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 build LLM products for 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 — which can cut production inference cost dramatically.
AI-native products — common questions
What kinds of AI products do you build?
RAG systems with citation discipline, multi-step agents with tool use, vision and OCR pipelines, and voice or realtime audio using tools like Whisper and Deepgram. Everything is built to run in production, not just demo in a notebook.
How do you stop the model from hallucinating or drifting?
We work eval-driven: we start with a golden set of inputs and expected outputs, and every prompt or model change has to beat the previous score before it ships. For RAG specifically we instrument embeddings, chunking, and rerankers and audit citation accuracy weekly.
AI features get expensive fast — how do you control cost?
Most queries do not need the smartest model, so we route by complexity (Haiku to Sonnet to Opus) and cache aggressively, which can cut production inference cost dramatically. We hand over the cost-routing infrastructure so the savings persist.
Why do AI prototypes stall before production?
The demo works, then the production version hallucinates, costs too much per session, and times out under load. Going from notebook to production is the genuinely hard part, and it is exactly the retrieval, evals, fallbacks, and cost work we specialize in.
What do you deliver on an AI engagement?
A production RAG or agent system, an eval harness with a golden set, cost-routing infrastructure, and citation accuracy monitoring. The goal is a system you can keep improving with data rather than vibes.
Is this the right fit for an early experiment?
We are most valuable when you are moving a working idea into real production use under load and cost constraints. If you are still validating whether the feature should exist at all, a lighter exploration may fit better before bringing us in.
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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.