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| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 10 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Captures audio and conversation data from multiple input sources including native app integrations (Zoom, Teams, Google Meet), optional wearable device streaming, and direct application APIs. Uses background audio processing with automatic source detection to route conversations to appropriate transcription pipelines based on platform-specific metadata and codec support.
Unique: Combines native platform integrations with optional wearable capture in a unified pipeline, using automatic source detection and codec-aware routing rather than requiring manual selection or separate recording tools per platform
vs alternatives: Captures conversations across platforms and ambient contexts that standalone meeting recorders cannot reach, while wearables like Otter.ai's hardware require separate subscription
Converts captured audio to text using streaming transcription APIs with automatic speaker identification and turn-taking detection. Processes audio chunks in real-time or near-real-time, maintaining speaker context across conversation segments and handling overlapping speech through diarization models that identify distinct speakers without explicit labeling.
Unique: Integrates speaker diarization directly into the transcription pipeline rather than as a post-processing step, enabling real-time speaker attribution during active meetings and reducing latency for downstream summarization
vs alternatives: Faster speaker identification than Otter.ai's post-processing approach because diarization runs in parallel with transcription rather than sequentially
Generates abstractive summaries of recorded conversations using large language models with access to full transcripts, speaker metadata, and optional meeting context (calendar title, attendees, agenda). Applies prompt engineering and few-shot examples to extract key decisions, action items, and discussion topics while preserving speaker attribution and temporal structure.
Unique: Chains transcript processing with LLM summarization while preserving speaker context and temporal ordering, using structured prompts to extract specific meeting artifacts (decisions, action items) rather than generic abstractive summarization
vs alternatives: Extracts structured action items with owner attribution that generic summarization tools miss, because it uses specialized prompts for meeting-specific patterns
Indexes transcribed conversations using vector embeddings (semantic search) and traditional full-text search, enabling users to find past discussions by meaning rather than exact keyword matching. Stores embeddings in a vector database with metadata (speaker, timestamp, meeting context) and supports hybrid search combining semantic similarity with keyword filtering for precise retrieval.
Unique: Combines vector embeddings with full-text search and conversation metadata filtering in a unified index, enabling semantic queries that also respect temporal and speaker context rather than treating all matches equally
vs alternatives: Faster retrieval than re-reading transcripts and more contextually relevant than keyword-only search, because it understands meaning while preserving metadata filtering
Aggregates recorded conversations from multiple sources (Zoom, Teams, Slack, email, wearable) into a unified timeline indexed by timestamp and participant. Deduplicates overlapping recordings (e.g., same meeting captured from multiple devices) and correlates related conversations across platforms using participant matching and temporal proximity heuristics.
Unique: Deduplicates and correlates conversations across platforms using participant matching and temporal heuristics rather than requiring manual linking, creating a unified interaction history that spans fragmented communication channels
vs alternatives: Provides cross-platform conversation context that single-platform tools cannot offer, while deduplication prevents duplicate summaries and search results
Parses transcripts and summaries to identify action items, commitments, and decisions using NLP pattern matching and LLM-based extraction. Extracts task description, implied owner (speaker who committed), deadline (if mentioned), and priority, then optionally integrates with task management systems (Notion, Asana, Linear) to create actionable items without manual entry.
Unique: Extracts action items with speaker-based owner assignment and integrates directly with task management systems, reducing the gap between meeting and execution rather than just listing items in notes
vs alternatives: Automatically assigns tasks to the person who committed rather than requiring manual reassignment, and pushes to task systems without copy-paste
Offers on-device recording and transcription options that keep sensitive audio and transcripts local rather than sending to cloud APIs. Uses local speech-to-text models (Whisper, etc.) and optional end-to-end encryption for cloud storage, with user control over which conversations are processed locally vs. cloud-based for performance tradeoffs.
Unique: Provides user-controlled hybrid mode allowing per-conversation choice between local and cloud processing, with E2E encryption support, rather than forcing all-cloud or all-local architecture
vs alternatives: Enables privacy-sensitive use cases that pure cloud solutions cannot support, while maintaining performance for non-sensitive conversations
Integrates with compatible wearable devices (smartwatches, AI pins, glasses) to capture ambient conversations and background audio without explicit app activation. Handles battery optimization through intelligent recording scheduling, audio compression, and periodic syncing to phone/cloud, with user controls for when recording is active (e.g., during work hours only).
Unique: Integrates wearable capture with intelligent battery optimization and user-controlled recording scheduling, enabling ambient conversation capture without constant drain or privacy violations
vs alternatives: Captures informal conversations that meeting-only recorders miss, while wearable-specific solutions lack the full Limitless pipeline (transcription, search, summarization)
+2 more capabilities
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Supabase scores higher at 42/100 vs Limitless at 24/100. Limitless leads on quality, while Supabase is stronger on ecosystem. Supabase also has a free tier, making it more accessible.
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Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
+1 more capabilities