What is Patalyze

Patalyze provides the data and presentation layer for agentic patent research. The data is a global patent corpus, product search, and the mappings that compare products against patent claims. The presentation layer is the pages (tables, dashboards, and notes) that turn results into something you can read. Together they compose into workflows like freedom-to-operate and infringement detection, all searchable over a single API and open to AI agents through MCP.

Research#

Everything you do in Patalyze happens inside a research: a self-contained database built around a single analysis. It keeps everything that belongs together in one place (the patents you gather, the products you track, the mappings between them, and the pages you build to review the results), fully isolated, so data never crosses from one research into another. Data is added from a shared global corpus of patents and products that every research can pull from, so you never start from zero.

A research should in most cases only answer exactly one question. Clearing a single product for launch is one research; scanning the market for products that might infringe a patent family is another. It can be tempting to reuse an existing research because the data is already there, but a verdict is only as trustworthy as its scope: a dedicated research keeps each conclusion clean, self-contained, and easy to stand behind later, even months after the work is done. If related researches start to pile up, group them in a folder.

Data#

Every analysis is built on data: a global corpus of patents you can search and pull in by the million, product search that turns any real-world item into structured features, the mappings that compare the two, and the attributes you track alongside them.

Presentation#

Pages are how you read and present a research. A table triages rows down to the dangerous few, a dashboard charts the shape of the whole dataset, and a note writes up what you found. Every page is one of these three surfaces.

Workflows#

Data and presentation compose into the analyses teams actually run. Each is a path through the same pieces, in a different order.

The pieces fit together simply: add patents and products to a research database, and Patalyze creates a mapping for every pair, comparing the product's features against each claim of the patent. Results land on pages: tables you can filter and sort, dashboards that chart the findings, notes that summarize them. That means your product on one side, the field of patents on the other, and a verdict building up between them.

Integrations#

Two MCP servers connect Patalyze to AI clients. Both authenticate with your Patalyze account over OAuth: add a URL, approve access in the browser, with no API key to manage.

Setup takes a few minutes. The flow is the same in every client; what differs is where you add the server URLs. Pick yours for step-by-step instructions:

Prefer direct HTTP? The Global Patents API runs boolean and semantic search across millions of patents, returning full bibliographic records and patent-family data.