Kadoa is a web data layer for the financial domain that uses AI agents to automatically build, monitor, and maintain mission-critical web data pipelines, generating validated datasets for investors.
Key capabilities:
- Automated crawler creation and maintenance: automatically generates crawlers and keeps data flowing when websites change.
- Self-healing pipelines: when a pipeline breaks due to source site changes, Kadoa can automatically repair, test, and redeploy it; operations teams step in when the agent needs help, and every step is logged.
- Quality checks: every run checks completeness, plausibility, schema, and custom domain rules, and provides a confidence score.
- Data traceability: every value links back to its source page, paragraph, or cell.
- Monitoring and alerting: success, throughput, and incidents are visible in Kadoa or your monitoring stack.
- When a fix cannot be proven safe, a ticket is generated explaining the cause of the failure, the attempted fix, and the evidence.
- No black-box model output: you always control every pipeline.
How to use it:
- Describe the data you need.
- Agents build, monitor, and repair your pipelines.
- Get data in the tools you already use. Kadoa provides native destination environments for analysts, engineers, or agents.
- All data comes from public web data, and you can test samples for free before paying.
Security and compliance:
- SOC 2 certified.
- Built-in platform security and privacy.
- Encryption at rest and in transit.
- Regular third-party penetration testing.
- Supports SSO/SAML and automated user provisioning (SCIM).
- Fine-grained, customizable user roles.
- Strict data isolation and multi-tenant architecture.
- Comprehensive compliance and audit logs.
- Supports on-premises or private cloud deployment options.
- Data is never shared between customers.
- Your data is never used for AI training.
- Your workflows, sources, and schemas are strictly proprietary and confidential.
- Configurable compliance rules and restrictions.
- Compliance officer approval required before data collection.
- Sensitive data detection.
- Automatic robots.txt checks.
- Independently audited by Sensiba, with an independent review of how public web data is collected.
Use cases:
- Provide investors with the most reliable datasets for the most demanding needs; provably correct data from self-healing pipelines, automatically monitored and repaired.
- Built for finance, developed over many years. Self-healing, quality checks, and anti-blocking are hardened in production, making your next thousand pipelines routine.
- Suitable for scenarios requiring the fastest, most reliable alerts for documents, prices, and website changes.
