Six months of chat history is only useful if you can find things in it. Nobody has ever gone back and tagged a year of transcripts by hand. ZChat's AI writes a short summary and a handful of tags for each conversation as it ends - so the archive becomes something you can filter and actually learn from.
Each finished conversation gets a few short tags, shown in your session list. Filter by one and you have every chat about that topic - without anyone having categorised anything by hand.
A couple of lines describing what the customer wanted and how it ended, stored with the conversation. Reviewing yesterday's chats stops meaning reading every transcript in full.
Summarising happens once the chat is over, in the background. Your agents never wait for it, and a slow model can't hold up a live conversation.
The value isn't tidiness - it's the questions you can suddenly answer.
Those most-common tags are also the best candidates for your AI knowledge base.
Tags are ordinary text on the conversation. Edit them, add your own vocabulary, or bulk-edit across many sessions at once. The AI is doing the work nobody was going to do by hand - it isn't claiming to know your business better than you do.
Fails quietly
If the model is off or unavailable the conversation is stored exactly as normal, just without a summary. Nothing is ever lost waiting on the AI.
Whole transcripts
This feature reads entire conversations, so the local-model option matters more here than anywhere else. See local or hosted.
ZChat gives you the installable server, web dashboard, website widget, and desktop agent tools in one self-hosted product you buy once and keep. Run it on infrastructure you trust and connect AI only if and how you want it.
Deployment
Install on Windows or Linux, behind IIS or Nginx, in a VM, or in Docker if that fits your stack.
Commercial model
One-time purchase, perpetual license, and no monthly per-agent bill attached to growth.
AI Flexibility
Use Ollama locally or connect OpenAI and Anthropic with your own provider accounts.