FastCatalog.ai
FastCatalog.ai provides a catalog platform that uses language models to convert scattered AI asset documentation—such as READMEs, research papers, and build scripts—into structured compliance analysis covering lineage, terms, and privacy signals. The platform references more than 15,000 data sources and models, and combines systematic AI analysis with human review for selected cases.
- Regulatory & Compliance Technology
- Artificial Intelligence
- Legal Technology
- Data & Analytics
- Enterprise Software
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
AI governance requires understanding the terms, lineage, and provenance of models and datasets, but this information is rarely available in a single document. The applicable terms, repository contents, upstream lineage, provider details, and privacy concerns are often scattered across READMEs, research papers, technical notes, blogs, and build scripts, making compliance review time-consuming and error-prone.
Solution
FastCatalog provides a catalog platform that uses language models to turn scattered AI asset documentation into structured analysis for compliance review. The platform breaks analysis into narrow tasks such as identifying applicable terms, inventorying repository contents, tracing lineage, analyzing research papers, and analyzing declared repository terms. Each analysis task uses a defined output schema that requires evidence and explanation alongside classification values, keeping results connected to source material. Human review is applied to selected cases, and the platform supports collaborative project management for data scientists, compliance teams, and other stakeholders. The system is designed to scale through disciplined repetition, with task-specific benchmarks used to select cost-effective model configurations for each analysis type.
Target Audience
Primary users are data scientists, AI compliance teams, and governance professionals who need to understand the terms, lineage, and provenance of AI models and datasets for regulatory review and risk assessment.
Features
- Lineage analysis that traces upstream model and dataset relationships, including identification of direct upstream components and their associated licenses
- AI-powered analysis of repository contents, research papers, and declared terms using language models with structured output schemas
- Human review workflow for selected cases requiring deeper attention or higher-capacity analysis
- Project-based collaboration with member access management for data scientists, compliance teams, and other stakeholders
- Advanced filtering and comprehensive analysis of catalog assets with legal tags across data lineage
- Task-specific benchmarking that evaluates model configurations on semantic similarity and normalized cost to select cost-effective defaults (e.g., GPT-5.6 Luna High recorded 85.1% semantic similarity at 5.1% of the Sol High reference cost)
- Enterprise integrations with custom solutions built for specific customer needs