LogiChat is a chatbot platform. Customers upload their docs, get a chat widget, never touch a model. For two years, "training the bot" meant hand-curating a Q&A list in a dashboard form — question and answer pairs, one row at a time, dumped into the prompt as few-shot pairs. I just deleted that and rebuilt the whole pipeline on Google's Agent Development Kit (ADK), with Vertex AI for the model, Firestore for vector storage, and Cloud Run for everything else.
It's not a "hello world" agent demo — it's the architecture I landed on after rebuilding the whole pipeline over the last few months, three Cloud Run services touched (the fourth, the Stripe top-up token subscriber, was untouched), and the one design choice that turned out to matter more than anything else: retrieval as a pre-model step, not an ADK tool.
The stack, end to end
The pipeline below touches three of those services — apps/api, apps/subscribers/doc-processor, and apps/agent — plus the Firestore vector index. apps/api appears twice in the diagram because it acts as both the upload ingress AND the gateway that mints ID tokens for the agent. Click through the five stages to see which service is active at each hop and what payload crosses each boundary:
