Jedify

Jedify is a context-graph platform that connects an enterprise's databases, warehouses, SaaS apps, BI tools, and unstructured sources into a single AI-ready context layer so AI agents can reason correctly over business data. It's built for mid-market and large enterprises whose agents lack the decision context a human analyst applies silently.

Jedify builds a 'context graph' — an infrastructure layer that fuses an organization's business logic and data into a single, AI-ready context layer so that AI agents understand the business rather than merely querying it. Unlike a semantic layer that stores metric definitions and SQL abstractions, Jedify's context graph captures decision traces, tribal knowledge, governance precedents, data-lineage signals, and exception history — the operational intelligence a human analyst applies silently. Its automated construction process, Semantic Fusion, mines historical SQL query logs, applies named-entity recognition to unstructured documents, uses BERTopic clustering to surface recurring concepts, and runs co-occurrence analysis to infer entity relationships and resolve conflicting definitions across connected databases, data warehouses and lakes, SaaS apps, BI tools, code, documentation, Slack channels, and meeting recordings. The platform is model-agnostic, updates in real time, inherits row-, column-, and table-level permissions from existing identity systems, and includes observability and governance tooling, with delivery via native data agents plus MCP and A2A servers for third-party agentic apps. Based in New York, Jedify raised a $24M Series A in June 2026 led by Norwest (with Snowflake Ventures as a strategic investor), bringing total funding to roughly $33M, and counts early customers such as Kiteworks and The Weather Company.

Category

Enterprise Search & Knowledge

Pricing

Contact for pricing

Target Market

CDOs, CIOs, Data Engineers, RevOps and Analytics Teams

Headquarters

New York, USA

Customers

10–20 early customers

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  Context graph

  Connects entities, relationships, and business logic — including decision traces and tribal knowledge — so agents understand the organization, not just its tables.

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  Semantic Fusion

  Automated construction that mines SQL logs, applies NER and BERTopic clustering, and runs co-occurrence analysis to model business meaning and resolve definition conflicts.

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  Broad data connectors

  Native integrations across databases, warehouses and lakes, BI tools, CRMs, code, documentation, Slack, and meeting recordings.

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  Permission-aware governance

  Inherits row-, column-, and table-level access from identity systems, with observability and data-integrity controls.

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  Agent-ready delivery

  Native data agents plus MCP and A2A servers let third-party agentic applications consume the context graph.

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  Accurate agentic analytics

  AI agents answer natural-language business questions correctly by reasoning over the context graph instead of raw metadata.

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  Unify a fragmented data stack

  Enterprises with multiple warehouses and SaaS systems consolidate meaning and definitions into one governed context layer.

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  Governed self-service

  Teams get permission-aware, lineage-backed answers without exposing data beyond a user's access rights.

This page was written from 2 sources, 1 on domains other than jedify.com.

1. 1.techcrunch.com — jedify raises 24m to help companies arm ai agents with conte
2. 2.jedify.com — jedify.comvendor