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Business FinanceTop 10 Best Conversation Analysis Software of 2026
Ranked review of conversation analysis software for sales and support teams. CallMiner, Avoma, and Chorus compared on accuracy and reporting.
How we ranked these tools
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Framework: Features 40% · Ease 30% · Value 30%
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CallMiner is the best fit for enterprise contact centers that must keep QA scoring consistent with maintained interaction taxonomies, whereas Avoma works well for revenue and CX teams that want repeatable meeting review workflows and shared collaboration around insights.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
CallMiner
Guided QA review queues that map conversation findings directly to scoring and agent coaching workflows.
Best for: Fits when enterprises need consistent QA scoring and analytics tied to maintained interaction taxonomies.
Avoma
Editor pickEvaluation templates and shared review queues that structure human-in-the-loop coaching at scale.
Best for: Fits when revenue and CX teams need repeatable meeting QA with shared review workflows and integrations.
Chorus
Editor pickMoment-level transcript annotation that turns analytics outputs into review-ready, editably structured artifacts.
Best for: Fits when QA and enablement teams need review workflows with validated summaries and consistent action-item capture.
Comparison Table
CallMiner
enterpriseConversation analytics platform for contact centers.
Guided QA review queues that map conversation findings directly to scoring and agent coaching workflows.
CallMiner’s core workflow starts with ingesting interaction audio and metadata from contact center systems, then producing structured insights for post-call review and reporting. Conversation intelligence outputs are designed to drive call scoring, QA rubric tagging, and coaching review queues without manual labeling for every interaction. Human-in-the-loop review is supported through auditor workflows that attach feedback to ranked findings and themes.
A tradeoff is that effective results depend on building and maintaining the scoring and taxonomy configuration that matches business definitions. CallMiner fits best when teams need consistent QA scoring and analytics aligned to established interaction categories, not ad hoc keyword browsing. It also suits organizations that require controlled review workflows for large queues and compliance-oriented evidence trails across many agents.
- +QA workflows connect conversation findings to scoring rubrics
- +Reusable taxonomies support consistent reporting across teams
- +Human review queues reduce time spent on low-signal calls
- +Integration-oriented outputs support analytics export and routing
- –Taxonomy and scoring configuration takes sustained governance effort
- –Advanced workflows can require more admin time than lighter tools
- –Insight quality can lag when source metadata is incomplete
- –Extending logic beyond standard patterns may require engineering help
Contact center QA teams
Score calls against rubrics
More consistent QA outcomes
Sales and retention ops
Find drivers of churn and win
Faster coaching prioritization
Show 2 more scenarios
Compliance and risk owners
Review sensitive interactions at scale
Reduced compliance exposure
Governed review queues help route higher-risk calls for additional human examination.
Contact center managers
Audit performance trends by taxonomy
Clearer performance accountability
Reporting ties performance metrics back to reusable categories for ongoing operational steering.
Best for: Fits when enterprises need consistent QA scoring and analytics tied to maintained interaction taxonomies.
Avoma
SMBAI meeting assistant with conversation intelligence and analysis.
Evaluation templates and shared review queues that structure human-in-the-loop coaching at scale.
Avoma’s core workflow starts with ingestion of recorded conversations and ongoing calls, followed by transcript-based analysis that surfaces highlights and themes for faster review. Team review is organized around shared work queues and configurable evaluation templates used for call scoring and coaching. The product supports admin governance for access control, review assignments, and auditability across team workflows.
A key tradeoff is that advanced evaluation logic and consistent taxonomy usually require upfront configuration of categories, keywords, and scoring rubrics. Avoma fits best when a team needs repeatable QA and coaching across many sellers or account teams with a review process that multiple managers share.
- +Review workflows map to coaching and QA teams’ day-to-day processes
- +Strong transcript search for moments, participants, and discussion themes
- +Automation and API support integration into CRM and analytics systems
- +Admin controls cover access separation and shared review assignment
- –Rubric and taxonomy consistency takes deliberate setup and maintenance
- –Some advanced analyses require additional workflow configuration
- –Large review queues can feel slow without disciplined tagging
Sales enablement teams
QA score calls for coaching
Faster coaching feedback loops
RevOps teams
Route insights into CRM records
Cleaner lifecycle reporting
Show 2 more scenarios
Customer success leaders
Review onboarding calls consistently
More consistent customer outcomes
Teams standardize evaluation rubrics for implementation discussions and track recurring risk moments.
