AI · architecture walkthrough
The model gets the headlines. But what makes an AI feature fast, affordable and reliable is everything around it. Type any question into the app below, then scroll through the nine steps at your own pace and see exactly what each box does with it.
Auth first, then a per-user token budget checked in Redis. One runaway script can burn a month’s AI bill — the limiter is the first line of defence.
Similar questions often have the same answer. A cache hit returns in milliseconds and never touches the model — the cheapest AI call is the one you don’t make.
A Spring Boot service loads the user’s session and context from Postgres, decides what to retrieve, and which tools the model may call.
Vector search finds the few chunks relevant to this question. Short, grounded prompts are faster, cheaper and hallucinate less.
Timeouts, retries with backoff, a circuit breaker, and a cheaper fallback model when the main one struggles. The model is one box; the safety net around it is backend.
Tokens flow back over Server-Sent Events as they’re generated, so the first word appears long before the answer is finished.
Token usage, cost per feature and user feedback go onto a queue. The user never waits for analytics.
New documents go through a queue to embedding workers. Jobs are idempotent, so a retry never creates duplicate vectors.
Sixteen boxes on this diagram. One of them is the model. Almost everything that makes the product fast, cheap and reliable is backend.
Built in production
I’ve built an ASR → LLM → TTS pipeline to automate support calls. In voice, every stage has to stream into the next — silence on a phone call feels like a bug.