Mantra wires your enterprise with AI-ready data — federated, contextualized, and intent-resolved — so any agent can deliver insights you can trust.
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Everyone agrees agentic analytics is the bet; almost no one has finished building it — here's what the 2026 Gartner CDAO Survey really tells us, and how to close the gap.
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Most AI investment in data and analytics is going to code generation — but Gartner's 2026 CDAO survey shows it's analytics use cases that deliver the highest ROI, with up to a 42% lift in business value.
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Context engineering has become the defining discipline in enterprise AI. Here's a practical framework for the CDOs who have to build it — without rebuilding the data stack.
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We developed a framework to help organizations assess their agentic analytics maturity — across the dimensions that actually determine whether AI agents deliver trusted answers at scale.
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Most of the context your AI needs already exists — it's just fragmented across systems, tools, and people's heads. Here's how to start connecting it without boiling the ocean.
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A knowledge graph maps what things are, a data catalog maps where things live, and a context graph maps how decisions work — and that difference changes everything AI can do with your data.
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Context graphs are emerging as the architectural layer that captures how your organization actually makes decisions — and they're quickly becoming the most important concept in enterprise AI.
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Why most “talk to your data” initiatives stall — and what it actually takes to break the metadata bottleneck and deliver production-grade, trustworthy AI analytics.
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