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Conversation Intelligence

Conversation intelligence is the analytics layer that sits on top of your conversations. It answers two questions operators ask after the fact: “what did the customer say and how did it go?” and “how is my team/agent performing across all of them?” This page names what is computed, where the source data comes from, how it is stored, and how to pull it over the API.

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What gets computed

Orbit computes two kinds of signal over a conversation.

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Built-in analysis (automatic)

Each conversation in the agent-conversations surface is scored once in the background for a fixed set of signals: The raw transcript sent to the model is truncated to control cost. When no LLM provider is configured, or the LLM call fails, a status-based heuristic fills the same fields (completed → positive, failed → negative) so the dashboard never renders empty purely because the model is unavailable. These signals feed every aggregate the dashboard and the API compute: volume and duration aggregates, sentiment distribution over the window, per-channel breakdown, an agent leaderboard, daily time series, top topics, the subset of topics that are surging (recent-window volume spiked against their own baseline), escalation rate, average quality score, and two resolution metrics — resolution_rate (status-only completions, kept for back-compat) and the un-gameable solution_rate, which only counts a conversation when the customer explicitly confirmed resolution (CSAT, thumbs-up, or an affirmative confirmation) or when the analysis judged sentiment positive and the workflow completed. A period-over-period csat_delta — current-window quality score minus the prior equal window — is computed so operators see direction of travel, not a point-in-time score.

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Tenant-defined operators (configured)

On top of the built-in pass you can define your own operators — named LLM instructions such as “intent: refund, order status, or complaint” or “churn-risk flag”. Each operator has an outputType (text, boolean, number, enum, or list); enum operators carry a choices array. An optional channels list scopes which text channels it runs on. Results are recorded per conversation and are readable per thread. Because every active operator adds one classifier call per message, the number of active operators per tenant is capped. Updating an operator’s instruction, output type, or choices bumps its version and re-runs results under the new definition. Operators are managed from the Inbox endpoints (see Endpoint families below).

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Where the data comes from

The source of truth is the conversation record (the conversations model). For voice, the audio-yield lane contributes recordings on the media plane, so a call thread can carry transcripts that the same scoring pass consumes. Analysis runs in the background — a poll against the endpoint kicks a throttled scan of un-analyzed conversations, and the hourly server-side scheduler re-kicks the same scan — so the first dashboard load after traffic lands may show the newest conversations without scores yet; they fill in on the next poll.

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Storage and retention

Results are cached on the conversation record itself (metadata.ai_conversation_analysis), so a conversation is analyzed once and re-used forever — the heavy aggregate scans crack that JSONB per window rather than calling the model again. Aggregates over the window are served through a short stale-while-revalidate cache so a burst of dashboard polls collapses into at most one scan; the “recent conversations” list is read fresh every time. Operator results inherit the same per-conversation storage, so deactivating an operator (a soft delete) retains previously computed values while freeing its slot against the active-operator cap. Retention of the underlying conversations follows the conversation-archive policy; the message metadata policy governs what scalars are encoded there.

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How to query the API

The endpoint lives under /stats:
  • days — lookback window in days (default 30).
  • trigger_analysis=1 — kick the background scan without paying for the full aggregate fan-out (used by the server-side scheduler; useful in integrations too).
Responded with a single payload of every subsection — aggregates, sentiment_distribution, channel_breakdown, agent_leaderboard, time_series, recent_conversations, top_topics, surging_topics, escalation_rate, avg_quality_score. Sections are computed in parallel and a slow section degrades to its zero value instead of a 5xx, so the dashboard keeps rendering while data fills in. Viewer-tier dashboard sessions and API keys whose scope includes analytics:read can call it.

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Endpoint families

Tenant-defined operators (the custom scores above) are managed on the Inbox API: The full request/response shapes are on the Inbox endpoint reference; the operator taxonomy is shared with the voice speech-category surface.

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Privacy and regulatory posture

Analysis runs on your tenant’s data in your tenant’s schema — the model receives a transcript of that thread’s messages, and the scored result is stored back on the conversation record in your tenant schema (see tenant isolation). For voice, any recording the thread consumes is governed by your recording-consent policy — which calls are captured and how participants are announced — and by voice data residency, which pins the recordings to your region. Conversation transcripts and their scores inherit the same tenant data-residency posture as the rest of your message traffic. Deleting a conversation archive removes its cached analysis with it. As with the other compliance topics, these are tenant-owned controls — the page describes Orbit platform behaviour, not legal advice; confirm residency and consent obligations with counsel.

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Example use cases

  • QA triage — the agent leaderboard and the quality-management program turn quality_score, solution_rate, and csat_delta into a weekly review loop instead of a chore.
  • Routing signals — a surging_topics badge and the conversation.topic_spike.anomaly_detected event the server-side scheduler raises pick up an emerging contact-driver before it blows up; wire it into routing rules to shift capacity.
  • Custom signals — tenant-defined operators let you tag refund intent, churn risk, or a compliance flag, then filter the Inbox per those values.
  • Agent coaching — pair the conversation-level quality_score trend with the time series to focus coaching where scores move.
The sentiment label is an analytical judgment, not a routing guarantee — a “positive” conversation can still fail its solution_rate if it was escalated. Base routing on robust signals (status, escalation) and use sentiment as a QA/low-signal input.