, and edge cases.
Tiered rigor
Heavy gates where a mistake is a breach or a wrong number; light-touch everywhere else. The goal: more speed without dropping review rigor.
A two-week pilot, then your team owns it
We don't take over your codebase. We install the operating model, prove it on one real feature with your engineers driving, and hand it off with trained champions.
Days 1–2
Assess
Map your current SDLC and capture a baseline you can measure against.
Days 3–5
Design
Build the operating model for your stack, tools, and risk profile.
Days 6–9
Pilot
Your engineers run a real feature through it, live, with us coaching.
Day 10
Hand-off
Playbook, measured indicators, trained champions, and a rollout plan.
The metrics from a two-week pilot are diagnostic indicators, not a CFO-grade ROI study. The point of the pilot is to prove the model works on your code, with your team — then scale it in a structured rollout. Still deciding the bigger picture? Start with AI Architecture & Advisory; need the AI itself built? Governed AI & RAG.
Worried about what AI is shipping?
A 30-minute fit call. We'll look at how your team uses AI today and one workflow that keeps you up at night — and tell you honestly whether this is a fit.
Book a fit call