Quality assurance managers
Create shared QA review queues
Higher audit consistency
Cross-functional reviewers work from the same transcript views and scoring guidance for consistency.
Best for: Fits when revenue and CX teams need repeatable meeting QA with shared review workflows and integrations.
Chorus
enterpriseConversation intelligence for sales teams recording and analyzing calls.
Moment-level transcript annotation that turns analytics outputs into review-ready, editably structured artifacts.
Chorus maps transcription to a review experience where analysts can validate and edit conversation artifacts during post-call work. It supports conversation recording ingestion and speaker diarization so review teams can attribute statements to the right participant. The workflow fits quality assurance and sales enablement processes that depend on repeatable summaries and review notes rather than raw transcripts alone.
A key tradeoff is that advanced governance, deep customization, and enterprise administration often require more setup than teams expect for day-one use. Chorus works best when a single team owns the review taxonomy and wants consistent action-item capture across many interactions, not when each analyst needs a radically different schema.
- +Annotation-first review links insights back to exact transcript moments
- +Action-item extraction helps convert calls into tracked next steps
- +Speaker attribution supports targeted coaching and feedback loops
- +Automated meeting summaries reduce manual re-reading time
- –Schema customization and governance depth take planning for multi-team rollouts
- –Complex cross-system automation may require engineering support
Contact center QA teams
Post-call review with coaching notes
Faster, consistent QA feedback
Sales enablement teams
Action-item capture after sales calls
Higher follow-through on commitments
Show 1 more scenario
RevOps operations teams
Standardized call review across regions
More comparable coaching sessions
Operators enforce consistent review artifacts so managers can compare performance across accounts.
Best for: Fits when QA and enablement teams need review workflows with validated summaries and consistent action-item capture.
Verbit
enterpriseTranscription and captioning with conversation analysis.
Governed human-in-the-loop QA workflow that links reviewer decisions to transcript segments for audit-ready outputs.
Verbit focuses on automating conversation analysis pipelines around transcription, diarization, and workflow-driven review. The workflow layer supports post-call quality assurance with configurable checks, human-in-the-loop review, and exportable results for downstream systems.
Verbit also emphasizes governance for large volumes through role-based access controls and audit logging tied to review and data changes. Integration coverage centers on contact center ingestion and conversation recording sources, then pushes structured outputs for analytics and operational use.
- +Workflow review with human-in-the-loop decisions tied to transcripts
- +Role-based access controls and audit log coverage for review activity
- +Configurable QA checks for consistent call scoring inputs
- +Structured outputs designed for ingestion into analytics and CRM-related workflows
- –Set up effort rises when multiple ingestion sources and workflows must align
- –Advanced orchestration depends on integrations and supported data destinations
- –Analytics taxonomy tuning can be slower than pure auto-insight products
- –Real-time analysis coverage is narrower than post-call workflows for many teams
Best for: Fits when QA and compliance review need governed workflows over high-volume transcripts and diarized segments.
Salesloft Conversations
enterpriseConversation intelligence within the Salesloft revenue platform.
Conversation intelligence stored in Salesloft records, enabling review and coaching to stay aligned with sales activities.
Salesloft Conversations ingests recorded calls and live sessions to produce post-call conversation intelligence tied to Salesloft activity. It combines speech-to-text transcription, speaker diarization, and conversation analytics workflows that fit sales team QA and coaching loops.
Admin controls map to Salesloft roles and workflow configuration so teams can standardize review steps across accounts. The primary differentiator is how conversation insights connect back to CRM-aligned sales execution inside the Salesloft ecosystem.
