There is a gap between how AI tools are sold and how they actually perform inside real companies. Sales pitches show clean demos. Production reality is messier: the AI does not know that your company defines "revenue" differently from the industry standard, does not know which data an intern can see versus a CFO, and has no idea what your internal approval process is. According to analysis across enterprise deployments, roughly 60% of AI projects fail not because the underlying model is inadequate, but because of data fragmentation, inconsistent context, and governance gaps.
Jedify, a New York startup founded in 2023, is building infrastructure to close that gap. Its platform connects to a company's existing data sources, including databases, CRM systems, Slack channels, meeting recordings, and internal documents, and builds a continuously updated map of how the business works. AI agents then draw on that map when making decisions, instead of guessing or hallucinating.
The company raised a $24 million Series A led by Norwest, with participation from Snowflake Ventures as a strategic investor. Total funding now stands at about $33 million. Snowflake is not just a backer: it is integrating Jedify's technology into its own AI products, which is a meaningful signal that even large data platforms see this problem as unsolved.
That last point matters more than it might appear. Big cloud and data platforms will tell customers to simply consolidate everything onto their platform. But most large companies have data spread across many different systems, and much of their institutional knowledge, how decisions actually get made, who approved what, what the exceptions are, lives outside any single vendor's environment. Jedify's argument is that it can sit above all of those systems without requiring a consolidation that will never happen.
Permissions are a practical concern Jedify addresses directly. The platform inherits access rules from existing systems down to the row and column level, which means an AI agent cannot surface a file to someone who does not have clearance to see it. This is not a minor feature: for any regulated industry, an AI agent that ignores access controls is a liability, not an asset.
The conflict-of-interest angle is worth noting. OpenAI, Anthropic, and Google have all started sending engineers into client companies to help with AI integration. But those same vendors charge by the volume of data processed, so they have little incentive to make those integrations efficient. Jedify's business model is independent of model usage, so it benefits from making the process leaner rather than more expensive.
Jedify currently has between 10 and 20 customers and is targeting companies with mature, multi-database data setups. Named clients include The Weather Company and Kiteworks, a compliance firm. The company is seeing interest from gaming, industrials, and consumer packaged goods, which are all sectors with complex internal data structures and little tolerance for AI errors.
The broader bet here is straightforward. As AI models from different providers become harder to distinguish from each other, the competitive advantage shifts to whoever has built the best internal knowledge layer. That layer, once built, is hard for a competitor to copy. Jedify is selling the infrastructure to build it.