12 Best Conversation Intelligence Software Platforms for Sales and Support

Customer conversations often disappear into disconnected recordings, meeting notes, CRM fields, support tickets, and quality-review spreadsheets. Sales managers may miss deal risks, contact center leaders may review only a small percentage of interactions, and support teams may lose context when a customer moves between channels. The best conversation intelligence software should turn those interactions into reliable insights, actions, and records without forcing teams to review every conversation manually.

For this guide, we tested more than 40 conversation intelligence, revenue intelligence, meeting intelligence, contact center analytics, and conversational AI platforms. Some platforms support sales coaching and pipeline visibility. Others automate quality assurance, guide contact center agents, summarize meetings, update business systems, or resolve customer requests directly. We shortlisted the final 12 based on workflow depth, current pricing details where available, and key features.

Best Conversation Intelligence Software at a Glance

Why Businesses Need Better Conversation Intelligence Software

A conversation may contain a customer objection, compliance risk, product issue, buying signal, support request, or operational decision. Yet that information often remains buried inside recordings, transcripts, personal notes, and disconnected systems. Better conversation intelligence software addresses gaps such as:

• Sales calls recorded without consistent summaries, coaching notes, or CRM updates
• Customer objections and competitor mentions tracked through individual rep notes
• Contact center teams reviewing only a small sample of calls for quality assurance
• Support agents repeating questions because channel history and customer context do not carry forward
• Meeting decisions lost before teams create tasks or update project systems
• Delayed follow-ups caused by incomplete notes and unclear ownership
• Customer sentiment assessed manually without consistent scoring or escalation rules
• Compliance risks discovered only after supervisors review recorded interactions
• Limited visibility into why customers contact support or why cases escalate

The right platform should match the point where conversation data loses value. A sales organization may need deal intelligence and coaching, while a contact center may prioritize quality management, compliance, agent guidance, or customer intent.

In-Depth Analysis of the Finest Conversation Intelligence Software Solutions

We evaluated each platform by the conversations it covers, the intelligence it produces, and the operational action that follows.

1. Auriga by Scry AI

Auriga Customer Support 360 is a conversational AI platform designed to handle customer queries, maintain context, trigger support workflows, and escalate unresolved cases. Enterprises can configure it with knowledge bases, policies, helpdesk records, documents, and operational systems. It can then deliver responses across chat, voice, digital avatars, portals, applications, and kiosks.

Auriga interprets customer intent, sentiment, language, urgency, and historical context before selecting an answer or action. Source-linked responses give support, compliance, and operations teams a clearer record of how the platform reached an answer. When automation cannot resolve a request, Auriga transfers the case to a human agent with the conversation history, summary, and recommended next steps.

Unlike sales-call or meeting intelligence products, Auriga participates directly in the support interaction. It is strongest where enterprises need multilingual Tier-1 support, controlled workflow execution, and auditable responses grounded in internal knowledge.

Key Features

• Multichannel support: Handles customer interactions across chat, voice, digital avatars, portals, applications, and kiosks
• Enterprise knowledge grounding: Uses SOPs, policies, knowledge bases, CRM records, helpdesk systems, and connected data sources
• Context retention: Preserves customer history and conversation context across follow-up questions and channels
• Intent and sentiment detection: Interprets customer purpose, emotional signals, language, urgency, and prior interactions
• Source-linked answers: Connects responses with supporting enterprise records for traceability and review
• Workflow automation: Triggers ticket triage, routine support tasks, system actions, and downstream processes
• Deployment flexibility: Supports enterprise cloud and on-premises deployment requirements

Pros

• Moves beyond post-conversation analysis into direct support resolution
• Connects customer intent with enterprise knowledge and operational workflows
• Preserves context when customers move between channels
• Gives human agents a structured handoff instead of a raw transcript
• Fits enterprises that need multilingual customer interaction coverage

Cons

• Quote-based pricing
• Limited third party data benchmarks

2. Salesforce Sales Cloud

Salesforce Sales Cloud provides conversation intelligence through Einstein Conversation Insights. It analyzes recorded voice and video calls, creates transcripts and summaries, and surfaces products, competitors, pricing discussions, custom keywords, next steps, and coachable moments. 

