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We build AI into products the same way we build everything else: architecture first, with clear boundaries, evaluated behavior, and a human decision about whether the output is actually right — not just plausible. That holds whether we're adding a single LLM-powered feature to your existing application or building a generative-AI product from scratch.
Our work spans LLM integrations — chat, extraction, classification, summarization, and agentic workflows — retrieval-augmented generation (RAG) over your own data with proper chunking, embeddings, and vector search, and generative content systems. That includes SEO-optimized AI content pipelines that produce structured, on-brand, search-ready copy with human review built into the loop, not bolted on afterward.
We build primarily with Claude and the OpenAI models, including Codex for code-generation workflows, using the Vercel AI SDK, LangChain, and Model Context Protocol (MCP) servers where they fit. Every AI feature ships with prompt versioning, evaluations, guardrails, and cost and latency budgets — plus fallback handling for when a model is slow or unavailable.
Primarily Claude (Anthropic) and the OpenAI models, including Codex for code-generation workflows. We pick the model per task based on capability, latency, and cost, and design the integration so you can switch providers later without rewriting your application.
Yes — that's a large share of this work. We start with where an LLM actually adds value, define the data and prompt contracts, and ship it behind evaluations and guardrails so it's safe to put in front of real users.
A pipeline that generates structured, on-brand content mapped to real search intent — with proper headings, metadata, internal linking, and schema — and routes every piece through human review before it publishes. The goal is content that ranks and reads well, not bulk text that gets penalized.
Tell us what you're building — we'll help you architect it right the first time.