Conversation Intelligence
Turning every approved conversation — support, email, team chat, customer messaging, voice, sales — into structured signal you can act on, instead of anecdotes that evaporate when a ticket closes.
Production AI is not a prompt. It is a system of context, tools, permissions, traces, evals, and feedback loops.
Closed-loop listening
Conversations reveal what systems know, what they fail to answer, and where product or operations need attention. The loop is only valuable if it feeds back: resolved threads update knowledge sources, hard cases become eval set additions, recurring patterns become tickets with named owners, and the rate of new bad threads becomes a tracked metric.
- Intent, subject, sentiment, and CSAT extraction
- Failure clustering and root-cause analysis
- Resolved threads converted to knowledge updates
- Hard cases captured as regression eval entries
Channels in scope
Anywhere a conversation actually happens: support platforms (Zendesk, Front, Intercom, Help Scout), email (Gmail, Microsoft 365, IMAP), team chat (Slack, Microsoft Teams, Discord), customer messaging (WhatsApp Business, SMS, Telegram, Messenger, Apple Messages for Business), webchat and in-app widgets, sales tools (HubSpot, Salesforce, Pipedrive), and voice transcripts from call platforms. Each channel keeps its own scope and retention; the analysis layer and the conversation table are unified.
Consent and retention
Listeners run on the channels you authorize, with the minimum scope needed, with retention windows agreed up front, and with a clear off-switch. Conversation tables live in the governed substrate and inherit the same access controls as the rest of the data foundations.
Connection to forensics and the learning loop
Bad threads detected here are the raw material for Conversation Forensics: incidents replayed in context, resolution outcomes extracted (did the user solve with the agent, after escalation, or not at all), and the result turned into either a regression eval case, a knowledge base update, or a candidate recovery recipe for the workflow. The signal layer surfaces what went wrong; the forensics layer extracts what actually fixed it; the closed-loop layer makes the next encounter better.
What it is not
Not surveillance. Not auto-replying without approval. Not training models on customer text without a separately consented dataset. The signal layer extracts structured facts and clusters; the response loop runs through the same approval gates as any other workflow.
Related resources
Incident detection and root-cause analysis on human↔agent conversations — replaying threads, reading the context around negative sentiment, extracting whether the user actually resolved their problem, and turning the answer into a learning artifact the system can use next time.
How an AI system gets durably better at its job — not by being smarter, but by routing every production failure into either a knowledge update, an eval case, a workflow patch, or a documented exception with a named owner.
A navigable map of every system your data lives in — schemas, documents, code, tickets, events, owners, and permissions — so an AI agent can find the right source and respect the right access boundary.
A company knowledge base built so an AI system can cite real answers from it — sourced from documents, tickets, code, conversations, and structured records; chunked, embedded, permissioned, evaluated, and kept fresh on AWS.