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Agentic Data Management

Stop Firefighting. Start resolving.

ADM helps trace failures across your entire estate and surface recommended next steps in minutes. Your pipelines, your governance, your data. No new headcount.

See ADM in Action

Explore Use Cases

90%+

Faster policy deployment

28s

Root cause analysis

15+

Specialized agents

TRUSTED BY ENTERPRISE DATA TEAMS WORLDWIDE

Your tools are fine.
Your data ops model isn't.

Your team has a catalog, a quality tool, a pipeline monitor, and a lineage viewer. When something breaks, they still open four tabs and correlate manually.

Root cause analysis takes 30-45 minutes because metrics live in separate dashboards with no cross-system correlation.

Policy creation for 500+ pipelines requires weeks of manual configuration - one asset at a time.

Business stakeholders can't self-serve data quality investigations without engineering support.

Use Cases

What your team can do now

Six capabilities, validated by enterprise teams running ADM in production today.

USE CASE 1

Incident Resolution & Root Cause Analysis

Ask ADM why a pipeline failed. Agents correlate execution logs, quality policy violations, and schema drift across systems — then surface the root cause with recommended next steps.

Pipeline Agent

Quality Agent

Catalog Agent

Drift Agent

Before ADM

Engineer opens quality dashboard → checks policy history → opens pipeline dashboard → exports data → builds chart in Excel → manually correlates patterns. 30–45 minutes per incident.

With ADM

One natural language question returns visual correlation analysis with root cause identified, impacted assets mapped via lineage, and recommended next steps. 28 seconds.

USE CASE 2

Automated Policy Generation & Quality Enforcement

Tell ADM to implement data quality monitoring across all customer tables. Agents identify assets, analyze characteristics, generate appropriate policies, and deploy them across distributed infrastructure — autonomously or with HITL.  

Workflow Agent

Quality Agent

Catalog Agent

Before ADM

Manual policy creation for 500–600 pipelines with 25 policies each. Weeks of configuration per domain, one asset at a time through the UI.

With ADM

Declare intent in natural language. Agents scan 1,000+ assets, generate SQL-based rules per asset type, and deploy across the Dataplane in 4–6 hours — versus 2–3 weeks manually.

USE CASE 3

Conversational Data Exploration & Asset Discovery

Ask about any asset in natural language. ADM routes to the right agents, synthesizes metadata, quality scores, lineage, and policy coverage — and flags gaps your team didn't know existed.

Catalog Agent

Quality Agent

Freshness Agent

Before ADM

Navigate to asset details → click through policy tabs → manually check each policy type → compile inventory. 5–10 minutes per asset. Hours for bulk audits.

With ADM

"What policies are attached to processed_transactions?" returns complete policy inventory with contextual gaps identified. 22 seconds. Scales to hundreds of assets in one query.

USE CASE 4

Anomaly Detection & HITL-Guided Resolution

ADM's semantic metadata layer translates raw field names into business meaning — turning signals like schema drift, freshness violations, and volume spikes into context-rich alerts. Agents surface the issue, trace upstream lineage, and propose corrective actions. Your team approves, adjusts, or escalates.

Semantic Metadata Layer

Drift Agent

Freshness Agent

Reconciliation Agent

Before ADM

Alerts fire across disconnected tools. Engineers manually triage, guess at context, and correlate systems by hand. By the time root cause is clear, the damage is done.

With ADM

Agents detect the anomaly, translate raw metadata into business context, trace upstream lineage, and surface a proposed action — ready for your team to approve, modify, or escalate in one click.

USE CASE 5

Cross-System Intelligence & Compliance

ADM connects to databases, cloud storage, monitoring tools, and documentation through MCP-DC — an extended protocol that adds distributed compute so agents can query petabyte-scale data lakes alongside structured systems. Ask about any data quality metric and get current values, historical incident context, compliance requirements, and lineage — all in one response, with access control enforced at every layer.

MCP-DC Gateway

Semantic Layer

Knowledge Base (RAG)

Quality Agent

Before ADM

Compliance teams spend weeks mapping data across Collibra, PowerBI, and Immuta. Standard MCP connections hit scale and latency limits before reaching data lake sources. No unified view across ERP, EDW, and cloud stores.

With ADM

Upload compliance docs to the Knowledge Base. MCP-DC routes queries across all connected systems — translating natural language into distributed compute jobs where needed — and returns results enriched with business definitions, tags, and lineage context.

USE CASE 6

Collaborative Investigation & Team Workspaces

Multiple users investigate together in real-time. @mention teammates and agents, build on previous analyses, and turn every conversation into searchable organizational knowledge via the Business Notebook.

Business Notebook

Multi-User Sessions

Shared Workbooks

Before ADM

Investigations live in Slack threads and spreadsheets. Context is lost between shifts. New team members start from zero on recurring issues.

With ADM

Persistent conversation threads with full agent context. Past investigations become templates. Your team's knowledge compounds with every investigation.

Ready to get started

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Products

Stop Firefighting. Start resolving.

Your tools are fine.Your data ops model isn't.

What your team can do now

Incident Resolution & Root Cause Analysis

Automated Policy Generation & Quality Enforcement

Conversational Data Exploration & Asset Discovery

Anomaly Detection & HITL-Guided Resolution

Cross-System Intelligence & Compliance

Collaborative Investigation & Team Workspaces

Ready to get started

Expert-led Demos

30-Day Free Trial

Meet with Us

Your tools are fine.
Your data ops model isn't.