As AI adoption accelerates, fragmented governance can create hidden costs, security risks, and operational complexity. This playbook explores how enterprises can establish unified governance with Databricks Unity Catalog to build a trusted foundation for scaling AI.
Topic: Data Management
Part 4: How MathCo Builds Trusted Enterprise MLOps with Databricks Unity Catalog
Enterprise MLOps can quickly become difficult to scale when ML assets lack consistent governance, cost visibility, lineage, and discoverability. Learn how MathCo uses Databricks Unity Catalog to build a governed MLOps foundation that helps enterprises operationalize ML, improve traceability, control costs, and scale trusted AI with confidence.
Part 3: How MathCo Builds Trusted Merchandising Intelligence with Databricks Unity Catalog
AI can only transform merchandising when the intelligence behind it can be trusted. Discover how MathCo uses Databricks Unity Catalog to address fragmented data, missing lineage, inconsistent metrics, and regulatory risk—creating governed, auditable merchandising intelligence that helps retailers scale AI with confidence.
Closing the Loop Around Every HCP Interaction: A Palantir Foundry Case Study in Pharma
Explore how MathCo connected HCP content and field engagement through AI-powered applications built on Palantir Foundry, creating a connected feedback loop that helps pharma team productivity, personalization, and compliance at scale.
Part 2: How MathCo Builds Trusted Retail Operations Intelligence with Databricks Unity Catalog
Retailers cannot scale autonomous AI without trusted operational data. Learn how a governed framework built on Databricks Unity Catalog bridges supply chain gaps, prevents phantom inventory, and empowers retail leaders to deploy AI agents with complete confidence.
Trust Before Autonomy: The Missing Link in Retail’s AI Evolution
Explore how MathCo evolved from a focused Palantir Foundry support engagement into a trusted engineering partner, demonstrating the platform expertise, operational excellence, and delivery discipline required to scale enterprise Foundry implementations.
The Agentic Enterprise: Own vs Rent vs Compose
AI success depends on the right architectural decisions, not just the right models. This white paper introduces a practical framework for designing future-ready, agentic AI systems across retail, manufacturing, pharma, and CPG. Learn how to strategically navigate ownership across the entire AI stack, minimize vendor lock-in, and turn proprietary workflows and context into compounding business value.
Automation vs. Augmentation: Where Enterprise AI Investment Actually Sits
Enterprise AI is moving beyond experimentation. Based on joint research by MathCo and HFS Research, this article explores the growing focus on process intelligence, the trust gap between automation and augmentation, and why transparency, explainability, and human-centered AI will determine enterprise AI adoption at scale.
From Platform Enablement to Strategic Partner: MathCo’s Palantir Foundry Expertise in Action
Explore how MathCo evolved from a focused Palantir Foundry support engagement into a trusted engineering partner, demonstrating the platform expertise, operational excellence, and delivery discipline required to scale enterprise Foundry implementations.
Reducing Enterprise Data Migration Timelines from Months to Weeks with MathCo’s Unity Catalog Implementation Accelerators
Enterprise data migrations often take 6–18 months—but much of that delay stems from implementation complexity, not scale. Learn how a governance-first approach and standardized accelerators can help organizations modernize to Databricks Unity Catalog faster and with lower risk.