LLM and AI integration for PHP and Symfony applications

The prompt is the easy part. What decides whether an LLM feature survives is everything around it: what it costs at real volume, how it behaves when the provider is slow, what happens when it returns something malformed, and how you know it got worse after a change.

That is the part I own.

What this looks like

• In-app assistants streaming over SSE, with provider-neutral tool use so you are not rebuilding the feature when you change models.
• Retrieval over your own content — knowledge-base lookup and live introspection of application data, rather than a model guessing.
• Structured extraction — turning CSV, Excel, PDF and DOCX uploads into typed, validated application data.
• Server-side model routing, role-based gating and cost budgeting, so a feature cannot quietly become your largest line item.
• Evals — the thing that separates "we changed the prompt" from "we improved the feature".

Where I fit

I am a backend engineer who ships AI features, not an AI specialist who also writes some backend. If the feature has to live inside an existing PHP or Symfony application, talk to the person who has done the integration rather than the model work in isolation — the integration is where these projects actually fail.