AI Data Management

Better data management for smarter AI

From siloed to live, governed, and AI-ready enterprise data

Most enterprises have data scattered across warehouses, SaaS apps, collaboration platforms, logs, and documents - each locked in its own silo. Unframe automates the heavy lifting: unifying, modeling, and mapping relationships across all those silos to make data AI-ready.

Avoid endless duplication and centralization projects

Govern with source-level rules enforced everywhere

Accelerate adoption with contextual, ready to use data

The problem with workarounds

Without a governed data layer, most enterprises fall back on stopgaps that don’t scale:

Writing custom ETL pipelines that break with every schema change

Building manual knowledge bases that go stale within weeks

Maintaining DIY embedding pipelines that don’t capture domain language

Re-implementing permissions logic outside of governed systems

Core data capabilities at a glance

Access and governance

Access without duplication: Connect to warehouses, SaaS apps, collaboration platforms, and documents stored directly at the source. Governed snapshots are used only where needed.

Outcome: Always fresh, always governed, and cost-efficient - without creating new silos.

Permission-aware governance: Inherit and enforce source-level access rules and policies automatically.

Outcome: Secure, least-privilege data access across systems.

Context and correlation

Data extraction & abstraction: Parse contracts, reports, logs, and tickets; convert unstructured content into structured signals.
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Outcome: Raw inputs become a usable context for analytics and AI.

Data modeling & relationships: Unify structured and unstructured data into a living model that adapts with your business.
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Outcome: AI solutions operate on a consistent, evolving representation of your business.

AI-readiness

Domain-tuned embeddings & model: Generate embeddings and models optimized for enterprise terminology and scale.
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Outcome: AI understands your business language, not just generic text.

Natural Language to SQL: Translate plain questions into SQL and semantic queries across systems, using embedded processing and federated execution.
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Outcome: Business users get contextual answers; technical users get precise, permission-aware queries.

Data mesh, made practical for enterprise teams

Unframe bakes in data mesh principles to give large organizations a trusted, scalable data foundation. Business units publish their data as products, while federated governance enforces enterprise standards. Interoperability happens at the source - no duplication, no bottlenecks - creating a semantic layer that powers AI at scale.

The foundation that powers Knowledge Fabric

By delivering federated, queryable, governed inputs, these data-management building blocks give Knowledge Fabric what it needs to create a semantically linked, contextualized layer that makes AI fluent in your business.

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