Inflectiv.ai is a structured dataset platform for AI agents, with the core goal of turning data scattered across files, URLs, documents, research materials, and professional knowledge into queryable, payable, and continuously value-adding living datasets. Its stated positioning is "structured datasets, for AI agents to use, queryable via API, SDK, and MCP."
In terms of use, the platform serves three types of roles. Data owners can build the knowledge they possess into assets, generate structured, queryable datasets from files, URLs, documents, research, or directly input knowledge, and set prices. Datasets can remain private for use by their own agents and tools (encrypted, with data not leaving), or be published to the marketplace to earn revenue each time they are queried; they can also choose to issue tokens on a curve, allowing the work to receive funding before it generates revenue. Knowledge workers annotate data in existing tools and enjoy half price at the point of use of the annotation. Developers and enterprises replace maintaining forty data sources with a single query, consuming one credit per question and obtaining records with sources and dates.
The main capabilities include: converting uploaded PDFs, documents, JSON, tables, images, etc. into records, embedding and deduplicating upon ingestion, and making datasets queryable as soon as they are created; supporting adding files or URLs, updating content, and setting datasets as public, private, or tokenized; dataset records include sources and dates, and answers come with record citations; agents write back what they learn when querying, so datasets continuously expand with queries; can be encrypted and can set on-chain who has the right to query; settle in USDC per question, with owners earning revenue each time they are asked. The application scenarios displayed by the platform include healthcare data intelligence, legal and policy intelligence, Web3 due diligence, AI vendor compliance, VC matching intelligence, customer intelligence, etc. These applications all run on top of Inflectiv datasets, and the query method is the same as users querying their own datasets.
In terms of usage, data owners publish datasets and set prices, earning revenue each time they are asked; knowledge workers annotate within existing workflows and enjoy half price; developers and enterprises query through a unified entry point, with one credit per question. The platform's stated workflow is roughly six steps: build a dataset and price it, an agent asks a question, an answer or record is returned, settle in USDC per question, write back what is learned, and after the dataset grows, return to step two.
Applicable scenarios include teams that need to provide queryable structured data sources for AI agents, individuals or organizations that want to monetize professional knowledge, and developers and enterprises that need a unified query entry point instead of maintaining multiple sources. In terms of limitations, the materialsNo explicit pricing amounts, supported blockchain or token details, available regions, data compliance certifications, performance metrics, and specific technical integration limitations are provided; the above information is not currently available.
