Conversation Intelligence

What it is

Conversation Intelligence is the practice of listening to the conversations your organization is already having — with customers, with prospects, with each other in Slack and Teams — and turning them into structured signal that drives the next decision. Not surveillance. Not auto-replying. The goal is to make the patterns visible: which questions keep coming back, which workflows break repeatedly, which products customers wish existed, which agent behaviors leave customers frustrated. Once those patterns are visible, they can be fixed instead of guessed about.

Why it matters

Conversation data is the highest-fidelity signal an organization produces about what its systems know, what they fail to answer, and where operations need attention — and most of it evaporates the moment a ticket closes. The cost is real: the same question is answered three times in three channels because nobody connected the dots; a product gap shows up in fifty WhatsApp threads before it shows up in a roadmap deck; a support agent's recovery move never makes it into the playbook. A loop that captures, structures, and re-uses the signal is what makes the organization measurably better over time, not just bigger.

What we build

Opt-in listeners on the channels you authorize capture conversations with explicit consent and clear retention boundaries. A processing pipeline extracts intent, subject, sentiment, satisfaction, and tool-performance signals. A clustering layer groups bad threads by root cause. Resolved high-quality threads become knowledge updates; hard cases become eval set additions; recurring failure patterns become tickets owned by a human.

• Consent-gated listeners with retention controls
• Canonical intent (vector-deduplicated), subject, sentiment, and CSAT extraction
• Failure clustering and root-cause analysis
• Resolved threads into knowledge; hard cases into evals

Channels we onboard

Wherever conversations actually happen: support platforms (Zendesk, Front, Intercom, Help Scout, Salesforce Service Cloud), email inboxes (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 and CRM activity (HubSpot, Salesforce, Pipedrive), and voice transcripts from call platforms (Twilio, Aircall, Five9, Genesys, Dialpad). Each channel onboards with its own scope, retention, and PII rules; the extraction layer and the conversation table are shared.

How we build it

Channel onboarding is permissioned and reversible. Listeners run with the minimum scope they need and write to a dedicated table governed by Data Foundations. Extraction uses task-appropriate models routed through the platform gateway. Clusters are surfaced to product, support, and engineering owners in a weekly review; the owner decides what becomes a knowledge update, what becomes a workflow change, and what becomes an eval case.

Why closed-loop matters

Conversation data is the highest-fidelity signal an organization produces about what its systems know, what they fail to answer, and where operations need attention. Without a loop, that signal evaporates into anecdotes. With a loop, every failure either becomes a tracked improvement or a documented exception — and the rate of new failures becomes a measurable trend.

How it actually works

Channel onboarding is permissioned and reversible. Listeners run with the minimum scope they need and write to a dedicated table governed by Data Foundations. Extraction uses task-appropriate models routed through the AI Platform — small fast models for classification and structured extraction, larger reasoning models when the call warrants it. Clusters are surfaced to product, support, and engineering owners in a weekly review; each cluster has a hypothesized cause, a recommended owner, and a recommended remedy. The owner decides what becomes a knowledge update, what becomes a workflow change, what becomes an eval case, and what becomes a tracked exception.

What it works with

Conversation Intelligence is the loop that closes around everything else. It reads operational signal that lands in Data Foundations, runs extraction through the AI Platform, and produces three kinds of output that flow into other layers: knowledge updates that go back into the source graph for retrieval to find next time, eval cases that go into Benchmarks so the failure cannot recur silently, and tickets with named owners that go into Agent Workflows for systematic resolution. When the work is human-agent conversations specifically, the forensics layer also detects incidents in those threads, replays them, and extracts whether the user actually resolved their problem.

Where we draw the line

Consent and an off-switch come first: no capture without permission, no auto-replies without the same approval gates as any other workflow, and no training on customer text without a separately consented dataset. The value is the loop back into operations, not a chart on the wall. Signal for action — not surveillance.

When you should start

Signals: the same support question is answered three times because nobody connected the channels; product hears about an issue weeks after support sees it daily; an AI assistant is shipping but no one knows whether it is getting better or worse; recurring complaints are diagnosed by anecdote rather than count. Common starting points are bad-thread clustering on the last 90 days of support to find the top recurring failure patterns, a single high-traffic channel (a Slack or Teams community, an email queue, a WhatsApp business line) wired into knowledge so resolved questions stop being asked twice, or a unified sentiment and intent feed across every channel that gives product, support, and ops a shared weekly view of what the customer base is actually saying.