What Is Conversation Intelligence
Conversation intelligence analyzes conversations to produce reviewable information such as summaries, topics, stated commitments and unresolved questions. The source may be an authorized text transcript or an already collected conversation record. Analysis is different from conducting the conversation: a generated insight needs evidence from the source and does not prove that an action was completed.
By Ai Flowi · Published
What is the difference between conversation intelligence and conversational AI?
Conversational AI concerns interpreting and responding within dialogue. Conversation intelligence concerns examining that dialogue. An assistant can answer a product question while a separate analysis stage summarizes the exchange later. A summary should preserve what was said, including uncertainty, rather than rewrite the exchange as a completed sale or resolved case.
Analysis can support customer conversation summaries and quality-assurance review. It can identify passages that merit attention, but interpretation is not an objective performance verdict. A short transcript, missing speaker information or incomplete context can change the meaning of a statement. Keep source quality and analysis quality separate.
Build an evidence contract for the analysis
The evidence table scrolls within its region; focus it and use arrow keys to inspect columns beyond the screen.
| Analysis item | Supporting source | Limit to preserve |
|---|---|---|
| Customer request | The relevant utterance and its conversation reference. | A request is not proof that the service was supplied. |
| Stated commitment | The exact passage and identified speaker, where available. | A tentative suggestion is different from a firm commitment. |
| Unresolved question | The question and the subsequent source context. | An excerpt ending early cannot prove the question stayed unanswered. |
| Review flag | A passage and the stated review criterion. | A flag invites examination; it is not an automatic misconduct finding. |
| Aggregate topic | Included record identifiers and selection rules. | A selected sample does not describe every customer conversation. |
Choose an explicit unavailable state when the source cannot support a field. A fabricated timestamp or inferred speaker makes an analysis harder to audit. If the transcript does not include usable source spans, preserve another agreed reference that lets a reviewer locate the passage. The source format determines what traceability is possible.
A small summary example
Imagine an authorized synthetic transcript where a customer asks about a delivery issue and the representative says they will investigate. A useful summary states the request, the investigation commitment and any question that remains unresolved in the supplied text. It should not state that the parcel arrived or that a refund was granted when neither event appears in the source.
The intended output is a review artifact with a concise summary and references to supporting passages. A reviewer can compare the wording with the transcript and correct a mistaken interpretation. This is an illustrative analysis task, not a recorded sales call, completed customer case or measured improvement.
Catalogue roles in AI Flowi Workflow
Follow customer service quality assurance for transcript-processing configuration and structured output for the single-record extraction interface.
The analysis task here distinguishes three evidence states. In a synthetic exchange, the customer says, “I have not received the parcel,” and the representative replies, “I will ask dispatch to investigate; I cannot confirm a replacement today.” A finding that the representative committed to an investigation is supported. A finding that the representative confirmed a replacement is contradicted by the explicit qualifier. A finding that dispatch later delivered the parcel is unavailable because the supplied exchange contains no subsequent event.
These states should remain distinct in the review artifact. Contradicted means the source contains evidence against the proposed finding. Unavailable means the supplied source cannot settle it. Reporting both as false would turn absent evidence into a denial of something that may have happened later. Reporting both as unknown would conceal an explicit contradiction that a reviewer can already inspect.
Attach the relevant speaker and source passage to each finding. The customer’s statement supports a report of non-receipt by the customer; it does not independently establish the carrier’s delivery record. The representative’s promise supports a future investigation commitment; it does not show that the investigation began. These distinctions let a reviewer choose a follow-up question without expanding the summary beyond the conversation.
Now add a later representative turn: “Dispatch has not replied yet.” The analysis can support that reported lack of reply at that point. It still cannot infer what happened after the excerpt ends. If another turn corrects an earlier statement, preserve the chronology and qualify the final finding rather than treating every turn as an equally current fact.
Batch Run, Structured Output and Table Operations are documented in the catalogue; not production-tested by Ai Flowi for this evidence exercise. A returned analysis table is a container for findings to inspect, not a validation of their evidence status.
Prepare authorized text before involving a model
Decide which conversations may be analyzed, who may read the output and which details are necessary for the task. Use synthetic text for an initial exercise. Exclude unnecessary personal or sensitive information before sending data to a provider, and agree on retention through your actual organizational process. This page does not provide legal or compliance advice.
Keep a stable conversation reference and distinguish the raw transcript from a cleaned version. Removing hesitation, repetition or negation can alter meaning. If text was transcribed from audio, transcription errors remain a source limitation. Automatic call recording, inbox export and native speech analytics are not established by the components discussed here.
Use your own provider account and the API key or other credentials required by the selected service. The model configuration guide explains available credential mechanisms. A configured credential does not establish authorization to analyze a particular conversation.
Customer service quality-assurance checks
Write the review criterion before requesting a model judgment. “Find the passage where the representative promised a follow-up” is more inspectable than “rate the conversation”. Test examples where the promise is explicit, absent, negated and made by the customer instead. The required result is correct evidence selection or an unavailable finding.
Include incomplete transcripts and ambiguous speaker labels. Compare generated summaries with source text and check omissions as well as invented statements. Have a person examine disputed findings before they affect a customer record or staff assessment. Do not treat a model-generated confidence value as a measured probability without a separate validation method.
Aggregating topics without overstating the result
When grouping analysis rows, define which records were included and how duplicates are handled. A repeated export can count the same exchange more than once. A topic present in the analyzed subset may be useful for review, but it does not establish organization-wide prevalence. Keep the selection rule attached to the report.
The intended aggregate is a traceable set of review topics with source references, not a fabricated metric or performance ranking. For examples of separately documented processing tasks, consult the use-case library. The AI Agent setup and training page explains a service offer rather than a verified conversation-intelligence product.
Common mistakes and when not to use it
- Replacing evidence with polished prose. Every substantive finding should remain traceable to the supplied conversation.
- Conflating a commitment with fulfillment. Completed actions need destination evidence.
- Using analysis flags as automatic staff verdicts. Preserve context and human review.
- Assuming transcript ingestion includes recording. Acquisition, authorization and transcription are separate tasks.
Manual review may suffice for a small set of sensitive conversations. Avoid model analysis when source access is unauthorized, context is too incomplete or findings cannot be challenged against the original record.
Go, Plus, Pro 100, Pro 200 and Pro 500 support different workflow needs. Included Flowi Tokens each month: Go 5,000; Plus 10,000; Pro 100 50,000; Pro 200 100,000; Pro 500 250,000. Each node run uses 1 Flowi Token. Choose around the steps your task executes, including branches and repeated work. Book a demo.
Related terms
CRM automation concerns permitted customer-record updates. Intelligent-agent examples explain actions, and conversational AI explains the dialogue being analyzed.