Production AI for regulated teams

Multi-agent orchestration that replaces brittle, human-stitched internal processes with supervised, deterministic execution. We design mesh fabrics where each agent role is bounded, observable, and recoverable, not a black-box chain of LLM calls.

• Microsoft AutoGen and LangGraph mesh topologies with deterministic state machines
• Azure AI Foundry tool binding, policy gates, and managed identity integration
• OpenTelemetry trace propagation across agent handoffs and tool invocations
• Human-in-the-loop checkpoints with immutable audit logs before side effects

AutoGen · LangGraph · Azure AI Foundry · Semantic Kernel · OpenTelemetry

Validation layers that sit between the LLM and the user. Structural output is guaranteed, hallucination is contained, and regulated data never leaves its boundary. Every response passes through schema enforcement before it reaches a downstream system or human reviewer.

• Strict serialization schemas enforced at inference boundaries (Zod, Pydantic, JSON Schema)
• Hallucination filters with confidence thresholds and citation-required outputs
• Prompt-injection, PII egress, and tool-use policy controls with deny-by-default routing
• SOC 2 and HIPAA-aligned isolation patterns with semantic cache invalidation on policy drift

Guardrails AI · NeMo Guardrails · Azure Content Safety · Pydantic-AI · Zod

High-scale semantic memory pipelines built on hybrid vector architectures, engineered to hold latency and cost under real enterprise query load. We treat retrieval as infrastructure, not a notebook demo, with strict serialization schemas on every chunk boundary.

• Hybrid vector retrieval across pgvector and Qdrant with dense-sparse fusion
• Semantic caching layers with TTL-aware invalidation and query fingerprinting
• Re-ranking, query rewriting, and citation tracing under latency budgets
• Cost-aware index design with OpenTelemetry metrics on retrieval fan-out

pgvector · Qdrant · LlamaIndex · Azure AI Search · Redis LangCache

Early-stage exploratory work on edge optimizations and post-2027 advantage positioning. We run bounded R&D engagements for partners who need feasibility signal before committing production budget. Work is anchored in the Greenwood Cyber + AI Lab ecosystem in Tulsa.

• Edge inference optimizations: quantization, speculative decoding, and KV-cache tuning
• Quantum-classical hybrid feasibility studies across VQE, QAOA, and Grover-style search
• Advanced engineering paradigms: event-sourced agent state, CRDT sync, WASM sandboxes
• Azure Quantum and IBM Qiskit prototyping with classical-quantum integration patterns

Azure Quantum · Qiskit · ONNX Runtime · WebGPU · PennyLane