AI features your product can actually run in production

AI product engineering is building AI features into your existing product rather than as a standalone tool. That means retrieval pipelines, LLM orchestration, model integration, and the infrastructure that keeps outputs and costs predictable when real users hit it at scale.

RAG, or retrieval-augmented generation, lets a model answer using your own data instead of only what it was trained on. You need it if your AI feature has to reference your documentation, product catalogue, or customer records. You do not need it if the task is pure generation or classification.

Through model routing, caching, prompt optimization, and setting hard usage ceilings. Most AI features are built with the strongest available model on every call, which is usually 5 to 10 times more expensive than necessary. We benchmark cheaper models per task and route accordingly.

Typically 6 to 12 weeks depending on how clean your data is. The AI integration itself is often the fast part. Getting your data into a state a model can use reliably is where most projects lose time, which is why we assess that first.

Guardrails are the controls that stop an AI feature from producing harmful, off-brand, or factually wrong output in front of users. In practice that means input validation, output filtering, confidence thresholds, and fallback paths when the model is uncertain. They are not optional in production.