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Enterprise Context Platform

Every data asset carries meaning, ownership, lineage, quality, and a trust state that shifts by the hour. Prizm captures, validates, and serves these signals as one context layer that humans and AI agents can rely on for every decision.

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Visionary

in the 2026 Gartner® Magic Quadrant™ for Augmented Data Quality Solutions

Leader

in Everest Group's PEAK Matrix® 2025 for Data Observability Providers

Top 10 Vendors

The Forrester Wave™: Data Quality Solutions, Q1 2026

Leader

in the G2 Spring 2026 Data Observability and Data Quality Grid® Report

EVOLUTION OF THE CATEGORY

From Catalogs to Context Platforms

The category has reinvented itself four times in two decades. Each generation answered a different business need, from storing metadata to enabling AI-readable context. Today's context platforms unify data understanding into a single, real-time layer that humans and AI agents read together.

2000s

01

Gen 1

Metadata Repositories
Metadata repositories, metadata management, data inventories.

The job was to document what data existed and store

Early–Mid 2010s

02

Gen 2

Data Catalogs
Data catalogs, discovery catalogs, searchable inventories.

The job became discovery. Searchable inventories, social signals, and curated documentation

Late 2010s

03

Gen 3

Governance-Led Catalogs
Governance catalogs, stewardship platforms, compliance artifacts.

Policies, glossaries, ownership, and stewardship workflows moved into the catalog.

Early 2020s

04

Gen 4

Active Metadata Platforms
Active metadata management, real-time signals, operational metadata.

Metadata started flowing through the stack as signals—lineage, query

2024–present

05

Gen 5

Context Platforms
Context platforms, AI context layers, semantic layers.

The current generation integrates seven context layers (semantic, operational, governance,

Read more

The 2026 question is the one every enterprise is being asked:
Can the layer between your data and your AI tools be trusted? A Gen 5 platform can answer that.

DEFINITION

A Context Platform Operates Seven Layers as One

A context platform is the enterprise intelligence layer that captures, validates, and serves the meaning, ownership, lineage, quality, and trust state of every data asset to humans and AI systems through a single window of truth. It is broader than a catalog, broader than a semantic layer, and broader than a knowledge graph.

A mature context platform integrates seven layers of context into one layer that is kept current as the underlying data changes. Each layer captures a distinct signal. The value comes from operating them as one entity.

What a data element means. Business definitions, metric calculations, term standards, and naming conventions.

How the asset behaves. Freshness, volume, schema cadence, pipeline state, and load history.

Who is accountable and what rules apply. Ownership, classification, policy references, and retention rules.

Whether the data is correct, complete, and consistent. Check results, reconciliation status, and segment scores.

How the asset is consumed. Query frequency, distinct users, downstream consumption, and AI agent activity.

What stewards and owners have provided. Comments, approvals, exception decisions, and the stewardship trail.

Why the asset exists. Product associations, KPI mappings, workflow dependencies, and segment usage.

operates these 7 layers as one.

Prizm by DQLabs is the enterprise context platform that captures all seven layers from your connected sources, integrates them into one layer that continuously updates as the data changes, and serves the result to people and AI agents through a conversational interface and through MCP.

  • Named a Visionary in the 2026 Gartner® Magic Quadrant™ for Augmented Data Quality.
  • Trusted by enterprises across financial services, healthcare, retail, manufacturing, and the public sector.

HOW PRIZM OPERATES THE CONTEXT LAYER

From connecting your sources to AI-ready context, in five steps

  • Connect to your warehouses, lakehouses, transformation tools, BI tools, and operational systems in one workflow.
  • Prizm pulls technical metadata from every connected source, including tables, columns, schemas, query history, and lineage paths.
  • Thousands of assets are ingested in the first hours of source connection. No manual tagging required.
  • Semantic, operational, governance, quality, usage, human, and business context are captured from your connected sources and reconciled against each other.
  • Each asset gets one integrated description that humans can read in plain language and AI agents can read through structured API responses.
  • The integration is the value, and it is what no catalog, observability tool, or quality platform delivers alone.
  • The layer is checked against operational signals from quality and observability runs every time the underlying data changes.
  • When context drifts, ownership shifts, or trust degrades, Prizm flags the change and updates the layer in real time.
  • Every asset carries a Context Score that summarizes its current trust state across all seven layers and surfaces the layers that need attention.
  • Prizm generates complete documentation for any asset, including business description, column details, lineage, and example queries.
  • Upload an existing policy document or data dictionary, and Prizm extracts business glossary terms automatically and at scale.
  • Stewards approve, edit, or reject any AI-generated change, and every action is logged for audit and regulatory review.
  • Generate an MCP token in Prizm and connect it to any MCP-compatible AI tool in minutes.
  • AI tools including Claude and Microsoft Copilot can read all seven layers, lineage, and current trust signals directly from Prizm.
  • Permissions follow the user. Audit trails follow every action. The governance you set in Prizm holds across every AI tool you connect.
1

Connect your stack. Prizm does the cataloging for you.