Conversation data remains connected with Salesforce records and can support reporting, CRM automation, prompts, and follow-up workflows. Its main distinction is native CRM context. Sales managers can examine conversations alongside opportunities, accounts, contacts, activities, and pipeline data rather than synchronizing a separate call-intelligence system.

Key Features

• Call transcription: Converts supported recorded voice and video conversations into searchable text
• Conversation summaries: Produces call recaps, action items, and relevant insights
• Keyword intelligence: Tracks competitors, products, pricing, custom terms, and points of interest
• Conversation Hub: Gives managers access to recent conversations, signals, metrics, and related opportunities
• Generative insights: Lets teams define prompts that extract selected information from call transcripts
• Call collections: Organizes useful conversations for coaching, examples, and team learning

Pros

• Connects call intelligence directly with CRM accounts and opportunities
• Reduces dependence on a separate conversation-data repository
• Supports workflow automation from conversation signals

Cons

• Optimized primarily for sales conversations rather than broad contact-center QA
• Edition, license, add-on, and feature requirements can complicate deployment

3. Gong

Gong captures calls, meetings, emails, and CRM activity, then converts those interactions into revenue signals. Sales leaders can track objections, competitor mentions, buying signals, next steps, rep behavior, and deal risks. Conversation data also supports Gong Forecast, Gong Enable, and Gong Engage workflows.

The platform differs from meeting assistants because it connects conversation analysis with deal execution, coaching, forecasting, and outreach. It is the best conversation intelligence software for established B2B revenue organizations that want a shared evidence layer.

Key Features

• Conversation capture: Records and analyzes calls, meetings, emails, and customer activity
• Topic tracking: Identifies objections, competitors, product references, and buying signals
• Deal intelligence: Flags inactivity, missing next steps, engagement changes, and revenue risks
• Coaching workflows: Supports scorecards, call review, rep comparisons, and performance analysis
• Automated summaries: Creates call recaps, next steps, and CRM-ready records
• Revenue forecasting: Connects conversation evidence with pipeline health and forecast workflows

Pros

• Connects conversation analysis with pipeline and forecast decisions
• Gives managers searchable evidence from actual buyer interactions
• Automates summaries and selected CRM updates

Cons

• Focuses on revenue workflows rather than automated customer support
• Requires configuration of trackers, scorecards, CRM fields, and operating processes

4. Fireflies

Fireflies records, transcribes, summarizes, searches, and analyzes team meetings. Its conversation intelligence tools measure speaker talk time, sentiment, topics, participation patterns, and other meeting signals. Teams can also extract tasks, generate follow-up content, search past conversations, and connect meeting outputs with CRM and collaboration systems.

Fireflies is broader than a basic note taker but lighter than a full revenue or contact-center platform. It fits sales, recruiting, customer success, research, finance, and internal teams that need searchable conversation records without adopting a complex coaching or QA system.

Key Features

• Meeting capture: Records supported online and phone conversations
• Transcription: Creates speaker-identified and searchable meeting transcripts
• AI summaries: Generates structured notes, key points, action items, and follow-up content
• Speaker analytics: Measures individual talk time and meeting participation
• Sentiment analysis: Identifies sentiment patterns across speakers and conversations
• Topic trackers: Monitors selected subjects, phrases, objections, and discussion themes
• AI Skills: Extracts specialized information and automates post-meeting outputs

Pros

• Makes transcripts and discussion history easy to search
• Combines notes, analytics, tasks, and follow-up outputs

Cons

• Does not provide the same deal-forecasting depth as revenue intelligence suites
• Topic tracking and analytics may require configuration before they support coaching goals

5. Spinach AI

Spinach AI focuses on the work created during product, engineering, and team meetings. It records and transcribes calls, extracts decisions, action items, owners, and blockers, then routes the resulting work into tools such as Jira, Linear, Asana, ClickUp, Slack, and Notion.

