Data & AI 75: Companies at the Convergence of Data and AI in 2026

Data & AI Trailblazer by Mosaic: The Trust Layer for Enterprise AI

STRATEGY MOSAIC makes enterprise AI more reliable by giving AI systems governed business context before they query enterprise data. As Strategy’s enterprise-grade Universal Semantic Layer, Mosaic sits between AI models and underlying data sources, centralizing business definitions, relationships, security, and query logic in one governed layer.

Instead of relying on an LLM to infer business meaning or generate SQL directly from raw schemas, Mosaic uses governed semantic definitions and deterministic query execution to translate business questions into trusted queries. This reduces inconsistent metrics, limits text-to-SQL errors, and gives employees and AI agents a common understanding of the business.

Mosaic also tackles the economics of AI at scale. Intelligent semantic caching reduces repetitive queries to underlying data platforms, while compressed semantic context gives LLMs the business information they need without sending entire raw schemas. Strategy benchmarks show 37% lower estimated cost per query and 50% fewer tokens per query, helping enterprises control both cloud compute and AI costs as usage scales.

These optimizations become increasingly important at agent scale, where automated workloads can generate far more analytical requests than individual users, magnifying the cost of every inefficient query.

Mosaic works across AI models, clouds, databases, and data lakes without requiring organizations to centralize or move their data, helping reduce vendor lock-in. Thousands of organizations worldwide rely on Strategy to deliver governed, trusted insights to employees and AI agents.

Strategy
software.strategy.com

Data & AI Trailblazer by Alok Pareek, Co-Founder and EVP of Engineering and Products

FOUNDATION MODELS are abundant, capable, and increasingly interchangeable. What separates AI that works, from AI that stalls, is the data underneath it—and in most enterprises, that data is a day old. An agent scoring fraud on last night’s batch is reasoning flawlessly about a world that has already moved on.

Striim closes that gap. Change data capture reads directly from Oracle, SQL Server, PostgreSQL, and MongoDB transaction logs—sub-
second, exactly-once, with minimal impact on the source. Schema changes propagate in flight, so adding a consumer becomes configuration rather than another pipeline. Transformations, enrichment, aggregations, and streaming SQL put decisions on the moving present instead of yesterday’s dashboard.

What makes Striim different is what runs inside the pipeline. Sherlock discovers sensitive data at the source. Sentinel masks and encrypts it in motion, before it ever reaches a model. Euclid generates vector embeddings continuously, so retrieval-augmented applications stop answering from stale indexes. Foreseer forecasts and flags anomalies on live time-series. Governance and intelligence are properties of the platform—not four more products to buy, integrate, and reconcile.

The result is data that arrives current, consistent, consumable, and contextual, delivered continuously to Snowflake, BigQuery, Databricks, Microsoft Fabric, ClickHouse and the vector stores your agents query.

Warehouses give AI a perfect memory of the past. Striim gives it senses for the present.

Striim, Inc.
www.striim.com