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.
Structured outputs rather than parsing prose, so downstream code gets a validated object instead of a best guess. Timeouts, retries with backoff, and a fallback path for when the provider degrades — which it will. Streaming where the interface benefits from it. Cost and latency instrumentation per feature. And an evaluation harness, without which you cannot safely change a prompt or upgrade a model.
The abstraction that matters most is a thin model layer of your own. Providers deprecate versions, pricing changes, and a better model ships every few months. Teams that call the SDK directly from twenty places spend a fortnight on every migration; teams with one adapter spend an afternoon.
Empirically, on your own evaluation set, using your own prompts. Public benchmarks correlate weakly with performance on a specific task, and the ranking changes with each release. Build fifty representative cases from real user input, run all three, and measure accuracy, latency and cost per completed task together — the cheapest model that clears your quality bar wins, not the highest scorer.
Route rather than standardise. Most products have a mix of easy and hard requests, and sending everything to a frontier model overpays for the easy majority. A small model handling classification and extraction with escalation to a larger one for the genuinely hard cases routinely halves inference cost.
Treat every piece of retrieved or user-supplied text as untrusted data, never as instructions. Keep system instructions separate from content, constrain the output schema so a compromised response cannot express an arbitrary action, and never let model output directly trigger a privileged operation without validation. If the model can call tools, allowlist them and validate arguments before execution.
For anything customer-facing, add an output check appropriate to the risk: schema validation everywhere, groundedness checks where the answer must be factual, and a human checkpoint where a wrong answer is expensive.
A well-scoped integration into an existing product runs $25k-$70k over 4-8 weeks, depending on how many surfaces it touches and whether retrieval is involved. Running cost scales with usage rather than seats: most features land between $0.02 and $0.30 per request. We report cost per feature from week one, because it decides whether something should scale or be switched off.
A 30-minute call. We'll talk scope, timelines, and what a realistic first release looks like. NDA signed before we start.