  • Hook up your warehouses, lakes, BI tools, and transformation layers — Snowflake, Databricks, dbt, Tableau, and more.
  • Prizm pulls metadata from every connected source: tables, columns, schemas, query history, lineage, and governance tags.
  • No manual tagging. No CSV uploads. No 'go ask your DBA.'
Data Observability
2

Connect your stack. Prizm does the cataloging for you.

  • Hook up your warehouses, lakes, BI tools, and transformation layers — Snowflake, Databricks, dbt, Tableau, and more.
  • Prizm pulls metadata from every connected source: tables, columns, schemas, query history, lineage, and governance tags.
  • No manual tagging. No CSV uploads. No 'go ask your DBA.'
Data Observability
3

Connect your stack. Prizm does the cataloging for you.

  • Hook up your warehouses, lakes, BI tools, and transformation layers — Snowflake, Databricks, dbt, Tableau, and more.
  • Prizm pulls metadata from every connected source: tables, columns, schemas, query history, lineage, and governance tags.
  • No manual tagging. No CSV uploads. No 'go ask your DBA.'
Data Observability
4

Connect your stack. Prizm does the cataloging for you.

  • Hook up your warehouses, lakes, BI tools, and transformation layers — Snowflake, Databricks, dbt, Tableau, and more.
  • Prizm pulls metadata from every connected source: tables, columns, schemas, query history, lineage, and governance tags.
  • No manual tagging. No CSV uploads. No 'go ask your DBA.'
Data Observability
5

Connect your stack. Prizm does the cataloging for you.

  • Hook up your warehouses, lakes, BI tools, and transformation layers — Snowflake, Databricks, dbt, Tableau, and more.
  • Prizm pulls metadata from every connected source: tables, columns, schemas, query history, lineage, and governance tags.
  • No manual tagging. No CSV uploads. No 'go ask your DBA.'
Data Observability

INTEGRATIONS

Operates inside the data stack your team already runs

Prizm operates as the context layer across your warehouses, lakehouses, transformation tools, BI platforms, semantic layer tools, observability systems, and quality platforms. Connect what you already have, keep what works, and let Prizm add the integrated context layer your employees and AI tools have been missing.

DATA SOURCES

Snowflake logo
Databricks logo
Google bigquery logo
Redshift spectrum logo
Azure synapse logo
Oracle logo
Postgresql logo
DBT logo
Tableau logo
PowerBI logo
PowerBI logo
PowerBI logo
PowerBI logo
PowerBI logo

BI-DIRECTIONAL WITH

Alation logo
Atlan logo
Collibra logo
Collibra logo
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WHERE THE CATEGORY IS HEADING

Analyst voices on the context platform category

ANALYST RECOGNITION

Recognized as an industry leader across analyst evaluations:

Trusted by enterprises across financial services, healthcare, retail, manufacturing, and the public sector.

Frequently asked questions about the context platform category

  • A context platform is the enterprise intelligence layer that integrates technical metadata, business knowledge, operational signals, governance state, lineage, usage patterns, and stewardship activity around every data asset into a single, machine-readable layer that humans and AI agents can rely on at decision time.

  • A data catalog describes data assets and supports discovery and governance. A context platform integrates the catalog with continuous operational signals from quality and observability, keeps the layer current in real time, propagates the trust signal through lineage, and exposes the result to AI agents through open standards like Model Context Protocol (MCP).

  • Context engineering is the practice of designing, building, and operating a context platform so it delivers the machine-readable context enterprise AI systems require. Gartner has stated that context engineering will replace prompt engineering as the central AI discipline by 2028.

  • The seven layers are semantic (meaning), operational (behavior), governance (accountability), quality (correctness), usage (consumption), human (stewardship), and business (purpose). A mature context platform integrates all seven.

  • Model Context Protocol (MCP) is the open standard for connecting AI assistants to external context and tools. A context platform that supports MCP allows AI tools including Claude, Microsoft Copilot, and emerging agent frameworks to read context, lineage, definitions, and trust signals directly without switching applications.

  • Prizm integrates bi-directionally with Alation, Collibra, Atlan, and Microsoft Purview, so existing investments remain in place. Prizm adds the integrated context layer, the continuous trust signal, and the AI-agent surface exposure that most active metadata catalogs do not provide today.

  • Modern context platforms typically deploy baseline context within the first weeks of source connection. Prizm customers typically connect their first source within hours and complete a full rollout, including business glossary import and stewardship configuration, in four to six weeks.

  • Prizm reads metadata from your sources but never accesses your actual data. All metadata sits in an encrypted Postgres repository with column-level encryption available for sensitive fields. Access is governed through single sign-on (SSO), multi-factor authentication (MFA), and granular permission controls that fit enterprise IAM patterns.

  • Yes. Business users interact with the context layer through a conversational interface that supports plain-language queries, voice input, and multilingual responses. They can find assets, understand business definitions, and locate owners without writing SQL or learning the underlying platform interface.

  • Prizm is the enterprise platform that operates the context layer, the data observability layer, and the data quality layer as one integrated product. DQLabs was recognized as a Visionary in the 2026 Gartner Magic Quadrant for Augmented Data Quality.

See the context layer in action.

Connect a source. Watch the context layer populate in real time. We will walk you through it.

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