Velocity up, trust down? You’re seeing the same AI pattern as everyone else.

AI Code Moving Fast—but Your System Feels Fragile?

I run a 4-week AI-native pilot to fix one painful problem in your codebase and install guardrails so you can ship faster without breakage.

Common pattern I see:

Velocity jumps with Copilot / Cursor / Claude…
…but race conditions, regressions, and performance cliffs creep in.
Architecture drifts, and you lose confidence in shipping.

I help teams tame that chaos. Using a deterministic AI-native methodology I developed while building MetaCurtis, I tackle one gnarly problem, stabilize it, and leave you with patterns your team can reuse.

Recent runs: 2–3 incidents a week down to 0; a 3-week estimate done in 3 hours with zero regressions. I’m the calm Marine-built specialist you bring in to make AI-driven systems feel safe to ship again.

The thing that always breaks right before a release.
The real-time view that randomly freezes under load.
The feature only 1–2 senior engineers are brave enough to touch.

🚨 Incidents: 2–3/week → 0 after installing single-writer governance
⚡ Velocity: 3 weeks of planned work → 3 hours with zero regressions
⚙️ MetaCurtis: WebGL engine with 15k+ particles @ 60 FPS built via AI guardrails

Apply for Pilot See MetaCurtis in Action

🎯 4-week pilot • 1 focused problem • 3 slots at a time • 🪖 Marine Corps veteran — calm in high-pressure builds • See the engineering patterns

The AI-Native Pilot (4 Weeks)

One painful problem. Four weeks. Real code in your repo, not a slide deck.

What we do in the pilot

Identify a single high-impact problem (race conditions, performance, complex feature, or architecture risk).
Instrument & map the current behavior so we’re not guessing.
Design a deterministic AI-native workflow (patterns, contracts, tests) around that problem.
Implement the fix in your codebase with your stack (pairing with your team as desired).
Document the patterns so your engineers can re-use them without me.

⏱ Approx. 4 weeks 👥 You + 1–3 engineers 💬 Weekly syncs + async support

Best fit if you are:

A startup or scale-up already using AI tools heavily (Copilot, Cursor, Claude, etc.).
Shipping a complex frontend or real-time system (React, WebGL, dashboards, visualizations).
Feeling the pain of instability, regressions, or “we move fast but don’t trust our releases.”

When Systems Broke Down — and How We Brought Them Back

Two real crises: one system frozen after a major refactor, another considered “too risky to touch.” Here’s exactly what we did — and the before/after in human terms.

Migration Recovery

Pattern S Refactor: From Frozen System to Predictable Releases

The Crisis:

After a large refactor split ~10K lines of code across modules (ConsciousnessEngine, TheaterDirector, BeatBus, WebGL renderer), everything started to wobble. Particles would freeze, narration fell out of sync, and the event bus spammed noisy, half-broken payloads. Engineers were afraid to touch the rendering path because every change risked another late-night incident.

What We Actually Did:

We applied Pattern S (single-writer governance) and rebuilt the system around clear ownership. I mapped who was allowed to write to which layer, added runtime guards around the event bus and renderer, and wired CI checks to reject any change that violated those contracts. We also added lightweight probes (BeatBus taps, renderer diagnostics, window.probe) so we could see exactly where behavior diverged in real time instead of guessing.

Before / After (In Human Terms):

8–16× Faster debug cycles (multi-hour “what is even happening?” hunts → 25–40 min targeted fixes)

4–6× Faster feature delivery (8–12 hr “do we dare touch this?” changes → 2–3 hr confident updates)

100% Incident reduction (2–3 production issues per week → 0 over the next cycle)

$273K Annualized labor savings from regained engineering time and fewer fire drills (modeled)

Technical Wins:

Single-writer contracts enforced at runtime and in CI, so ownership drift can’t sneak back in.
Automated ownership manifest generation (docs/OWNERSHIP.md) to keep the mental model in sync with the code.
Evidence-first debugging via BeatBus taps, renderer probes, and window.probe hooks.
Zero-regression rollout guarded by pre-commit and pipeline validation.

Velocity Sprint

Blueprint Pipeline: 3 Weeks of Work in 3 Hours (Without Breaking Anything)

The Situation:

The blueprint pipeline sat at the heart of the system: 14 architectural pieces touching narrative, typography, visual effects, caching, and orchestration. Everyone agreed it needed an overhaul, but it was considered “too risky to touch” without weeks of work and a full freeze on new features. Estimated effort: 38–49 hours of senior engineering time.

What We Actually Did:

I treated the work as an AI-native velocity sprint. We grounded everything in a single-source-of-truth spec (SST), broke the problem into tightly scoped task blocks, and paired human judgment with multi-agent AI for implementation. After each block, we ran fast validation against the spec and existing behavior to catch drift immediately, not days later.

Before / After (In Human Terms):

60–80× Velocity multiplier (3 hours of focused work vs. a 38–49 hour estimate)

51 min Elapsed time to complete the heaviest Phase 1–3 changes safely

14 Architectural systems touched in one coordinated pass, without fragmentation

0 Regressions after merge—no surprise breakage, no “mystery side effects”