AI App Development Company That Actually Ships

A functional AI MVP — chat, agent, RAG over your data, or an LLM-driven workflow — is buildable in 14 to 28 days when scope is frozen. Sprint 1 ships the core LLM loop (retrieval, prompts, evals, a usable UI). Sprint 2 hardens the edges: cost guardrails, streaming, auth, billing, and the observability you actually need in production. Most "6 month AI app" timelines are scope problems, not technical ones.

TypeScript end-to-end. Next.js or Remix on the frontend with the Vercel AI SDK for streaming UIs. Node.js or Python backends depending on the model tooling. For LLMs: OpenAI, Anthropic Claude, and open models via Bedrock or Together. For RAG: pgvector or a managed vector DB, with hybrid retrieval and reranking when accuracy matters. For agents: tool-calling with strict schemas, Temporal or LangGraph for long-running workflows, and per-step evals so you can ship without flying blind. Everything is observable through Langfuse or Helicone with cost dashboards from Day 1.

Customer-facing AI products: chat copilots, AI agents that take actions on behalf of users, RAG-powered internal search and knowledge tools, voice agents, AI-assisted workflows inside Shopify or SaaS products, and AI-first mobile apps. I do not take on pure research, training-from-scratch, or computer-vision-heavy projects — those need a different team. If your product wraps a foundation model with a real workflow, that is exactly the work I ship.

Three layers. First, model routing — cheap, fast models for classification and fallback, frontier models only for the steps that need them. Second, prompt and context budgets — every call has a hard cap on tokens, and prompts are versioned so we can compare cost and quality across changes. Third, full observability with per-feature, per-user cost dashboards on Day 1 — you should never be surprised by a bill. The AI API Cost Calculator on this site is the same model I use for client estimates.

Every AI feature ships with an eval set written before the feature exists. We measure accuracy on a fixed test set on every prompt or model change. For RAG, that means retrieval@k metrics plus answer-grounding checks. For agents, that means tool-call success rates and end-to-end task completion. We use confidence thresholds, structured outputs (JSON schemas with validation), and explicit fallback paths instead of hoping the model "gets it right."

Yes. Mutual NDAs are standard before we get into the specifics. The first 30-minute call is a fit check — pricing, timeline, and high-level architecture — and almost never requires one. Once we move to a detailed scoping doc, an NDA is signed before I see any proprietary data, customer lists, or unreleased product detail.

Yes. 100% of the code, prompts, eval sets, infrastructure-as-code, and documentation is yours from Day 1 — clean repo, README, deployment runbook, and onboarding notes for the next engineer who touches it. There is no Week One Labs "platform" you depend on. You can take it in-house or hand it to another team whenever you want.