Deterministic code intelligence · Fleet-wide ontology · MCP tooling · Spec-driven SDLC · Distributed Systems

I build AI-native engineering systems that let AI agents do real software engineering work — not just autocomplete code. My focus is deterministic infrastructure around LLMs: intelligence layers, ontology systems, MCP tooling, agent orchestration, and spec-driven SDLC platforms that turn large codebases into queryable engineering substrates.

The thesis: most "AI for code" fails because it treats the codebase as opaque text and the LLM as a search engine. Production-grade AI engineering needs pre-computed structural understanding, deterministic retrieval, and machine-verifiable workflows — so agents operate on facts, not vibes.

• Spec-driven SDLC — drives development from specs → plans → tasks → implementation → review against machine-checkable engineering rules, so AI operates inside guardrails, not around them.
• A Claude Code ecosystem — 48 commands, Skills, Hooks, and specialized Agents for production engineering workflows.
• An MCP server — a thin-surface architecture that consolidated 100+ tools into 22 high-leverage capabilities while cutting attention overhead.
• A fleet-wide intelligence layer — indexes microservice architectures into a 112k+ node ontology that answers cross-service questions in under 100ms, with no LLM call.
• Structural intelligence — extracts and connects APIs, routes, schemas, frontend/backend relationships, infra topology, and event flows into searchable substrates.
• A measurement layer — a privacy-preserving telemetry rail feeding a warehouse + dashboards that quantify how AI-assisted development actually performs across an org.
• Cross-platform distribution — macOS + Windows + Linux, installable in one command.

large-scale software intelligence · autonomous engineering systems · developer productivity · next-generation software delivery

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