- +Transcripts and diarization stay linked to Salesloft conversation records for review
- +Conversation analytics supports repeatable QA and coaching workflows with human review
- +RBAC-aligned access controls reduce accidental review exposure across teams
- +Integrations align conversation insights with sales execution rather than standalone reporting
- –Quality assurance workflow design depends on Salesloft-specific configuration
- –Deeper analytics exports can be constrained by the available integration and API surface
- –Omnichannel coverage is uneven if recordings come from non-Salesloft sources
- –Sentiment and intent style signals are less granular than call-center-focused analytics suites
Best for: Fits when sales teams need conversation review tied to pipeline execution, with standardized QA steps inside Salesloft.
Deepgram
API-firstSpeech-to-text and conversation understanding API.
Time-aligned diarized transcripts exposed through an API designed for real-time and batch ingestion into custom analytics flows.
Deepgram is a conversation analysis option for teams that want transcription and analysis built around an API-first speech pipeline. It turns incoming audio or video into time-aligned transcripts with speaker diarization, then supports downstream analytics through programmable workflows and extensibility. The clearest fit appears when conversation intelligence needs to be embedded into contact center systems, QA programs, or agent coaching tooling via automation and ingestion controls.
- +API-first transcription and analysis that fits custom call workflows
- +Speaker diarization produces time-aligned turns for QA review
- +Extensible ingestion for audio and video sources feeding analytics
- +Automation-friendly responses that support post-call pipelines
- –Governance controls like RBAC and audit logging are not the core strength
- –Advanced conversation intelligence workflows require engineering integration
Best for: Fits when transcription and conversation analytics must be programmable inside existing contact-center and QA tooling.
Dialpad Ai Voice
enterpriseBusiness phone system with built-in conversation intelligence.
Transcript-linked QA views that connect AI call insights to agent coaching and team review in the same workspace.
Dialpad Ai Voice combines call transcription with AI-driven conversation intelligence inside Dialpad’s unified communications workspace. It supports real-time analysis and post-call workflows built around transcript-backed QA, coaching, and team review.
Dialpad’s differentiation is how conversation insights connect to Dialpad contact center and messaging interactions rather than living as a standalone analytics layer. Conversation insights rely on configurable rule and model outputs for topics, sentiment signals, and structured call scoring used during QA review.
- +Real-time and post-call insights share the same transcript context for faster QA triage
- +Conversation insights map directly into coaching and QA review workflows
- +Works within Dialpad’s contact center and communications stack instead of requiring a separate layer
- +Configurable scoring and summaries support consistent team review across calls
- –Deeper automation needs access to Dialpad’s integration surfaces, not custom analytics pipelines
- –Governance options for large multi-tenant environments are less granular than enterprise QA suites
- –Some analysis types depend on model availability and accuracy at call-time
- –Advanced taxonomy and schema customization for conversation analytics is limited
Best for: Fits when contact-center teams want AI insights tied to QA and coaching inside the Dialpad workflow.
Jiminny
SMBConversation intelligence platform for sales teams.
Evidence-linked coaching views that connect review decisions to exact transcript moments during QA sessions.
Jiminny turns recorded customer and sales conversations into analyzable conversation intelligence with transcripts, highlights, and structured insights. It focuses on conversation analytics workflows built around agents, managers, and QA reviewers rather than only manual note taking.
The core value is faster review cycles using searchable moments and coaching-oriented views tied to interaction evidence. Integrations center on getting audio or conversation artifacts into the system and aligning outputs with team review routines.
- +Searchable conversation moments reduce manual scrub-through time for QA reviewers
- +Manager views support repeatable coaching feedback tied to specific transcript segments
- +Workflow orientation keeps review, scoring, and follow-up aligned to team routines
- +Conversation ingestion supports ongoing review without rebuilding analysis each cycle
- –Advanced governance controls are less granular than enterprise contact center suites
- –Automation coverage depends on available templates for specific review objectives
Best for: Fits when mid-market teams need repeatable conversation review workflows with fast evidence lookup.
Rasa
API-firstOpen-source conversational AI platform with analysis tools.
Tracker-first analytics that binds conversation state events to the same dialogue management logic used in production.
Rasa performs conversation analysis by combining tracked dialogue events with configurable NLP and machine learning pipelines. It can ingest conversation recordings for post-call review and also drive real-time intent classification and response generation using custom components.
Core capabilities include workflow-style conversation flows, event-level analytics exports, and extensibility through its component and training pipeline. Governance is handled through project configuration controls and access controls around user workspaces, rather than through prebuilt QA scorecards.
