kadoa.com

Kadoa groups its web data offering into three products with different jobs: Monitors alert you when something changes on the web, Pipelines automate end-to-end scraping and keep it running as sites change, and Datasets are curated collections built around a specific investment thesis from public web data. Choose Monitors when you need to know when something happens, Pipelines when you need a repeatable feed of data you define, and Datasets when you want a ready-made dataset tied to a research question—with a free sample available to test before you pay.

Side-by-side comparison

Monitors: fastest alerts on changes

Monitors are positioned as the fastest, most reliable way to be alerted to filings, prices, and website changes. The value is timing: you find out about a market-moving event without polling sources yourself. This fits workflows where the trigger matters more than the full dataset—regulatory filings, price moves, or a page quietly changing.

Pipelines: automated, self-maintaining scraping

Pipelines take a description of the data you need and have Kadoa agents build, monitor, and repair the scraper. The stated design goal is that data keeps flowing as websites change, rather than breaking within a week and requiring manual QA. Kadoa says 85% of incidents are resolved automatically and that the time from request to first dataset is under two hours. This is the option for recurring needs where reliability over time is the main cost driver.

Datasets: built around your thesis

Datasets are assembled from public web data around a specific investment thesis. Instead of defining a scraper, you get a finished dataset, and Kadoa lets you test a free sample before paying. This suits research where the question is well-defined and you want the data delivered rather than the pipeline managed.

How they connect

All three sit on one data layer and can deliver into tools teams already use: Excel, Google Sheets, and Slack alerts for analysts and PMs; Snowflake, Databricks, BigQuery, S3, SFTP, webhooks, and API for engineers; and MCP, Claude, ChatGPT, and internal agents for AI workflows. In practice, a Monitor can flag an event, a Pipeline can keep the underlying feed current, and a Dataset can package the result for a specific thesis.

How to decide

• Need to react to events quickly? Start with Monitors.
• Need the same data refreshed continuously despite site changes? Use Pipelines.
• Need a finished dataset for a defined research question? Request a Dataset and test the free sample first.
• Unsure? The three are complementary, so the choice is about whether your bottleneck is timing, maintenance, or assembly.

Kadoa is a web data platform built for finance teams. It turns public web sources into verified datasets that investment teams and AI agents can use directly. You describe the data you need, and Kadoa's AI agents build, monitor, and repair the pipelines that collect it. It fits workflows where data reliability matters more than one-off scraping—filings, prices, and website changes that feed research or trading decisions.

Who it's for

Kadoa targets organizations that depend on timely, correct web data:

• Hedge funds
• Asset managers
• Sell-side firms
• Data providers

These teams typically need recurring, auditable data rather than a single extraction. If your need is a one-time scrape, a general-purpose scraper is likely simpler. If you need a maintained feed that survives site changes, Kadoa's model is more relevant.

The three core products

The distinction matters: Monitors are for event detection, Pipelines for ongoing extraction, and Datasets for packaged research data.

How the workflow runs

Kadoa's own comparison frames the difference against a traditional data-engineering path:

1. Point at any source.
2. Describe the data you need.
3. Get your dataset in minutes.

The stated benchmark is under two hours from request to first dataset, and 85% of incidents resolved automatically. Treat these as vendor-reported figures, not independently verified numbers.

Under the hood, the mechanism is self-healing pipelines: agents build the scraper, monitor it, and repair it when a source changes. That addresses the common failure mode where a custom scraper breaks within a week and requires manual QA.

Where the data lands

Kadoa delivers to tools teams already use, split by role:

• Analysts and PMs: Excel, Google Sheets, Slack alerts, the Kadoa app
• Engineers and data teams: Snowflake, Databricks, BigQuery, S3, SFTP, webhooks, API
• AI agents: MCP, Claude, ChatGPT, internal agents, API

This matters for adoption: you don't need to rebuild your stack to consume the output. An analyst can work in Sheets while the same data lands in Snowflake for engineering.

