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| Pricing | Free | Paid |
| Capabilities | 11 decomposed | 4 decomposed |
| Times Matched | 0 | 0 |
Captures audio and video from Zoom, Google Meet, and Teams calls directly through browser extension or native app integration without requiring a meeting bot to be invited. The capture mechanism operates client-side at the browser/app level, intercepting the media stream before it reaches the meeting platform's servers, then streams or buffers the raw audio/video for post-processing. This approach eliminates the need for explicit bot invitations and reduces meeting participant friction.
Unique: Eliminates bot-based recording by capturing at the browser/app level rather than injecting a participant into the meeting, reducing UX friction and meeting participant visibility compared to Otter.ai, Fireflies.io, or Fathom which use bot-based approaches
vs alternatives: Superior UX friction vs bot-based competitors because no bot appears in participant list and no explicit invite is required, though technical implementation details are opaque
Converts captured meeting audio into timestamped text transcripts with speaker identification, enabling users to search and reference specific moments in calls. The transcription pipeline processes audio post-meeting (latency unknown) and generates word-level timestamps, allowing clips and summaries to reference exact moments. Speaker attribution mechanism is undisclosed but implied by action item extraction and CRM logging features that track who said what.
Unique: Integrates speaker attribution with transcription to enable action-item tracking and CRM logging by speaker, whereas generic transcription tools (Otter.ai, Fireflies) treat transcripts as undifferentiated text without deep speaker-action mapping
vs alternatives: Tighter integration with downstream CRM and action-item systems because speaker attribution is built into the transcription pipeline rather than post-processed, reducing latency and improving accuracy of speaker-action mapping
Offers a free tier that includes unlimited meeting recording, transcription, and basic summarization without time limits or meeting count restrictions. The free tier is designed to reduce friction for individual users and small teams to adopt tl;dv before upgrading to paid features. Specific limitations of the free tier (e.g., storage limits, feature restrictions, user seat limits) are not disclosed in documentation.
Unique: Offers unlimited meeting recording on free tier without meeting count or storage limits (claimed), whereas competitors like Otter.ai and Fireflies impose strict free tier limits (e.g., 600 minutes/month for Otter.ai) to drive paid upgrades
vs alternatives: Lower barrier to entry for individual users and small teams because unlimited recording on free tier means no surprise paywalls when hitting quota limits, whereas competitors force upgrade after hitting free tier limits
Generates post-meeting summaries using AI models with user-selectable frameworks (MEDDIC, Smart AI Topics, custom) that structure the summary output to match sales, product, or marketing workflows. The summarization engine processes the full transcript and produces abstractive summaries (not just extractive highlights) in 1-5 minutes (claimed 'instantly' but latency unknown). Users can define custom summary templates via prompts, enabling role-specific summaries (e.g., 'extract only objections and how they were handled' for sales, 'extract feature requests and prioritize by frequency' for product).
Unique: Offers framework-based summarization (MEDDIC, Smart AI Topics) with custom prompt templates, whereas competitors like Otter.ai and Fireflies provide generic summaries without role-specific structuring or template customization
vs alternatives: Better for sales and product teams because summaries are pre-structured for domain-specific workflows (MEDDIC for sales, feature extraction for product) rather than generic bullet-point recaps, reducing post-processing work
Identifies commitments, tasks, and next steps mentioned during meetings and extracts them as structured action items with speaker attribution, due date inference, and optional CRM task creation. The extraction uses NLP/LLM-based pattern matching to identify phrases like 'I'll send you', 'we need to', 'by next week', etc., and maps them to speakers and inferred deadlines. Extracted action items can be automatically logged to CRM systems or exported as task lists.
Unique: Combines speaker attribution with action item extraction to automatically assign tasks to the right person, whereas generic action item tools (Otter.ai, Fireflies) extract items without reliable speaker mapping, requiring manual assignment
vs alternatives: More actionable than competitor action item extraction because items are pre-assigned to speakers, reducing manual work and improving accountability tracking in CRM workflows
Automatically logs meeting summaries, transcripts, action items, and call outcomes to CRM systems (specific platforms unknown) without manual data entry. The integration maps tl;dv outputs (summary, action items, speaker attribution) to CRM fields (call notes, next steps, deal stage, etc.) and creates or updates CRM records based on meeting participants and detected deal context. Supports auto-drafting of follow-up emails and task creation within the CRM.