Its main difference is workflow execution after the meeting. Instead of leaving teams with only a transcript, Spinach can recommend or create project tickets and distribute assigned actions. It is strongest for agile teams that want meeting decisions reflected in project-management systems without manual copying.

Key Features

• Meeting recording: Captures supported Zoom, Google Meet, Microsoft Teams, Webex, and Slack Huddle conversations
• Meeting transcription: Produces searchable records of team discussions
• Decision extraction: Identifies decisions, blockers, and important discussion outcomes
• Action-item ownership: Assigns tasks to the people identified during the meeting
• Ticket suggestions: Recommends new Jira, Linear, Asana, or ClickUp tasks from discussions
• Meeting organization: Keeps agendas, past discussions, and meeting records accessible

Pros

• Converts meetings into assigned operational work
• Connects decisions with existing ticketing systems
• Reduces the chance that actions remain buried in meeting summaries

Cons

• Does not provide sales pipeline, deal-risk, or forecasting intelligence
• There is room to improve navigation across previous meeting notes

6. Level AI

Level AI provides a customer-experience intelligence layer for human and virtual agents. It analyzes voice, chat, email, and screen activity, and scores conversations. It also identifies customer intent and friction, guides agents during live interactions, and supports coaching after the conversation.

The platform is strongest for contact centers that want to replace sampled manual QA with broader interaction coverage. It also supports voice-of-customer analysis, real-time agent assistance, screen monitoring, autonomous AI workers, and virtual-agent evaluation.

Key Features

• Automated quality assurance: Scores interactions against configured quality and compliance criteria
• Conversation intelligence: Identifies what customers say, why they contact support, and where friction occurs
• Voice-of-customer analysis: Quantifies customer intent, sentiment, effort, and recurring themes
• Agent Assist: Provides real-time answers, prompts, knowledge, and next-best actions
• Coaching workflows: Uses interaction evidence to target agent development

Pros

• Combines real-time guidance with post-interaction analysis
• Covers both human and virtual-agent performance
• Supports regulated contact-center and compliance workflows

Cons

• Does not provide sales pipeline or revenue forecasting
• Offers little value for teams that only need internal meeting summaries
• Enterprise contact-center deployment requires QA design and system integration

7. Cresta

Cresta combines Conversation Intelligence, Agent Assist, Quality Management, coaching, and AI agents for enterprise contact centers. It analyzes human and automated customer interactions across voice and digital channels, identifies behaviors that influence outcomes. 

The platform turns those findings into scorecards, coaching priorities, and operational recommendations. It differs from post-call reporting products through its connection between analysis and live execution. Contact-center leaders can examine customer intent and friction, guide agents during conversations, and identify workflows suited to automation.

Key Features

• Conversation Intelligence: Analyzes customer interactions across human and AI agents
• Natural-language analysis: Lets users ask questions about conversation data without building reports manually
• Quality Management: Scores conversations for performance, behavior, and compliance
• Outcome correlation: Connects agent behaviors with sales, resolution, CSAT, and other results
• Agent Assist: Provides live guidance, knowledge, prompts, and summaries

Pros

• Connects real-time guidance, QA, coaching, and analytics
• Supports voice, chat, email, and digital customer-service channels

Cons

• Centers on enterprise contact centers rather than internal meetings
• Requires configuration of scorecards, behaviors, outcomes, and integrations

8. Allego

Allego embeds conversation intelligence inside a wider revenue-enablement platform. It captures and transcribes sales calls, identifies topics and buyer signals, creates summaries and action items, and connects those insights with coaching, learning, practice, and approved sales content.

Its main distinction is the link between actual customer conversations and enablement programs. Managers can create showreels, use consistent scoring, share feedback at selected call moments, and reinforce effective behaviors through learning content or role-play.

