Not a menu of services, not a linear pipeline — a sequenced system where each phase builds momentum for the next. Strategy follows proof. Every rotation compounds.
Start with something real. One working system, one data point, one thing that actually ships — before the strategy or the roadmap.
Now that we know what's real, strategy goes where the evidence leads. No strategy delivered by someone who hasn't touched your infrastructure or your code.
Working system, proven approach — now operationalize and compound. Every rotation of the flywheel builds speed and reduces organizational friction.
Each phase builds on the last. Strategy follows proof. Systems scale from the ground up, not from theory. The flywheel keeps turning.
Problem: AI engineering teams need reusable, composable skill modules that work across agent frameworks.
Built: An open-source skills repository with structured modules for Hermes Agent and other orchestration systems.
Outcome: Live codebase powering a multi-agent development squad with 10+ specialized skills in daily use.
Problem: Personal knowledge management lacks AI-assisted organization and frictionless cross-device sync.
Built: A React Native mobile app with Firebase Cloud Functions, Firestore, Auth, and OpenRouter AI proxy.
Outcome: In private beta with StoreKit IAP integration and real user feedback driving iteration.
Problem: Plant owners need a smart directory that identifies species, tracks care, and keeps plant data organized across devices.
Built: A React Native mobile app with AI-powered plant identification from photos, care suggestions, location tagging, and a searchable plant database.
Outcome: Live at gardendex.app with plant recognition, personalized care reminders, and a growing user base.
Problem: Coordinating a team of specialized AI agents across research, coding, QA, and PM tasks.
Built: A kanban-driven orchestration system with 6 agent profiles, automated dispatch, and heartbeat monitoring.
Outcome: Active pipeline delivering code changes, research, and documentation on a daily cadence.
One agent trying to be everything hits a ceiling that looks exactly like burnout. The 7-week journey from generalist amnesia to a crew of 7 that argues in ticket comments — and why the arguing is the point.
Headline API rates aren't the real cost. Cache hit rate, context window, and architecture implications matter more. Here's what to actually look at when evaluating inference at scale.
When your platform owner competes with the products on their own shelves, the shelf gets narrower. Microsoft, GitHub Copilot, and Claude Code made it visible. The playbook is older than AI — here's how to navigate it.
Let's talk about your goals and how codegrit.dev can help you move work forward.