Enterprise AI will not be won by isolated chatbots or copilots.
The next layer is a control plane for AI work: model routing, policies, telemetry,
evaluations, workflow orchestration and institutional knowledge capture.

I design and build enterprise AI platforms at the intersection of software architecture, distributed systems, applied AI and financial infrastructure.

My work turns ambiguous business problems into governable, observable and reusable AI capabilities — not demos.

Current focus areas:

• AI control planes and agentic workflow runtimes
• LLM orchestration with multi-provider abstraction, routing and cost visibility
• AI governance: policies, auditability, human-in-the-loop and evals
• Developer tooling and AI-assisted software engineering systems
• Financial-grade automation in regulated environments (CBDC, DREX, smart contracts)
• Distributed systems connecting AI, data and business operations at scale

Most enterprise AI initiatives start with isolated copilots, prompts and agents.
They quickly hit the same wall: no policies, no tracing, no approvals, no evaluations, no reusability.

The missing layer is not a better model. It's a control plane for AI work.

A control plane that handles model routing by capability, cost and latency; policy enforcement before and after execution; structured tracing and observability; human-in-the-loop gates; evaluation pipelines; workflow orchestration with retries, branching and compensation; and institutional knowledge capture across executions.

Languages · C# · JavaScript · Python · TypeScript · Go · Java · Kotlin · Swift

AI systems · LLM orchestration · Agentic workflows · RAG · Evals · Multi-provider architectures · AI governance · Observability

Platforms · Azure · AWS · Kubernetes · OpenTelemetry · CI/CD · Cloud-native · Distributed systems

Domains · Financial services · CBDC/DREX · Blockchain · ZKP · Enterprise automation · Developer tooling · AI-assisted software engineering

This profile is organized around authored systems, architecture notes and applied AI work that reinforce one direction: enterprise AI needs governable, observable and reusable execution layers.

Public repositories here should point to real code, technical writing or curated maps of AI systems architecture. Forks, short experiments and unrelated demos stay out of the showcase.

"Embeddings drift. Re-index or regret."