What to check before committing

• Pricing: Kadoa has a pricing page, but the provided material doesn't list figures or payment terms. Confirm cost and any login requirements directly before assuming a free tier.
• Dataset fit: Datasets are built around a thesis from public web data, with a free sample offered before payment. Test the sample against your actual research question.
• Reliability claims: The sub-2-hour and 85% figures come from Kadoa's marketing. Ask for references or a trial in your own environment.
• Compliance: Kadoa lists "Compliant" as a pillar but the material doesn't detail certifications. Check the trust center for specifics relevant to your firm.

Bottom line

Kadoa is a reasonable fit if you're an investment team that needs recurring, verified web data delivered into existing tools, and you'd rather buy maintained pipelines than staff a scraping team. It's less suited to one-off extraction or to teams that need confirmed pricing and compliance details up front—those should be verified before a demo.

Kadoa is a web data platform built for finance teams that need datasets drawn from public web sources, delivered into tools they already use. It fits best when your research depends on filings, prices, and website changes that must be monitored continuously and kept reliable as sites evolve. Before committing, evaluate it against four things: source fit, reliability model, destination compatibility, and how you validate output before paying.

Start with whether your data need is public-web data

Kadoa positions itself as "the web data layer for finance," serving hedge funds, asset managers, sell-side firms, and data providers. Its three products map to distinct jobs:

If your requirement is internal, licensed, or proprietary data, this is not the tool's stated scope. The relevant question is whether the signals you track live on public websites.

Assess the reliability model, not just the scraping

Kadoa's core claim is that "coding got cheap, curated data did not." Its agents build, monitor, and repair pipelines automatically, and the site cites under two hours from request to first dataset and 85% of incidents resolved without manual intervention. For an investment team, the practical test is whether self-healing pipelines cover the sources you depend on and whether "verified datasets" meet your QA standard. Ask during a demo how repairs are validated and how you would be notified of a break.

Check destination compatibility against your stack

Kadoa delivers natively to the environments teams already use:

• Analysts and PMs: Excel, Google Sheets, Slack alerts, the Kadoa app
• Engineers and data teams: Snowflake, Databricks, BigQuery, S3, SFTP, webhooks, API
• AI agents: MCP, Claude, ChatGPT, internal agents, API

Map your existing workflow to this list before evaluating anything else. A gap here is harder to work around than a feature gap.

Confirm compliance and trust requirements

Kadoa lists "Compliant" as a headline capability and maintains a trust center. For regulated investment firms, review that material directly and confirm it satisfies your internal data-governance requirements rather than relying on the marketing label.

Validate before you commit

The site offers a free sample dataset to test before you pay, plus a demo booking and a "try it out" path. Use the sample to check data accuracy on a source you know well, and use the demo to pressure-test the reliability and destination questions above. Pricing details are on the pricing page; confirm commercial terms there rather than assuming a free tier.

Kadoa's product, pricing, and resource information is all reachable from its main site, kadoa.com. The top navigation splits into Product, Use Cases, Pricing, Resources, and Company, and the homepage also offers direct entry points: Sign in, Get started, Book a demo, and Try it out. The pricing page is at kadoa.com/pricing. The site does not state prices on the pages summarized here, so treat the pricing page as the place to check current terms rather than assuming any tier is free.

Product information

Kadoa describes itself as a web data layer for finance, with AI agents that build, monitor, and maintain web data pipelines for investment teams and AI agents. The product is organized into three parts, and the homepage explains each:

The homepage also lists where data can be delivered. For analysts and PMs: Excel, Google Sheets, Slack alerts, and the Kadoa app. For engineers and data teams: Snowflake, Databricks, BigQuery, S3, SFTP, webhooks, and API. For AI agents: MCP, Claude, ChatGPT, internal agents, and API.

To see the product in action, use Try it out or Book a demo from the homepage. The site also links to a recorded webinar on how a large sovereign wealth fund uses data and AI.

