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Production AI systems
Most AI work fails after the demo. We design the system around the model: retrieval quality, tool use, evals, fallbacks, tracing, and the operational habits that catch drift before customers do.
- Architecture for RAG, agents, and tool-using systems
- Evaluation harnesses tied to product quality, not vibes
- Observability, incident paths, and model-change playbooks
- Cost and latency envelopes you can actually operate