We design, build, and operate multi-agent AI systems — and the LLM inference & GPU/memory economics that decide whether they're affordable at scale. Enterprise systems depth meets modern AI.
Four areas that make or break production agentic AI.
Multi-agent orchestration, RAG, tool use, evals, and observability — built to run reliably, not just demo.
Profile where GPU memory goes, find the bottleneck, and cut inference cost — quantization, batching, KV-cache sizing.
Kafka/CDC streaming, idempotency, retries, back-pressure — the reliability layer agents depend on.
Deep experience across Salesforce, MuleSoft, and Heroku — wiring agentic AI into real enterprise systems.
Featured project
A small open model fine-tuned into a Salesforce Apex specialist that writes governor-limit-safe, bulkified code — and proves it beats the base model on an objective, executable eval. Try it live: type a task and watch base vs. fine-tuned answer side by side.
Architecture reviews, LLM/GPU cost audits, and custom multi-agent system development.
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