Pricing

Go to Pricing in the top navigation or visit kadoa.com/pricing directly. The site's own description of Datasets says you can test a free sample before you pay, which indicates a paid offering exists, but the summarized pages do not list prices or plan details. Check the pricing page for current figures and conditions.

Resources

The Resources menu collects the site's documentation and editorial material:

• Docs — product documentation
• Blog — articles and updates
• Webinars — recorded sessions, including the sovereign wealth fund webinar
• Insights — the "Latest posts & insights" section on the homepage
• Trust center — compliance and trust information; the homepage highlights "Compliant" as a product theme

The homepage also links to Open Datasets and a Changelog, which are useful if you want to see sample data or track product changes.

Company and getting started

Company covers About Us, Careers, and Contact. To begin using the product, choose Get started; to evaluate it with the team first, choose Book a demo; existing users go through Sign in. The homepage claims a first dataset in under two hours from request, but that is a vendor statement — confirm timelines and access conditions during a demo or trial rather than relying on the marketing figure.

Kadoa builds a web data pipeline from a plain-language description of the dataset you need, then keeps it running by monitoring the pipeline and repairing it automatically when source websites change. It is aimed at investment teams and the AI agents that support them, and it delivers the resulting data into the tools those teams already use. The approach suits teams that need recurring, verifiable data from public web sources but don't want to own scraper creation and maintenance themselves.

How a pipeline gets built

The build process is deliberately short. According to Kadoa, the sequence is:

1. Point at any source. You identify the website or sites the data should come from.
2. Describe the data you need. You state the dataset in natural language rather than writing extraction code.
3. Get your dataset. Kadoa's agent builds and runs the pipeline, and the company claims a first dataset in under two hours from request.

The same "describe it and the agent builds it" pattern applies to the broader product: Kadoa describes its agents as building, monitoring, and repairing pipelines, with scraper creation and maintenance automated so data keeps flowing as websites change.

What this replaces

Kadoa frames the alternative as a ticket-driven cycle: submit a request to data engineering, wait, follow up, build a custom scraper, watch it break within a week, run manual QA, and miss market-moving updates before repeating. The described Kadoa path collapses that into pointing at a source, describing the data, and receiving the dataset. That comparison is the company's own framing, but it clarifies the workflow it is designed to remove.

How pipelines are maintained

Maintenance is the part that usually decides whether a scraping setup survives. Kadoa's stated mechanisms are:

• Monitoring. Pipelines are watched continuously, and the Monitors product is positioned around catching filings, prices, and website changes quickly.
• Self-healing. When a site changes and a pipeline breaks, the agent repairs it rather than leaving it for a person to rebuild.
• Automated QA. Data is checked rather than passed through raw, which is the basis for Kadoa's claim of "provably correct data."
• Reliability metrics. Kadoa states that 85% of incidents are resolved automatically and that request-to-first-dataset time is under two hours.

These figures come from Kadoa's own materials and should be treated as vendor claims to validate against your own sources during a trial rather than as independently verified benchmarks.

Where the data lands

Kadoa positions itself as a single data layer delivered natively to three kinds of consumers, which matters if the pipeline output has to fit an existing stack:

The practical implication: you don't need to build export plumbing for each audience. If your team works in Sheets and Slack but your warehouse runs on Snowflake, both can be fed from the same pipeline.

What to check before committing

• Source coverage. Confirm the specific sites and document types you depend on are supported, especially if your thesis relies on filings or price pages with unusual structures.
• Verification method. Ask how automated QA flags errors and what a "verified" dataset looks like in practice for your use case.
• Delivery fit. Match the destination list above against your actual stack; if you rely on a tool not listed, confirm API or webhook coverage.
• Commercial terms. Kadoa offers a free sample dataset to test before paying and a demo path, but pricing details are not specified in the available material, so confirm costs and access terms directly.

For teams weighing whether to build in-house or buy, the deciding factor is usually maintenance load rather than initial extraction: if your sources change often and a broken pipeline costs you timely information, an automatically repaired pipeline is the relevant comparison, not the cost of a one-off scraper.