AI from concept to production.

Custom AI agents, LLM integrations, RAG pipelines, and intelligent workflows. Production-grade systems — not demos.

Most AI projects stop at the demo.

The model works on five test prompts. The founder is impressed. The team builds a Slack thread of more prompts that worked. The build never ships, because nobody addressed the boring parts: how the data gets in, how the model gets evaluated, what happens when the API rate-limits, how cost scales when ten users become a thousand.

Orange County Design takes AI past the demo. Into the codebase. Into the workflow. Into the bill it generates and the time it saves.

What gets built

• Custom AI agents and assistants — Claude, GPT, open-source models. Single-turn or multi-step. Tool-using. Memory-aware.
• RAG pipelines over private data — proper chunking, hybrid retrieval, retrieval evaluation. Not a vanilla embeddings demo.
• LLM integrations into existing apps and workflows — Slack bots, internal copilots, customer-facing assistants, automated processing pipelines.
• Evaluation harnesses and observability for AI systems — the part most teams skip and regret. Production AI without evals is a coin flip.
• Cost optimization, prompt caching, and model routing — same output, lower spend. Often cuts costs 60–80% on production workloads.

Stack defaults

Anthropic Claude · OpenAI · FastAPI · Postgres + pgvector · LangGraph · LlamaIndex

The stack is opinionated for a reason. Anthropic and OpenAI cover the model spectrum. Postgres + pgvector keeps vector storage in the existing database rather than a separate service. LangGraph for agent orchestration. LlamaIndex when RAG complexity warrants it.

Different models or frameworks get used when the project demands them. The defaults exist so builds don’t restart their tooling decisions from zero.

What separates production from demo

• Evals before launch. No production AI ships without a documented evaluation set and pass/fail criteria.
• Cost monitoring on day one. Every deployment ships with token usage dashboards and cost alerts.
• Prompt caching everywhere it works. Long system prompts cached at the model level — meaningful cost reduction.
• Graceful degradation. When the API is down, the user-facing experience degrades, not explodes.
• Documented handoff. The next engineer reading the code can understand it without a Loom video.

Pricing

Custom AI builds — starting at $50,000. Every engagement starts with a Discovery + Scoping Session ($2,500, credited toward the build) that produces a written specification, milestone plan, and fixed-bid quote.

The $50K floor isn’t arbitrary — it’s the minimum that buys real production engineering: data pipeline + evals + cost monitoring + deployment + handoff. Anything cheaper is a demo, and demos don’t ship.

A note on engagement type

AI work is fixed-fee or weekly-billed, not hourly. For advisory sessions, AI Opportunity Audits, and time-boxed consulting, the rate is $225/hr.