Kadoa: Initiation of Coverage

GAII Intelligence defines the Alternative Data & Data Infrastructure market as companies that supply investment firms with non-traditional evidence on company and economic performance — transaction, receipt, location, web and business-spend data — and the tools that collect, clean and deliver it. Buyers are heads of data strategy and data sourcing, fundamental and quantitative analysts, and the compliance teams who must approve each new source.

Capabilities we treat as mandatory

• Point-in-time integrity: history that can be tested without look-ahead, with restatements, panel changes and survivorship handled and disclosed.
• Provenance and screening: a documented chain from source to delivery, with consent, licensing, MNPI and personal-data screening a compliance team can review.
• Investable mapping: data resolved to tickers and entities, so that a signal can be tied to a security without manual work.

Capabilities we treat as differentiating

• Predictive-value evidence: published back tests or, better, independent studies showing that the data anticipates reported results.
• AI-ready delivery: data shares into Snowflake and Databricks, and MCP connections that let a client's AI assistant query the data under entitlement.
• Transparent AI answers: conversational tools that show the query, panel size and method behind each answer and decline unsupported questions.
• AI in the pipeline: models that find sources, map entities and repair collection, cutting the time from idea to usable dataset.

Kadoa competes with in-house scraping teams, with managed web-data vendors and scraping platforms such as Bright Data, Zyte and Apify, and with alternative-data providers whose web-derived datasets a fund could instead build itself.

The following are the capabilities and characteristics that, in our assessment, most distinguish Kadoa within the Alternative Data & Data Infrastructure market. Each is drawn from publicly documented product capability, company releases and documentation, or independent reporting.

• Agentic collection: The Assistant explores a site, picks the most reliable source, proposes a schema, builds and tests the pipeline and waits for approval before going live.
• Self-healing pipelines: Generated code is regenerated when sources change, with escalation to a person only when recovery fails.
• Value-level lineage: Deterministic code rather than per-page model calls, with each value traceable to its source and edit history kept.
• AI-assistant delivery: An OAuth MCP server usable from Claude, ChatGPT, Cursor and Codex, extended almost monthly since February 2026.
• Compliance workflow: A Compliance Center for reporting and rule monitoring, automated robots.txt checks and approval workflows.
• Easy to trial: A free trial and a no-commitment evaluation period on every plan.

The following are factors that prospective clients should weigh, and areas where our public review did not find sufficient evidence to reach a settled view. Several are common across vendors at this stage of the market and are not specific to Kadoa. We encourage prospective clients to raise each of them directly with the company; we have done the same, and will update this note as answers arrive.

• No history: Datasets begin when a pipeline is built, so they cannot be back-tested until they have run for some time.
• Mapping: We did not locate ticker mapping or entity resolution; buyers should expect to map output to securities themselves.
• Screening: MNPI and personal-data screening are not documented; scraping terms-of-use risk remains with the client's compliance review.
• Evidence: Clients are described as top-five hedge funds and market makers but none is named, and outcome figures are company-reported.
• Model disclosure: The model providers behind the Assistant are not named.
• Scale: Seed-stage, with funding amounts and headcount undisclosed.
• Assurance detail: SOC 2 reports and policies are available only on request; on-premise deployment appears only in a blog post.

Kadoa was founded in Zurich by Adrian Krebs (CEO), Johannes Engler (CTO) and Tavis Lochhead (CRO); CB Insights records 2023 as the founding year. The team is fully remote across Europe and North America, and the company describes discretionary hedge funds as its core users, with systematic firms a growing target.

Vi Partners led a funding round in March 2025, with Fyrfly Venture Partners participating; the amount was not disclosed. CB Insights also lists AIX Ventures among investors. No revenue, client count or headcount has been published.

Company-reported figures are shown as such and are not independently verified. Verification is carried into the vendor questionnaire.

Kadoa presents one platform, which it calls a web scraping operating system, with an Assistant on top and several delivery routes. Figure 2 sets out our assessment of how it maps onto the alternative data workflow.

Kadoa Assistant

Builds a web dataset from a single prompt and keeps it running (June 2026).

Monitors and pipelines

Real-time change detection, scheduled extraction, validation and observability dashboards.

Compliance Center

Rule monitoring, approval workflows and reporting for compliance officers (July 2025).