Unique: Integrates meeting intelligence (summaries, action items, speaker attribution) directly into CRM workflows with auto-field population and follow-up drafting, whereas competitors like Otter.ai and Fireflies provide transcripts/summaries but require manual CRM entry or generic Zapier integration
vs alternatives: Reduces manual CRM data entry by 80%+ for sales teams because meeting outputs are automatically mapped to CRM fields and tasks, whereas competitors require copy-paste or generic workflow automation that doesn't understand meeting context
Enables full-text and semantic search across all recorded meetings to find specific topics, speakers, or moments, then generates shareable video/audio clips of matching segments. The search mechanism is undisclosed but likely combines transcript keyword matching with semantic embeddings to find conceptually similar moments across meetings. Clip generation extracts the relevant audio/video segment with context (speaker name, timestamp, summary) and produces a shareable link or downloadable file.
Unique: Combines semantic search with automatic clip generation to enable quick sharing of meeting moments, whereas competitors like Otter.ai and Fireflies provide search but require manual clip creation or don't support video clip generation
vs alternatives: Better for marketing and training use cases because clips are automatically generated from search results with context (speaker, timestamp, summary), enabling quick creation of highlight reels without manual video editing
Analyzes patterns across multiple meetings to identify trends, recurring themes, and aggregate insights, then generates custom reports via email or dashboard. The analysis engine processes summaries and transcripts from multiple meetings, applies user-defined custom prompts (e.g., 'extract all customer pain points and rank by frequency'), and produces structured reports with visualizations (charts, tables — format unknown). Reports can be scheduled (daily, weekly, monthly) or generated on-demand.
Unique: Enables custom prompt-based trend analysis across meeting archives, allowing teams to define their own analysis criteria rather than pre-built reports, whereas competitors like Otter.ai and Fireflies focus on individual meeting summaries without cross-meeting aggregation
vs alternatives: More flexible for diverse use cases (sales objection tracking, product feature extraction, marketing pain point identification) because custom prompts allow teams to define their own analysis logic rather than using pre-built report templates
+3 more capabilities
Whisper employs a transformer-based architecture trained on a diverse dataset of multilingual audio, leveraging weak supervision to enhance its performance across various languages and accents. This model utilizes a combination of self-supervised learning and fine-tuning techniques to achieve high accuracy in transcription, even in noisy environments. Its ability to generalize from a wide range of audio inputs makes it distinct from traditional speech recognition systems that often rely on extensive labeled datasets.
Unique: Utilizes a large-scale weak supervision approach that allows it to learn from vast amounts of unlabeled audio data, enhancing its adaptability to different languages and accents.
vs alternatives: More versatile than traditional ASR systems due to its training on diverse, unannotated datasets, enabling it to handle a wider range of speech patterns.
Whisper's architecture is designed to support multiple languages by training on a multilingual dataset, allowing it to accurately transcribe audio from various languages without needing separate models for each language. This capability is facilitated by its attention mechanism, which helps the model focus on relevant parts of the audio input while considering language-specific phonetic nuances.
Unique: Trained on a diverse multilingual dataset, allowing it to perform well across various languages without needing separate models.
vs alternatives: More effective in handling multilingual audio than competitors that require distinct models for each language.
Whisper's training includes a variety of noisy audio samples, enabling it to perform well even in challenging acoustic environments. The model incorporates techniques to filter out background noise and focus on the primary speech signal, which enhances its transcription accuracy in real-world scenarios where audio quality may be compromised.
tl;dv scores higher at 55/100 vs Whisper at 22/100. tl;dv also has a free tier, making it more accessible.
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Unique: Incorporates training on noisy audio samples, allowing it to effectively filter background noise and enhance speech clarity during transcription.
vs alternatives: Superior to traditional ASR systems that often falter in noisy environments due to lack of robust training data.
Whisper can process audio input in real-time, leveraging its efficient transformer architecture to transcribe speech as it is spoken. This capability is achieved through a combination of streaming audio processing and incremental decoding, allowing the model to output text continuously without waiting for the entire audio clip to finish.
Unique: Utilizes a streaming architecture that allows for continuous audio processing and transcription, making it suitable for live applications.
vs alternatives: Faster and more responsive than many traditional ASR systems that require buffering before processing.