Latttice — Have a conversation with your data
Latttice — the Data Product Workbench for Collibra, now on the Collibra MarketplaceLatttice — the Data Product Workbench for Snowflake70% Less Complexity with LattticeDeliver Trusted Data 80% FasterLower the Cost of Building and Operating Data Products by 70%Latttice is available where business teams work — Slack, Excel, LattticeGPTLatttice the Data Product Workbench brings trusted, fit-for-purpose data to the point of decisionsLatttice the Data Product Workbench is the bridge between the Business and Data TeamsLatttice delivers active governance at the point of data access, so trusted data products are created, controlled, and used with confidenceDesigned in North Carolina, USA
Latttice — Have a conversation with your data
Executive One Pager
Executive Summary

The Enterprise AI Operating Model.

How reusable Data Products create the trusted foundation for AI models, AI agents and business decisions.

AI does not fail because of the model. It fails because trusted business information is rebuilt for every initiative. Latttice changes the sequence — build the trusted foundation once, reuse it across every AI capability.

Enterprise Data → Data Products → AI Intelligence → Enterprise Action
The Enterprise AI Operating Model — a five-layer stack from Foundation Data Products through Fused Data Products, AI Models & Analytics, AI Agents & Automation, to Business Decisions & Outcomes.
Fig 1One governed foundation of Data Products powers every model, agent, application and decision.
Why AI Does Not Scale

AI pilots are built as isolated projects rather than reusable enterprise capabilities.

  • Data lives across disconnected systems
  • Business definitions vary across teams
  • Trusted information is hard to identify
  • Governance is applied inconsistently
  • Every AI project rebuilds similar data
  • Business users can't confidently interpret AI outputs

This is not primarily a model problem. It is an operating model and architecture problem.

The Operating Model

A reusable operating model for Enterprise AI.

Rather than building AI on top of fragmented enterprise data, the organization creates reusable Data Products first — establishing trusted business meaning, governance and context once.

1
Foundation Data Products
Trusted, reusable representations of core business subjects.
2
Fused Data Products
Combined subjects for richer business and decision context.
3
AI Models
Predictions, recommendations, classifications and forecasts.
4
AI Agents
Use trusted information and models to support or execute processes.
5
Business Outcomes
Better decisions, automation, customer experiences and performance.
From Enterprise Data to Enterprise Action — the five-stage Latttice operating model with governance, quality, security and lineage applied consistently across all layers.
Fig 2From Enterprise Data to Enterprise Action — one trusted foundation, every stage connected, enterprise action delivered.
Foundation Data Products

Trusted, reusable representations of core business subjects — with business meaning, ownership, governance, quality, security and lineage built in.

CustomerProductFinanceAssetsSuppliersWorkforce
Fused Data Products

Foundation Products combined into richer business context, so AI can reason across the enterprise instead of a single subject.

Customer IntelligenceSupply Chain IntelligenceClaims IntelligenceAsset ReliabilityWorkforce EffectivenessFraud Intelligence
One Foundation, Many Consumers

One Data Product ecosystem powers many AI capabilities.

  • Executive Intelligence
  • Enterprise AI Agents
  • Predictive Analytics
  • Digital Assistants
  • Process Automation
  • Decision Support

The same trusted foundation is reused across every AI initiative — from analytics and dashboards to agents and executive decisions.

Why Latttice

Most AI platforms focus on models. Most data platforms focus on data. Latttice connects the two — creating trusted Data Products that give AI the business context, governance and quality it needs to deliver reliable enterprise outcomes.

Rather than building isolated AI solutions, organizations build a reusable foundation that powers every future AI initiative.

Every successful AI initiative begins with trusted data.

Build the trusted foundation once. Reuse it across every model, agent and decision.

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