Build an AI-ready manufacturing foundation

Strategy Mosaic is a universal semantic layer. It gives discrete and process manufacturers, suppliers, and supply chain teams a governed way to define and reuse business metrics, relationships, hierarchies, and access policies across data platforms, BI tools, applications, APIs, and AI agents.

A warehouse or lakehouse stores and processes data. Mosaic governs what that data means, how it should be calculated, which relationships are approved, and who is permitted to see each result. It works across your existing storage and cloud platforms rather than asking you to replace them. No. Mosaic can query data where it resides and operate within approved public-cloud, private-cloud, or customer-managed architectures. Deployment design depends on your data platforms, security requirements, and operating model.

Yes. Mosaic is built to sit above mixed data and analytics environments, so you can reuse governed business logic across major cloud, database, lakehouse, BI, application, and AI ecosystems. Mosaic keeps business logic independent from any single warehouse, BI tool, cloud, or model provider. You can modernize your technology stack while preserving the governed definitions that users and applications rely on.

Mosaic gives AI agents certified metrics, approved relationships, reusable query logic, and user-aware security context. That reduces how often a model has to infer business meaning directly from raw schemas, a common cause of text-to-SQL hallucinations: SQL that runs successfully but applies the wrong business rule. With governed context in place, you move closer to trusted AI, where the answer follows the approved definition.

Mosaic centralizes approved definitions, applies role and data-access controls, preserves lineage, validates query behavior, and shows you who accessed what. That supports AI data governance and audit readiness for quality, supplier, and data-protection requirements, though it does not replace your legal, compliance, risk, or control processes.

Yes. Many manufacturers begin with a high-value domain such as unplanned downtime, quality and yield, supply chain visibility, or inventory planning. The governed foundation can then expand to additional plants and product lines without rebuilding the same metrics and policies.

Strategy unifies your data on one platform to eliminate silos and surface near real-time insights. It connects machines, sensors, and systems with 200+ integrations so every team, from plant managers to executives, has current data. AI-powered analytics detect bottlenecks, inventory risks, and production delays before they escalate, and pixel-perfect reports and dashboards help you spot inefficiencies in inventory, procurement, and logistics.

Strategy's semantic layer defines metrics once, with built-in lineage tracking and quality checks, so every site, system, and team works from the same numbers. Live, mobile-ready dashboards track production, inventory, and supply chain metrics across facilities, and AI-powered dashboards highlight performance gaps and optimization opportunities by site.

Strategy's cloud-native architecture grows with your business without expensive licensing or infrastructure costs. Built-in AI answers complex questions in natural language to surface delays and inefficiencies, and live dashboards track lines, shifts, and facilities to help increase throughput, reduce cycle times, and improve resource utilization.

Manufacturers use Strategy to deploy self-service analytics to thousands, or even hundreds of thousands, of users; unify KPIs from multiple systems in web and mobile apps, online or offline; automate thousands to tens of thousands of personalized, pixel-perfect report deliveries a day; move off legacy BI platforms as they sunset; support several analytics tools on one semantic layer with centralized governance; and power embedded or monetized data products.