Delivery

Snowflake, S3, Databricks Delta Sharing, API, webhooks, Slack and an MCP server.

Scope of the product today

We did not locate a proprietary panel, historical datasets, ticker mapping, research content or named client references.

Kadoa uses large language models to generate collection code once, run and test it, and regenerate it when a source changes, instead of calling a model on every page. Agentic navigation, added in August 2025, moves through sites without scripted steps.

Values link to their source page with a confidence score. The model providers are not named, and no evaluation has been published.

Kadoa collects whatever public web, PDF or file sources a client points it at, so breadth is wide but defined by the client. Real-time monitors flag changes as they happen.

Under this variant the gaps are history, which starts when a pipeline does, and mapping to tickers and entities, which we did not find documented.

Kadoa fits the data-sourcing team: monitoring, alerting and delivery into Snowflake, Databricks, S3 and AI assistants are its strongest workflow criteria.

It offers no backtest support or predictive-value evidence; testing whether a dataset carries signal is left to the client.

Kadoa lists SOC 2 Type 1 and Type 2 in its trust centre, offers SAML SSO and IP allowlisting on Enterprise, and states that customer data is not used for AI training. Figure 3 sets out what our public review located.

We would encourage buyers to request the SOC 2 report, the model providers, the subprocessor list and a written no-training clause.

The main offsetting factor is scale: a seed-stage company with undisclosed funding.

Kadoa publishes its plan structure but not prices. Starter includes a free trial; Enterprise adds Snowflake, S3 and MCP delivery, SSO and an SLA, with consumption pricing and volume discounts.

Buyers should ask for named references and a cost comparison with their current scraping spend.

Kadoa sits in the Emerging quadrant of the segment matrix, with the highest AI & Technology score among the five companies we cover but the second-lowest Investment Utility, ahead only of Advan Research.

GAII Alternative Data & Data Infrastructure coverage, 21 October 2026, scored on the segment variant of Data & Content and Workflow Fit; not directly comparable with Research & Intelligence or Risk, Compliance & Surveillance scores.

11.1 Where Kadoa is strongest relative to peers

• Against in-house scraping, on time to a working pipeline and on maintenance.
• Against data vendors, on control of sources and schema.
• Against peers, on agentic AI and MCP delivery.

11.2 Areas of narrower functional focus

Kadoa supplies collection, not data, and is scored here against companies that supply both.

• History: none before a pipeline starts.
• Mapping: ticker and entity resolution not documented.
• Evidence: no named clients or predictive-value studies.
• Scale: seed-stage funding.

Scores are on a 0–10 scale. Each dimension score is the mean of its sub-criteria, and the GAII Overall Score is the weighted mean of the six dimensions. The detailed sub-criteria scores that build each dimension are set out in Appendix A.

These are the areas where our public review did not find sufficient evidence to form a settled view. We have put them to the company and will update this note when they are answered. Prospective clients may find them a useful agenda for their own evaluation call.

• Named client references in investment management.
• Ticker mapping and entity resolution.
• MNPI and personal-data screening of sources.
• How terms-of-use risk is assessed.
• Model providers and no-training terms.
• SOC 2 report period and scope.
• On-premise and private-cloud deployments in use.
• Funding raised and runway.
• Headcount and support capacity.
• Pricing for a typical fund deployment.
• Historical or pre-built datasets.
• Evidence of outcomes for clients.

This note is an assessment of a vendor against a defined framework, not a recommendation to buy or to decline. The following are the considerations we would put at the centre of an evaluation of Kadoa specifically.

If you run research or data sourcing

• Pilot on a source you already scrape.
• Plan for history to accrue before back-testing.
• Use MCP to put monitored data in front of analysts.

If you are responsible for technology, compliance or vendor risk

• Open with Figure 3; request the SOC 2 report.
• Route new sources through the Compliance Center and your own review.
• Confirm model providers and training terms.

If you are assessing the category rather than the company

• Agents are cutting the cost of building web datasets.
• Funds may build some data they used to buy.
• Compliance review is becoming software.

Each dimension score in Section 12 is the mean of the sub-criteria below. Data & Content and Investment Workflow Fit use the Alternative Data & Data Infrastructure segment variant.