ChatRecorder

ChatRecorder is the quiet half of the stack that closes the loop on quality. An AI agent is only as good as its last hundred conversations. Without recordings, teams fly blind — they can't see what customers actually asked, which answers landed, and which ones fell flat.

Every conversation an AI agent runs is captured: the full transcript, timestamps per turn, participant metadata, and audio for voice chats. The data is stored securely under the same identity and workspace rules the rest of the platform uses, so the usual access controls apply without extra setup.

Transcripts are searchable. Find every conversation where a customer mentioned a specific product, a competitor, a concern. That single capability changes how product and marketing teams talk to each other — instead of opinions, they work from evidence.

Tagging surfaces patterns. Automated tags flag common themes — pricing questions, technical concerns, cancellation requests — so the heavy analytical work is front-loaded and humans only spend time on the interesting edges.

Voice conversations get transcribed automatically, with speaker labels and timestamps that match the audio. That makes it trivial to jump to the exact moment a customer said the thing you need to hear, without scrubbing through minutes of conversation.

Tighter feedback loops follow naturally. Operators review transcripts, spot an agent behaviour that needs correcting, update the prompt or the scenario, and measure the effect on the next batch of chats. The platform closes the loop from deployment to improvement without a separate analytics tool.

Privacy matters. Recording policies are explicit, retention is configurable, and customers are informed according to the owner's compliance profile. ChatRecorder is the accountability layer underneath a fleet of AI agents — the thing that makes sure quality is measurable rather than assumed.