Key Features

• Conversation capture: Records and organizes sales calls and virtual meetings
• Multilingual transcription: Converts conversations into searchable transcripts
• Topic search: Finds pricing, competitor, objection, and product discussions
• AI summaries: Creates notes, action items, and call recaps
• Call scoring: Evaluates conversations against consistent scorecards
• Showreels: Curates successful call moments for team learning

Pros

• Connects call intelligence with structured sales enablement
• Gives managers multiple feedback and coaching formats
• Turns top-performing conversations into reusable learning assets

Cons

• Broader than teams seeking only meeting transcripts or call summaries
• Requires governance across learning, content, coaching, and conversation modules

9. CloudTalk

CloudTalk combines cloud telephony, call-center workflows, and AI conversation intelligence. It records and transcribes calls, creates summaries and smart notes, evaluates sentiment, extracts topics, and provides analytics for sales and customer-support conversations. Outputs can also move into connected CRM systems.

Its strength lies in analyzing conversations within the same environment used to make and receive business calls. It suits distributed sales and support teams that need international calling, routing, dialing, and voice analytics without purchasing a separate conversation-capture platform.

Key Features

• Call transcription: Converts recorded calls into searchable text
• Automatic summaries: Highlights important moments, outcomes, and action items
• Smart notes: Creates structured post-call notes for agents and managers
• Sentiment analysis: Assesses emotional direction across recorded calls
• Topic extraction: Identifies recurring subjects and important discussion themes
• CRM export: Sends summaries, notes, and conversation data into supported CRM systems

Pros

• Combines cloud calling and conversation analytics
• Fits sales and support teams with voice-heavy workflows
• Removes the need to synchronize a separate recorder with the phone system

Cons

• Does not currently provide live captions or real-time speech-to-text during active calls
• Focuses primarily on voice rather than full omnichannel interaction intelligence

10. Avoma

Avoma combines meeting recording, transcription, structured notes, conversation intelligence, coaching, and revenue intelligence. Sales teams can use live answer cards during calls, automatically score conversations, track objections and competitors, examine talk patterns, and connect meeting outputs with CRM records.

The platform sits between lightweight meeting assistants and enterprise revenue-intelligence suites. It is the right conversation intelligence software for sales, customer success, and RevOps teams that want meeting administration, real-time guidance, post-call coaching, and pipeline insight within one product family.

Key Features

• Meeting recording: Captures calls across supported conferencing and dialer systems
• AI transcription: Produces speaker-identified conversation records
• Structured summaries: Generates notes, action items, and CRM-ready meeting outputs
• Live Answer Assistant: Presents contextual answer cards during sales conversations
• AI call scoring: Scores every supported call against standard or custom scorecards
• Talk-pattern insights: Measures rep behavior, participation, objections, and topic trends

Pros

• Covers the meeting lifecycle from scheduling through coaching
• Provides real-time guidance as well as post-call analysis
• Supports customizable scorecards and sales methodologies

Cons

• Advanced conversation and revenue modules increase recurring cost
• Teams must configure scorecards, trackers, answer cards, and CRM workflows

11.Observe.AI

Observe.AI provides an intelligence layer for contact-center conversations across voice, chat, and email. It structures interaction data, extracts intent and sentiment, supports agent assistance, and connects multiple contacts about the same issue into a continuous case record.

The platform is built for enterprise support operations that need interaction coverage, quality controls, coaching, compliance, and customer-journey context. Its current architecture also extends conversation data into AI agents, copilots, performance systems, and natural-language analytics.