- +Event-level dialogue analytics tied to the same tracker used for bot behavior
- +Extensible pipeline for custom intent, entity, and NLU components
- +Conversation flow design enables human-in-the-loop review of dialogue branches
- +Exports conversation and training artifacts for internal quality processes
- –Conversation intelligence requires model and pipeline engineering for each domain
- –Omnichannel conversation recording ingestion is not as plug-and-play as call-center suites
- –Built-in quality assurance workflows need significant configuration to match QA programs
- –Large-scale analytics depends on external storage and analytics pipelines
Best for: Fits when teams need analyzable dialogue traces tied to a custom assistant across channels.
Gong
enterpriseRevenue intelligence platform analyzing sales conversations.
Quality Assurance workflows that attach coaching tasks to specific audio moments with transcript-level navigation.
Gong is conversation intelligence software built around call and meeting capture plus structured analysis for sales and support teams. Its core workflow centers on speech-to-text transcription, speaker diarization, and searchable call insights that connect themes, moments, and coaching actions to specific segments.
Gong also supports real-time and post-call analysis with quality assurance workflows that route findings to reviewers. Governance features include admin controls for user access and organization settings used to standardize capture and analysis behavior.
- +Fast retrieval of moments linked to transcript timestamps for QA reviews
- +Structured playbooks that convert call themes into reviewable coaching tasks
- +Strong orchestration of transcription and speaker diarization across recordings
- +Admin controls for user access and organization-level analysis settings
- –Higher setup effort for accurate mappings between channels, systems, and review workflows
- –Automation relies on configuration work that can be difficult to maintain at scale
Best for: Fits when sales and support teams need repeatable QA review workflows from captured conversations.
Conclusion
After evaluating 10 conversation analysis software, CallMiner stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right Conversation Analysis Software
Conversation analysis software turns recorded interactions into searchable transcripts, time-aligned turn data, and review artifacts that QA, coaching, and enablement teams can act on. This buyer’s guide covers CallMiner, Avoma, Chorus, Verbit, Salesloft Conversations, Deepgram, Dialpad Ai Voice, Jiminny, Rasa, and Gong, focusing on how each tool handles review workflows, automation, and governance.
The evaluation lens emphasizes integration depth with conversation ingestion and workflow systems, the underlying data model exposed to admins and automation, and the API and automation surface used to connect analytics to QA and coaching steps. The top option for consistent guided QA workflows that map conversation findings into scoring and agent coaching workflows is CallMiner, with Avoma and Chorus leading adjacent approaches to structured human review and moment-level annotation.
Conversation Analysis Software that converts call and meeting recordings into governed review and coaching workflows
Conversation analysis software ingests audio or video and produces speech-to-text transcription with speaker diarization so teams can analyze who said what, when, and how those segments map to review criteria. Tools such as CallMiner and Avoma then organize findings into scoring rubrics and shared review queues so QA reviewers can apply the same standards across teams.
Beyond transcripts, conversation intelligence features connect insights to workflows like QA evaluation and agent coaching so teams can retrieve evidence at specific transcript moments. Chorus extends that review loop by centering moment-level transcript annotation that becomes editably structured artifacts for action-item capture and consistent review outputs.
Conversation analysis features that determine QA, coaching, and governance outcomes
Conversation analysis software becomes useful for QA when it connects transcript moments to reviewer decisions, scoring rubrics, and coaching actions inside the same workflow. CallMiner leads this category with guided QA review queues that map conversation findings directly to scoring and agent coaching workflows.
These workflows also need consistency across teams, not just searchable transcripts. Avoma and Chorus both structure human-in-the-loop review, but they differ in how review artifacts are created and how moment-level context becomes review-ready outputs.
Guided QA review queues tied to scoring and coaching
CallMiner maps conversation findings into scoring rubrics and agent coaching steps through guided QA review queues, which keeps standards consistent across QA cycles. Dialpad Ai Voice and Gong also connect QA views to transcript context, but their automation depth depends more on how their review experiences are configured inside the vendor workspace.
