AI-native products
RAG, agents, vision, voice — practical and cost-aware.
AI product development is the engineering work of shipping LLM-powered features into production — covering retrieval-augmented generation (RAG), tool-using agents, vision pipelines, evaluations, and cost-aware model routing.
Why this work matters
The demo works. The prod version hallucinates, costs $40 per session, and times out under load. Going from notebook to production is the hard part — and it's where most AI startups stall for two quarters.
The work, in detail.
- RAG with citation discipline
- Multi-step agents with tool use
- Vision & OCR pipelines
- Voice & realtime audio (Whisper, Deepgram)
- Cost-aware model routing
- Evals + golden-set regression
- →Production RAG / agent system
- →Eval harness + golden set
- →Cost-routing infrastructure
- →Citation accuracy monitoring
We've shipped 30+ LLM products into production. Most of what's hard isn't the prompt — it's retrieval discipline, evals, fallbacks, and cost.
The approach.
Eval-driven dev
We start with a golden set of inputs + expected outputs. Every prompt or model change has to beat the previous score. No vibes.
Retrieval first
RAG quality is mostly retrieval quality. We instrument embeddings, chunking, and rerankers — and audit citation accuracy weekly.
Model routing for cost
Most queries don't need the smartest model. We route by complexity (Haiku → Sonnet → Opus) and cache aggressively. Production cost typically drops 60–80%.
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The cost of waiting
is your competitor.
Every 90 days you delay is 90 days of authority compounding for someone else. Get the audit. See the math. Then decide.