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— OPERATIONAL · DATA

Data that decides, not just reports.

Editorial hub on the operational layer of AI in the enterprise: how to build a data foundation models can rely on, how to turn dashboards from reports into action recommendations, and how to automate market monitoring. Three pillar studies, each with verifiable figures and primary sources — Gartner, McKinsey, Deloitte.

  • P·01

    Data Foundation

    AI-ready data architecture: versioned ETL, a central warehouse, vector stores and embeddings on your critical sources, exposed to agents via MCP. Without it, 60% of AI projects fail (Gartner).

  • P·02

    Decision Hub

    Real-time decision intelligence: dashboards that recommend actions, predictive scoring and integrated alerting. The difference between seeing a chart and knowing what to do with it.

  • P·03

    Market Command

    Automated market intelligence: competitors, prices, trends and LLM visibility monitored continuously. Daily refresh, weekly reports, signals that matter filtered from the noise.

Studies

Three pillar pieces, one per product line. Quality over volume — we publish when we have data and a clear angle.
In the pipeline
  • ERP + CRM as a single source of truth — connecting systems without a new silo

    in the pipeline
  • B2B compliance and the EU AI Act — what to log in an AI decision system

    in the pipeline
  • MCP in production — how to safely expose internal data to AI agents

    in the pipeline