Kadoa

Kadoa offers several key features:

• Self-serve dataset building: Analysts can describe data needs in plain language and get structured datasets in minutes, with no code required.
• Native ETL support: Engineers can write and run ETL code directly in Kadoa, with scaling, monitoring, and self-healing handled automatically.
• Cloud-native delivery: Data can be pushed directly to S3, Snowflake, BigQuery, or other data warehouses without glue code.
• Real-time monitoring: Users can receive real-time updates on market-moving data changes via Slack, email, or webhooks.
• Source-grounded outputs: Every data value is traceable to its exact source page, paragraph, or cell, ensuring audit readiness.
• Data quality checks: Automated validation checks for completeness, plausibility, and schema adherence on every run.
• Self-healing workflows: When a workflow breaks, Kadoa detects and fixes the code automatically, with all fixes logged.
• Transparent error handling: If automated recovery fails, users are notified with full context.
• Observability: A dashboard tracks success rate, throughput, and incidents, with options to stream metrics to external monitoring stacks.

Kadoa is used for various financial data needs, including:

• Investment research: Building datasets for analysts to source web data independently, reducing reliance on central data teams.
• Real-time alerts: Monitoring websites for market-moving changes and receiving alerts before they appear on traditional platforms like Bloomberg.
• Documents and filings: Extracting and normalizing data from cross-regional company filings, providing better coverage than traditional data providers.
• Portfolio monitoring: Tracking portfolio-related data from web sources.
• ETL framework: Migrating existing pipelines into Kadoa and letting it handle scaling and maintenance.

The page does not provide specific pricing details. It offers a 'Book a demo' and 'Try it out' option, suggesting a custom pricing model based on enterprise needs.