| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 15 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Automatically ingests unstructured documents (PDFs, reports, earnings calls, contracts) from enterprise systems and extracts structured data into spreadsheets and tables without manual configuration. The system appears to use document parsing combined with LLM-based semantic understanding to identify relevant fields, entities, and relationships, then outputs itemized data in standardized formats. Supports bulk processing of heterogeneous document types across finance, legal, and market research domains.
Unique: Operates as an autonomous agent within the proprietary Olympus platform that continuously monitors integrated enterprise systems for new documents and auto-extracts data without per-document configuration, unlike point-and-click extraction tools that require template setup per document type.
vs alternatives: Scales to heterogeneous document types (earnings reports, contracts, market data) in a single workflow without rebuilding extraction rules, whereas traditional RPA or Zapier-based extraction requires separate logic per document format.
Aggregates and synthesizes financial data across multiple earnings reports, SEC filings, and consulting reports to extract key metrics (revenue, margins, growth rates), identify management sentiment and forward guidance, and generate comparative analysis across companies or time periods. The system performs cross-document reasoning to identify trends, anomalies, and relationships that would require manual review across dozens of documents. Outputs structured financial reports and insight summaries.
Unique: Operates as a continuous agent that maintains cross-document context across an entire earnings season or competitive set, enabling comparative reasoning that identifies relative performance shifts and sentiment divergence — unlike batch extraction tools that process documents in isolation.
vs alternatives: Synthesizes insights across 50+ documents in a single analysis pass with semantic understanding of financial concepts and management intent, whereas manual review or spreadsheet-based comparison requires weeks of analyst time and misses subtle sentiment shifts.
Analyzes text content (earnings calls, news articles, market research, consumer feedback) to extract sentiment signals and identify emerging trends or shifts in market perception. The system performs semantic sentiment analysis to distinguish between positive/negative sentiment and identify sentiment drivers (specific products, features, competitive threats). Outputs sentiment trends, driver analysis, and anomaly flags.
Unique: Performs semantic sentiment analysis across heterogeneous text sources to identify sentiment trends and drivers without manual content review — unlike simple keyword-based sentiment which misses context-dependent sentiment and trend drivers.
vs alternatives: Analyzes sentiment across multiple text sources (earnings calls, news, social media, reviews) in a single workflow to identify emerging trends, whereas manual sentiment tracking requires separate tools and manual synthesis.
Aggregates consumer data from multiple sources (surveys, focus groups, social media, reviews, purchase behavior) and synthesizes insights about consumer preferences, pain points, and emerging needs. The system performs cross-source analysis to identify patterns and validate insights across data types. Outputs consumer segment profiles, need statements, and opportunity assessments.
Unique: Synthesizes consumer insights across heterogeneous data sources (surveys, social media, reviews, behavior) to identify patterns and validate needs without manual research synthesis — unlike single-source research which provides incomplete consumer understanding.
vs alternatives: Aggregates and reasons across multiple consumer data sources to identify validated insights and opportunities, whereas traditional market research requires separate studies for each data type and manual synthesis.
Analyzes content performance data, audience engagement metrics, and competitive content to develop content strategies and optimize distribution. The system identifies high-performing content themes, audience segments, and distribution channels, then recommends content topics and formats. Outputs content strategy recommendations, editorial calendars, and performance benchmarks.
Unique: Analyzes content performance and audience engagement across channels to develop data-driven content strategies without manual analysis — unlike spreadsheet-based content planning which requires manual data aggregation and pattern identification.
vs alternatives: Synthesizes content performance data, audience insights, and competitive analysis to recommend content topics and distribution strategies, whereas manual content planning relies on intuition and misses data-driven optimization opportunities.
Analyzes brand perception data from multiple sources (surveys, social media, news, competitor positioning) to assess brand positioning, identify perception gaps, and recommend positioning adjustments. The system performs semantic analysis of brand messaging and perception to identify how the brand is perceived relative to competitors and target positioning. Outputs brand perception reports, positioning recommendations, and messaging guidance.
Unique: Analyzes brand perception across multiple sources to identify positioning gaps and recommend adjustments without manual brand research — unlike traditional brand studies which are point-in-time and require manual interpretation.
vs alternatives: Synthesizes brand perception data from multiple sources to identify positioning gaps and recommend messaging adjustments, whereas manual brand analysis requires separate research studies and expert interpretation.
Integrates Athena with existing enterprise applications (CRM, ERP, data warehouses, document systems) to enable autonomous workflows that read from and write to these systems. The system operates as an agent within the Olympus platform that monitors integrated systems for new data, triggers analysis workflows, and writes results back to source systems. Supports bi-directional data flow and maintains data consistency across systems.
Unique: Operates as an autonomous agent within the Olympus platform that maintains bi-directional integration with enterprise systems, enabling workflows that read, analyze, and write data without manual data movement — unlike traditional ETL or RPA which requires explicit data export/import steps.
vs alternatives: Enables seamless integration with existing enterprise systems to automate data workflows end-to-end, whereas traditional integration approaches require separate ETL tools and manual data movement between analysis and source systems.
Analyzes contracts and legal documents using predefined or custom 'playbooks' that encode domain-specific rules, risk patterns, and compliance requirements. The system scans documents for key provisions (liability caps, indemnification clauses, termination rights, regulatory obligations), flags deviations from standard terms, and surfaces red flags for due diligence or M&A workflows. Playbooks appear to be templates that encode legal expertise without requiring manual document review.
Unique: Encodes legal domain expertise into reusable 'playbooks' that operate as autonomous agents scanning contract portfolios without per-contract manual configuration, enabling scaling of legal review across hundreds of documents — unlike traditional contract review which requires attorney time per document.
vs alternatives: Playbook-based approach allows non-lawyers to configure contract review rules once and apply them consistently across portfolios, whereas manual review or generic contract AI tools lack domain-specific risk pattern recognition and require legal expertise to interpret results.
+7 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 Athena Intelligence at 22/100. Athena Intelligence 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