MathCo Intelligent Ops uses AI agents, operational knowledge, and controlled automation to investigate incidents, identify root causes, recommend actions, and support remediation. By connecting detection, diagnosis, action, and verification, it helps teams reduce manual effort while maintaining governance and oversight.
Topic: AI/ML
Adoption Architecture: Designing Intelligence People Choose to Use
Enterprise AI struggles to deliver business value not because of technology, but because of adoption. This white paper explores how an Adoption Architecture builds trust, drives decision-making, and measures AI adoption through user behavior.
When RAG Is Not Enough: How to Decide Between Semantics, Ontology, and Knowledge Graphs
RAG alone may not be enough for complex enterprise AI queries. Discover how semantics, ontologies, knowledge graphs, and context engineering address different failure modes, and learn how to choose the right architecture for your business needs.
Building the Enterprise Data Foundation for the AI Era with Mathco’s Lakebase LTAP Accelerator
Discover how MathCo’s Lakebase LTAP Accelerator helps enterprises take Databricks Lakebase from implementation to production. This white paper explores MathCo’s reusable capabilities and eight-phase methodology for building governed, scalable real-time applications, and showcases an Always-On Marketing Mix Modeling implementation that reduced refresh time from four weeks to under four hours and cut data-preparation effort by 70%.
Beyond AI Ideation: How Leading Enterprises Build an Executable Agentic AI Roadmap in Six Weeks
Learn how leading enterprises can move from Agentic AI ideation to execution with a structured six-week approach to identifying opportunities, prioritizing investments, defining solution blueprints, and building an actionable roadmap.
Synthetic Market Simulation: Enhancing Marketing and Creative Testing
Discover how synthetic market simulation is reshaping marketing creative testing for CPG brands. MathCo’s AI-driven framework combines Nemotron Personas—a population of 1M+ synthetic personas grounded in real demographic and labor-force data—with audience-specific purchase intent simulation to evaluate concepts, claims, and creative at scale. By enabling AI consumer research and richer AI-driven consumer insights, the approach helps brands identify what resonates with their target consumers, refine creative faster, reduce research costs, and build greater confidence before launch.
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.
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.
Part 1: How MathCo Builds Trusted Marketing Intelligence with Databricks Unity Catalog
Marketing organizations are scaling AI faster than they are scaling trust in the data behind it. Learn how a governance-first framework on Databricks Unity Catalog enables marketing teams to unify fragmented customer data, trace decisions, and scale AI with confidence.