Human-in-the-loop review templates with shared queue operations
Avoma provides evaluation templates and shared review queues that structure meeting QA with coaching workflows, while maintaining transcript search for moments and participants. Verbit also emphasizes governed human-in-the-loop QA decisions tied to transcripts and diarized segments, but it adds governance controls that matter most for audit-ready review output.
Moment-level annotation that creates editably structured review artifacts
Chorus centers moment-level transcript annotation so reviewers can create action-item capture artifacts linked back to exact transcript moments. Jiminny similarly links evidence to transcript moments for coaching views, but Chorus pushes the review output into editably structured artifacts that teams can standardize for repeatable review.
Programmable transcription and time-aligned turn exposure via API
Deepgram exposes time-aligned diarized transcripts through an API designed for real-time and batch ingestion into custom analytics flows. This API-first posture differs from CallMiner and Avoma, which focus more on organizing analytics into QA and coaching workflows than on enabling custom transcript pipelines.
Governance controls for reviewer activity and transcript-linked decisions
Verbit provides role-based access controls and audit log coverage for review activity, and it ties reviewer decisions to transcript segments for audit-ready outputs. CallMiner also requires sustained governance effort for taxonomy and scoring configuration, while Deepgram and Rasa prioritize programmable conversation data flows over enterprise governance depth.
How to choose based on review workflow design and automation ownership
Conversation analysis software options separate into two practical philosophies: workflow-first review systems that standardize QA and coaching, and programmable engines that expose time-aligned transcript turns for custom analytics. The choice determines how much admin time is spent on configuration versus engineering time for integrations.
Teams should also decide where the “review truth” lives. CallMiner and Avoma keep review structure inside guided queues tied to scoring and coaching, while Chorus turns moment-level edits into structured artifacts that teams can reuse across review cycles.
Pick the workflow philosophy: guided QA queues or annotation-first artifacts
If QA review must drive scoring and agent coaching through reviewer queues, CallMiner fits with guided QA review queues that map conversation findings into scoring rubrics and coaching steps. If review teams need moment-level annotations that become editably structured action artifacts, Chorus fits with annotation-first moment linking and action-item extraction.
Decide whether governance lives in taxonomy design or reviewer controls
If standardization depends on interaction taxonomies and scoring rules shared across teams, CallMiner and Avoma both require deliberate setup and maintenance for rubric and taxonomy consistency. If governance depends more on reviewer access controls and audit log coverage tied to transcript segments, Verbit provides role-based access controls and audit log coverage for review activity.
Choose automation ownership: configuration inside the vendor workspace or programmable APIs
If automation is expected through structured workflow configuration and shared queue operations, Avoma and Dialpad Ai Voice keep automation closer to the vendor workflow experiences. If automation requires building custom ingestion and analytics pipelines around time-aligned diarized turns, Deepgram provides API-first transcription and analysis suited to programmable call workflows.
Map evidence to the exact moment and then define how reviewers act on it
If evidence lookup must land on the exact transcript moments during QA sessions, Gong and Jiminny both attach coaching tasks or manager views to specific transcript timestamps and moments. If evidence must flow into coaching workflow objects and standardized review outputs, CallMiner and Chorus connect transcript context into scoring and action-item capture.
Align integrations to the system of record for the business workflow
If conversation records must stay inside a sales workflow system, Salesloft Conversations ties transcripts and diarization to Salesloft conversation records for review and coaching tied to pipeline execution. If the conversation data needs to bind to a custom assistant and production dialogue logic, Rasa focuses on tracker-first analytics tied to dialogue management logic for analyzable dialogue traces across channels.
Who benefits from Conversation Analysis Software built around review workflows
Conversation analysis software benefits teams that turn transcripts into repeatable evaluation and coaching cycles with evidence tied to exact moments. The strongest fit depends on whether the team operates primarily inside a QA workflow, a coaching workflow, or a programmable analytics workflow.
CallMiner and Avoma serve QA and revenue operations teams that need consistent human-in-the-loop review at scale, while Chorus and Verbit serve teams that need structured artifacts or audit-ready governance around reviewer decisions.
Enterprise QA programs that require consistent scoring and coaching standards across teams
CallMiner supports guided QA review queues that map findings into scoring rubrics and agent coaching steps, which reduces variance across QA cycles.
