AI-driven research for crypto and U.S. equities with licensed market data and transparent methodology
Discovery engine for unidentified data phenomena
Stage
Not given
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Founded
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Based in
US
US
Pricing model
Freemium
Contact for pricing
Pricing
Freelancer: $0/mo (500 credits at sign-up, up to 1,000 once you verify); Space Analyst: $19/mo (1,500 credits/month); Galactic Merchant: $39/mo (4,000 credits/month); Interstellar Financier: $79/mo (10,000 credits/month); Patron of Science: $199/mo (30,000 credits/month)
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Key features
Technical analysis examining price, volume, momentum, trend and volatility
Fundamental analysis of tokenomics, protocol revenue and equity financials
News and sentiment analysis across markets
On-chain data analysis and whale activity tracking
Derivatives and volatility research with options analysis
Portfolio tracking with account and wallet connections
SAGE research assistant with plain-language summaries
Educational classroom features with gradebook and tests
Support for seven data shapes including time series, events, networks, and spatial measurements
32 categories spanning domains from financial markets to genomics
Discovery engine for changing relationships and recurring behaviours
Reproducible execution with documented model configuration and runtime context
Confidential compute options including Intel TDX, AWS Nitro Enclaves and AMD SEV
Developer API and GitHub integration for agent-based exploration
Support for investigating evidence and testing explanations
What makes it different
Every figure is computed from licensed market data and traceable to its source
Deterministic methods that return the same result every time for reproducibility
Transparent confidence levels and stated limitations on every report
User maintains full control with no money management or trade execution by the platform
AI integrates into existing assistants like ChatGPT rather than requiring a separate tool
Separates phenomenon discovery from explanation, allowing rigorous investigation of root causes
Retains all outputs alongside original data for inspection and reproducibility
Works with existing data without requiring pre-identification of phenomena
Keeps execution accountable through documented model identity and configuration
Kavara™