What is Kadoa?
Kadoa is a web scraping automation platform built for finance and investment teams. It uses AI agents to extract structured data from websites, PDFs, spreadsheets, and images — then delivers that data to downstream tools and warehouses. Analysts describe what they need in plain language, and Kadoa builds the extraction workflow automatically. The platform targets teams that need reliable, up-to-date public data without relying on data engineering resources.
Features & Benefits
- Prompt-based workflow creation – Describe a data need in natural language and Kadoa generates the full extraction pipeline automatically, with no coding required.
- Multi-source data extraction – Pull structured data from websites, PDFs, Excel files, images, and APIs through a single workflow.
- Agentic browser automation – AI agents handle page navigation, login forms, filters, and dynamic content to reach data that static scrapers cannot access.
- Document parsing – Extract tables, text, and values from PDFs and file-based sources, including company filings and regulatory documents.
- Data transformation and formatting – Clean, normalize, and reshape extracted data using context-aware transformation logic before it reaches its destination.
- Custom validation rules – Define domain-specific rules that run on every workflow execution to flag inconsistencies or out-of-range values.
- Self-healing workflows – Kadoa detects when a source changes its layout or structure and adapts the extraction code automatically to maintain continuity.
- Real-time change monitoring – Monitor any public data source for updates and receive alerts via Slack, email, or webhook when market-moving data changes.
- Source grounding – Every extracted data point links back to its exact origin, making outputs fully auditable and traceable.
- Flexible data delivery – Push datasets directly to S3, Snowflake, spreadsheets, or any destination via REST API, SDKs, webhooks, and pre-built connectors.
- MCP and CLI support – Connect Kadoa to internal AI tools and agents using the MCP interface or command-line integration.
- No-code analyst interface – Business users can configure, run, and monitor workflows without involving data engineering teams.
- Anti-blocking infrastructure – Rotating residential and datacenter proxies combined with human-like browser behavior reduce the risk of extraction being blocked.
- Error handling with human escalation – When automated recovery fails, the Kadoa support team investigates and resolves the issue directly.
- Access control and user management – SSO/SAML login, SCIM provisioning, granular user roles, and multi-tenant data isolation for enterprise deployments.
- Compliance automation – Automated robots.txt checks, configurable collection restrictions, sensitive data detection, and compliance officer approval workflows.
- Audit logs – Comprehensive logs cover all workflow runs, data changes, and user actions for internal review and regulatory purposes.
Real-World Applications
Investment analysts tracking competitor pricing, job postings, or retail inventory changes can use Kadoa to build automated web scraping pipelines that run on a schedule. Rather than submitting a ticket to a data team and waiting days, an analyst can point Kadoa at any public source, describe the data fields needed, and get a working dataset within minutes. This type of self-service data collection is particularly useful when monitoring dozens of sources simultaneously.
Quantitative research teams at hedge funds and asset managers may use Kadoa to aggregate company filings, earnings documents, and regulatory disclosures across multiple jurisdictions. Kadoa handles the document parsing and normalization automatically, turning unstructured PDFs into clean, queryable datasets. Teams that previously spent months collecting this data manually can instead access it on demand with full source traceability.
Market makers and options desks that need speed-sensitive data can configure real-time alerts for specific web data events. When a monitored source updates — a regulatory filing, a pricing change, or a public announcement — Kadoa can push a notification before the information reaches aggregators like Bloomberg. This kind of automated change detection may give trading teams enough lead time to act on signals before the market moves.
Data science teams building internal AI pipelines or LLM-powered tools can connect Kadoa via its MCP interface or API to feed structured web data directly into their workflows. Instead of maintaining brittle custom scrapers, teams can rely on Kadoa’s self-healing extraction infrastructure to keep data feeds current. This frees data engineers to work on higher-value modeling tasks rather than firefighting broken pipelines.
