LLMs are easy to demo and surprisingly hard to ship. I help teams cross that gap: pick the right model, build evaluation harnesses, and design around cost, latency, and the ways these systems fail.
LLM-powered features for existing apps and new products: agent workflows, RAG pipelines, and integrations with OpenAI, Anthropic, and similar providers.
Practical integrations that ship, not demos. Prompt engineering, evaluation harnesses, and the unglamorous parts of running LLMs in production: cost, latency, and failure modes.
Anthropic Claude, OpenAI, embeddings, vector databases, evaluation frameworks, prompt caching.
Not by default. Both Anthropic's and OpenAI's enterprise APIs treat your input as non-training data. For private workloads we can also self-host an open-source model so the data never leaves your infrastructure.
With an evaluation harness built from real examples, ideally collected from your domain. Each release is scored against held-out cases for accuracy, latency, and cost so regressions show up before users do.
Treated as design constraints, not edge cases. Outputs are validated, fallbacks are explicit, and high-stakes flows have a human review step. The goal is a feature that fails predictably rather than one that fails invisibly.
Tell me what you're trying to do. I'll let you know honestly whether I'm a good fit.
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