Key Features

• Interaction intelligence: Converts raw conversations into structured and governed signals
• Transcription and redaction: Creates searchable records while supporting sensitive-data controls
• Intent and sentiment analysis: Extracts customer purpose, emotional signals, entities, and outcomes
• Auto QA: Scores interactions against configured quality and compliance requirements
• Manual QA: Supports disputes, appeals, calibration, and judgment-based reviews
• Case Management: Connects voice, chat, and email interactions around one customer issue

Pros

• Analyzes contact-center interactions across multiple channels
• Preserves journey context across repeated customer contacts
• Supports regulated and audit-sensitive service operations

Cons

• Focuses on contact-center operations rather than sales pipeline management
• Offers little benefit for internal meeting documentation

12. Jiminny

Jiminny captures sales and customer conversations across phone, video, and email, then transcribes, summarizes, and analyzes them for revenue teams. It supports coaching, competitor intelligence, CRM logging, call review, and pipeline visibility.

The platform is strongest for sales managers who want a practical link between call intelligence, rep development, and CRM accuracy. It differs from generic meeting assistants through its focus on revenue conversations, coaching opportunities, competitor activity, and deal risks.

Key Features

• Conversation capture: Records customer interactions across phone and video systems
• Email intelligence: Includes relevant email activity within the revenue workflow
• AI transcription: Produces searchable, speaker-aware call records
• Conversation summaries: Extracts key points, outcomes, and follow-up actions
• Sales coaching: Gives managers interaction-based coaching insights
• CRM logging: Synchronizes notes, activities, and updates with connected CRM records

Pros

• Connects call review directly with sales coaching
• Captures voice, video, email, and CRM context
• Reduces manual note entry after sales conversations

Cons

• Does not provide automated customer-support resolution
• Its value depends on consistent CRM, recording, and coaching adoption

How to Choose the Best Conversation Intelligence Software

The right platform depends on what should happen after a conversation occurs. The following criteria can help buyers evaluate the complete flow from conversation capture to business action.

1. Define the Conversation Workflow

Start by identifying which interactions the platform must support. These may include sales calls, internal meetings, contact center voice, chat, email, video, or automated customer-support conversations.

Then map the required outcome. A sales team may need deal-risk signals and CRM updates while a support operation may prioritize intent detection, quality scoring, knowledge delivery, ticket creation, or human escalation.

2. Compare Real-Time and Post-Conversation Capabilities

Post-conversation intelligence supports summaries, coaching, reporting, quality reviews, and trend analysis. Real-time intelligence can guide representatives, provide answers, identify compliance risks, or recommend next actions during the interaction.

A conversation intelligence platform should match the urgency of the workflow. Teams should not pay for live guidance when summaries are sufficient, but delayed analysis may not suit regulated support or time-sensitive sales conversations.

3. Review Integration Depth

Examine how the software connects with CRM, CCaaS, helpdesk, ticketing, calendar, conferencing, knowledge-base, project-management, and collaboration systems.

Check what each integration actually transfers. A connector may capture recordings without updating contacts, opportunities, tickets, tasks, outcomes, or approval records. Buyers should define which system owns the customer record and how conversation intelligence returns verified information to that system.

4. Assess Governance and Conversation Controls

The platform should support role-based access, recording permissions, consent rules, retention policies, redaction, approval controls, and restricted access to sensitive interactions.

Managers should also be able to control scorecards, tracked topics, prompts, coaching templates, automation rules, and escalation thresholds. These controls matter when different teams use the same conversation data for sales, support, compliance, and operational reporting.

5. Evaluate Reporting and Analytical Depth

Review whether the platform reports on topics, objections, sentiment, intent, talk patterns, customer effort, agent behavior, quality scores, resolution, compliance, deal risk, and follow-up activity.

The best conversation analytics software should connect conversation signals with measurable outcomes. A dashboard that counts keywords may provide less value than one that links customer intent or representative behavior with resolution, conversion, CSAT, or escalation.

6. Examine Exception Handling

Conversation workflows rarely follow one predictable path. Customers may change topics, provide incomplete information, request restricted actions, dispute an answer, or move between channels.

Evaluate how the platform handles low-confidence interpretations, missing knowledge, failed integrations, compliance risks, and unresolved requests. It should assign ownership, preserve context, and route the interaction to the appropriate human or system without forcing the customer to repeat the issue.