One knowledge graph. Every data source.
Our federated knowledge graph connects your entire data environment into a single intelligence layer without forcing every domain to give up control. Better search, better governance, better AI.
Your data is everywhere—your AI can only use what it can find and understand
of organizations argue over whose data is correct due to inconsistent definitions
Source: Gartner
hours lost per employee every week searching for siloed data
Source: Forrester
of CIOs will adopt federated governance models to accelerate AI adoption by 2026
Source: IDC
Know your data so you can trust your AI
Make compliance easier
End-to-end relationship mapping and data lineage give compliance teams the traceability they need, without manual documentation.
Enable self-service analytics
An enterprise data marketplace with trusted and governed data products means your users can find the data they need for self-service analytics, fast.
Stop AI guesses
AI agents work from shared, approved business definitions rather than inferring meaning from column names and table structures.
Centralize definitions
Federated architecture lets every domain own its data while contributing to a unified semantic layer. No waiting on one team to model and publish everything.
One federated knowledge graph, built for every team that touches your data
Data discovery
- Smart search and interactive exploration: Users can find what they’re looking for fast with concept-aware search, then explore it in context with visual relationship maps across datasets and catalog assets.
- AI assistance: Query data in plain language through AI Analyst for catalog-grounded answers. Additional AI assistance with categorization, tagging, and metadata discovery and syncing helps keep the catalog current.
- Enterprise data marketplace: Accelerate self-serve analytics with confidence: select verified data products from the enterprise data marketplace without filing a ticket.
Data governance
- Shared definitions: Centralize KPI and business definitions and map how datasets connect to business processes, compliance requirements, and analytical goals—building consistency and the knowledge needed to use data effectively across your organization.
- Flexibility for growth: Ontology management and comprehensive taxonomy accommodate new platforms, evolving schemas, and changing pipelines without restructuring your knowledge graph.
- PII and security visibility: Automatic PII flagging, security classification, and tagging tools protect sensitive data and support compliance, even across complex and distributed data environments.
AI readiness
- Unified context layer: Connect metadata, lineage, and governance into a single contextual intelligence layer across your entire organization: a critical component for avoiding AI hallucinations.
- Context-sourced AI Analyst: AI Analyst connects to your catalog and syncs with its semantic layer so that answers are grounded in the knowledge graph, not inferences.
- MCP server: Make context and data quality results available directly in your favorite AI assistants and agent workflows via the MCP server, securely and at scale.
Data engineering
- Dynamic automated updates: New metadata and relationships are automatically discovered and incorporated, so your knowledge graph stays current without manual intervention.
- Knowledge-graph-powered lineage: Track your data’s location and movements from start to finish with lineage powered by the full relationship context of the federated knowledge graph.
- Data quality and observability: Surface data quality issues before they cause problems downstream. Proactive, shift-left quality rules deploy fast with AI assistance.
Modern data architectures
- Federated by design: Domain teams own and manage their data while contributing to a unified semantic layer, delivering autonomy and consistency—no forced centralization.
- Data products with context: Attach lineage, governance policies, quality scores, and business definitions to data products so consumers know exactly what they’re getting.
- Scalable governance: A phased, iterative implementation approach means governance doesn’t require a big-bang deployment. Start with one domain and scale organically, maintaining consistency via the graph. Open standards ensure your architecture stays interoperable.
Every source connected and every relationship captured
More sources added to your knowledge graph mean a stronger context layer, which leads to better data, analytics, and AI outcomes. Native support for 100+ sources, automatically discovered and kept current.
Modern data stack sources (Snowflake, Redshift, S3)
Legacy data environments (on-premise)
Semi-structured data (JSON, Parquet)
High-performance engines (Iceberg)
Specialized and NoSQL databases (Cassandra, DynamoDB)
BI tools (PowerBI, Tableau)
Every source connected and every relationship captured
More sources added to your knowledge graph mean a stronger context layer, which leads to better data, analytics, and AI outcomes. Native support for 100+ sources, automatically discovered and kept current.
Modern data stack sources (Snowflake, Redshift, S3)
Legacy data environments (on-premise)
Semi-structured data (JSON, Parquet)
High-performance engines (Iceberg)
Specialized and NoSQL databases (Cassandra, DynamoDB)
BI tools (PowerBI, Tableau)
The world’s most data-intensive organizations run on Actian’s knowledge graph
A Knowledge Graph That Grows With Your Organization
Actian’s federated architecture means you can start with one domain and expand organically. Most teams deploy in weeks, not months.
Book a 30-minute demo to see:
- Why federation outperforms centralized models at enterprise scale.
- How concept-based search changes data discovery for business users.
- What adoption looks like in customer environments with 1,000+ weekly active users.
Request a Product Demo
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