We integrate large language models into production applications — not as a chat widget bolted onto a sidebar, but as typed, guardrailed, observable API calls wired into your actual product logic. Model routing, structured outputs, cost controls, and fallback paths included.
Production integration — not a wrapper around the OpenAI API.
Pick the right model per task — GPT-4o for reasoning, Claude for long context, Gemini for multimodal, a fine-tuned small model for high-volume classification. We wire routers that pick automatically.
Zod schemas, function calling, and output parsers that guarantee your LLM returns valid JSON — not free-text you have to regex apart.
PII detection, prompt injection defense, topic boundaries, and output validators. The safety layer most weekend integrations skip.
Per-call cost logging, budget alerts, and automatic fallback to a cheaper model when the frontier model is down or over budget.
Every LLM call traced in LangSmith or Langfuse — prompt, completion, latency, tokens, cost. Debug any response in 30 seconds.
When one LLM call isn't enough — chained prompts, tool-using agents, and planner-executor loops for complex workflows.
No discovery phase that never ends. Each step has a deliverable, a date, and a demo.
Map which product features benefit from LLM intelligence. Kill the ones that don't — not every text field needs AI.
Define input/output schemas, pick models per endpoint, wire structured outputs with Zod validation.
Add guardrails, content filtering, rate limiting, cost budgets, and model fallback paths before any user sees the feature.
Deploy with full tracing, eval set, and a dashboard your product team can read without asking engineering.
Multi-model by default — no single vendor lock-in.
A 30-minute call. We'll talk scope, timelines, and what a realistic first release looks like. NDA signed before we start.