Jedify
by Jedify
The context graph for enterprise AI — give agents full business context, not just metadata.
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.
At a Glance
- 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
Key Features
- ✓Context graph
Connects entities, relationships, and business logic — including decision traces and tribal knowledge — so agents understand the organization, not just its tables.
- ✓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.
- ✓Broad data connectors
Native integrations across databases, warehouses and lakes, BI tools, CRMs, code, documentation, Slack, and meeting recordings.
- ✓Permission-aware governance
Inherits row-, column-, and table-level access from identity systems, with observability and data-integrity controls.
- ✓Agent-ready delivery
Native data agents plus MCP and A2A servers let third-party agentic applications consume the context graph.
Capabilities
Use Cases
- •Accurate agentic analytics
AI agents answer natural-language business questions correctly by reasoning over the context graph instead of raw metadata.
- •Unify a fragmented data stack
Enterprises with multiple warehouses and SaaS systems consolidate meaning and definitions into one governed context layer.
- •Governed self-service
Teams get permission-aware, lineage-backed answers without exposing data beyond a user's access rights.
Ideal For
Best For
- ✓Grounding enterprise AI agents in business-specific context
- ✓Unifying scattered data warehouses, SaaS, and unstructured knowledge into one context layer
- ✓Governed, permission-aware natural-language analytics
Market & Ratings
10–20 early customers
Market Analysis
Pros
- ✓Targets a real gap — agents that lack business context produce wrong answers
- ✓Strong governance/permission model for enterprise data
- ✓Strategic backing and integration from Snowflake Ventures
Cons
- ✗Early-stage with a small (10–20) customer base
- ✗Value depends on the maturity and cleanliness of the connected data stack
Pricing
Enterprise
Contact for pricing
- ✓Context graph across connected data sources
- ✓Semantic Fusion construction
- ✓Governance, permissions, and observability
- ✓Native agents plus MCP/A2A servers
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