Revenue operations teams that run meeting QA with shared templates and repeatable reviewer queues
Avoma provides evaluation templates and shared review queues that align with day-to-day coaching and QA workflows while keeping strong transcript search for moments and themes.
Compliance-driven review operations that need governed reviewer decisions tied to transcript segments
Verbit provides role-based access controls and audit log coverage for review activity and links reviewer workflow decisions directly to diarized transcript segments.
Enablement and QA teams that need action-item capture outputs grounded in exact transcript moments
Chorus centers moment-level transcript annotation so review teams can extract action items and produce editably structured artifacts tied back to specific moments.
Teams building custom analytics around time-aligned diarized turns
Deepgram exposes time-aligned diarized transcripts through an API designed for real-time and batch ingestion, which supports custom call workflows beyond a vendor-managed QA queue.
Common pitfalls when buying Conversation Analysis Software
A common failure mode is choosing a tool that looks good for transcripts but does not provide the review workflow wiring needed for scoring, coaching, and governance. Another failure mode is underestimating the setup work needed to make review outputs consistent across teams.
These pitfalls show up most often when teams treat taxonomy and rubric configuration as a one-time step, or when they expect cross-system automation without engineering help for orchestration.
Assuming moment-level transcript search automatically produces consistent QA scoring
CallMiner and Avoma both emphasize rubric-driven review queues where reviewer decisions map into scoring or coaching workflows, while tools that focus on evidence lookup without structured scoring can leave QA teams to translate findings manually.
Underestimating the governance work needed for taxonomy and rubric consistency
CallMiner and Avoma require sustained governance effort to keep taxonomy and scoring configuration consistent across teams, so early rollouts should budget time for rubric governance and review calibration.
Overlooking how governed review output ties reviewer actions to transcript segments
Verbit explicitly ties human-in-the-loop decisions to transcript segments with role-based access controls and audit log coverage, so audit-focused programs should avoid relying on tools that prioritize transcript navigation over governed reviewer activity records.
Expecting custom analytics pipelines without API-first exposure
Deepgram provides API-first time-aligned diarized transcripts designed for programmable ingestion, while workflow-first platforms like CallMiner and Avoma focus more on QA and coaching workflow operations than on building bespoke analytics pipelines.
Assuming complex cross-system automation will work without workflow engineering
Chorus supports sophisticated annotation-to-artifact workflows, but schema customization and governance depth require planning for multi-team rollouts, and complex cross-system automation may require engineering support.
How We Selected and Ranked These Tools
We evaluated conversation analysis software across integration depth, automation surface, and governance controls that connect conversation findings to QA and coaching workflows. Features received 40% weight because transcript-linked review artifacts and guided queues are the core operational differentiator in CallMiner, Avoma, Chorus, and Verbit.
Ease and value each received 30% weight because admin setup effort affects whether teams sustain consistent review and scoring at scale. CallMiner ranked first because guided QA review queues map conversation findings directly into scoring rubrics and agent coaching workflows while reusable taxonomies support consistent reporting across teams.
Frequently Asked Questions About Conversation Analysis Software
How does CallMiner structure quality assurance workflows around conversation findings?
How does Avoma handle human-in-the-loop review for meeting insights?
What tradeoff appears with Chorus when teams review annotated moments versus only dashboards?
Which tool is most suited for governed QA workflows over large, diarized transcript volumes?
When does Deepgram’s API-first approach matter for conversation analysis deployments?
How does Dialpad Ai Voice connect call analysis to in-workspace coaching and team workflows?
Where does Jiminny typically fall short if review requires audit trails tied to governance decisions?
What breaks if Rasa’s conversation analysis needs must align with production dialogue-state logic?
How does Gong implement admin controls for standardized capture and analysis behavior?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
- Communication MediaTop 10 Best Conversation Software of 2026
- Business FinanceTop 10 Best Finance Analysis Software of 2026
- Data Science AnalyticsTop 10 Best Text Sentiment Analysis Software of 2026
- Marketing AdvertisingTop 10 Best Content Analysis Software of 2026
- Finance Financial ServicesTop 10 Best Option Trading Analysis Software of 2026
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