4.5 Gartner® Peer Insights™

Trust your data,
accelerate your AI

Scalable data management for better AI and business outcomes.
Data quality, observability, catalog, and lineage with a built-in
AI Agent to streamline your stack and speed impact.

ONE AI Agent

Explore the platform

Data Quality

Data Observability

Leading enterprises choose Ataccama for data trust

Product launch

Data Observability now available

Data Trust can break in motion. Now you can detect, triage, and remediate issues before there’s any downstream impact to the business and strategic AI initiatives.

Learn more

From messy data silos to data products that drive growth

Scalability

Scale data management for the AI era

Manual processes and overloaded data teams won't work anymore. Extend your team with our AI Agent, your digital data steward. Federate data quality into the business with agility, not headcount.

Compliance

Stay audit-ready, always

Act on data quality issues before regulators come knocking. Turn compliance from a recurring fire drill into a lasting foundation.

Migration

Data modernization

Maximize your Snowflake or Databricks investment and minimize rework by bringing trusted data with you. Start clean, stay clean.

Snowflake

Databricks

BigQuery

AI Readiness

Make your data AI-ready

Your AI is only as good as the data behind it. Ensure every dataset is verified and trusted so your models and agents deliver reliable outcomes.

Our customers drive impact

"As part of the journey we've gone through with Ataccama, we're becoming more data‑first, moving from assumption to assurance around data quality."

Steven Bennett Head of Data Enablement, Lloyds Banking Group

"Other vendors are transactional in their behavior. With Ataccama, there's a genuine belief of shared responsibility of success that we feel within T-Mobile."

Daniel West Data Management Lead at T-Mobile

The data trust layer for your modern AI stack

Data Trust Index scores your data. MCP connects it to your AI stack.

Click any layer to explore

Applications

Layer 5

Applications

Value realization

This is the layer enterprises actually feel. Agents don't surface outputs for human review here, they execute inside operational systems. Refunds are issued. Compliance escalations are filed. Service requests are routed and resolved, all at machine speed.

What lives here:

Autonomous AI agents built on platforms like Agentforce, ServiceNow, and Ironclad
Copilots powered by ChatGPT, Microsoft Copilot, and Power BI
Chatbots and self-service applications like Fin AI and Sierra
Custom-built AI apps using Cursor, Replit, and Bolt

The foundation decides the outcome

The quality of everything below this layer determines whether this layer is an asset or a liability. Agents can only be trusted to act autonomously when the data they act on has been validated, certified, and cleared, before execution, not after.

Layer 4

Agent orchestration

Layer 4

Agent orchestration

Reasoning & planning

This is where large language models, agent frameworks, and retrieval systems take trusted data and turn it into action. Agents reason over enterprise context, plan sequences of steps, and coordinate execution across systems, without a human authorizing each move.

What lives here:

Agent frameworks like LangChain, CrewAI, AutoGen, and LlamaIndex
Foundational models including GPT, Gemini, Claude, Llama, and others
Vector stores for retrieval-augmented generation
MCP as the interface connecting agents to governed data sources and tools

Probabilistic reasoning needs a deterministic foundation

LLMs are powerful precisely because they handle ambiguity well. But when agents trigger financial transactions or compliance actions, that same flexibility becomes a risk. Orchestration can't resolve data conflicts on its own, it needs certified, governed context passed up from the layer below.

Layer 5

Layer 3

Data trust layer

Layer 3

Data trust layer

Context & quality

Most AI initiatives have Layers 1, 2, 4, and 5. What they're missing is this one. The Data trust layer sits between consolidated data and the systems that act on it, validating, governing, resolving, and certifying data before any agent is permitted to execute against it.

What lives here:

Data quality validation and automated remediation at the source
Entity resolution that produces a single authoritative master record
Data governance including catalog, lineage, and business definitions
Continuous observability that detects anomalies as they emerge
The Data Trust Index: a real-time, machine-readable signal that tells agents whether a dataset is cleared for autonomous action

Available data isn't trusted data

The gap between available and trusted is exactly where agentic AI breaks down. Data that loads without errors and passes schema checks can still carry duplicates, stale values, and missing context. This layer closes that gap, continuously, across the entire data estate. Ataccama ONE delivers this layer as a unified platform, combining data quality, observability, governance, lineage and reference & master into a single environment purpose-built for trusted agentic AI.

Layer 4

Layer 2

Cloud lake houses

Layer 2

Cloud lake houses

Storage & compute

Before AI can operate at scale, enterprise data needs to live in one place. Cloud lakehouses consolidate fragmented source data into a scalable, compute-capable environment built for modern AI workloads, eliminating the infrastructure barriers that make coordinated AI deployment nearly impossible.

What lives here:

Cloud data platforms like Snowflake, Databricks, Amazon Redshift, and Google BigQuery
Native Python execution for data science and ML pipelines
Open table formats like Apache Iceberg for cross-platform flexibility

Consolidation solves infrastructure. It doesn't solve quality

Centralizing data is the necessary first move, and the return is substantial. But migrating data to a modern cloud platform doesn't clean it, resolve duplicate entities, or certify it for AI use. That work happens in the layer above.

Layer 3

Layer 1

Data sources

Layer 1

Data sources

Raw data

Every enterprise AI program starts here, with the operational systems that run the business. ERP platforms, CRMs, billing systems, document repositories, and data feeds that were built for process efficiency, not AI consumption. The data is there. The problem is what it carries.

What lives here:

Structured sources like SAP, Workday, Oracle, Salesforce, NetSuite, PostgreSQL
Unstructured sources like Slack, Gmail, SharePoint, Notion, Google Drive
Operational and transactional feeds across business functions

Years of quality debt come with the territory

Source systems accumulate inconsistent entity definitions, duplicate records, incomplete fields, and business logic that varies by region, system vintage, or the migration that last touched them. That's not an exception at enterprise scale. It's the expected condition of raw data, and it's exactly what the layers above are designed to address.

Layer 2