Microsoft Fabric IQUnified intelligence on Fabric

Help enterprises move from a unified data platform on Microsoft Fabric to unified intelligence—where data, business meaning, planning, and autonomous agents share one semantic foundation.

CognitiveBricks implements Fabric IQ as a consulting and engineering engagement: assess readiness, shape ontology and graph models, wire planning workflows, and deploy governed data and operations agents on your Fabric estate.

Target outcome

Enable contextual intelligence across the business:

• Shared business semantics across analytics and operations
• Multi-hop dependency discovery via graph queries
• Dynamic plans and scenarios on trusted Fabric data
• Domain-specific data agents for plain-language insights
• Operations agents that monitor, reason, and act with explainability
• Alignment with enterprise AI readiness and governance

Intelligence flow

Extend semantic models

Push existing Power BI semantic models beyond dashboards—into operations, planning, and agent grounding—so the same definitions power reports, workflows, and autonomous decisions.

Ontology

Model the things that matter to your business: entities, relationships, governing rules, and permitted actions. We jump-start ontology from your semantic models or OneLake tables.

• Entity and relationship catalog
• Business rules and constraints
• Action definitions for agents
• Alignment with existing data contracts

Graph

Explore interconnected entities with visual, no-code queries—surfacing multi-hop dependencies that flat tables cannot reveal (supply chain, customer journeys, system blast radius).

Enterprise planning

Build dynamic plans, forecasts, and what-if scenarios directly on Fabric data—keeping finance and business teams aligned on a single trusted foundation.

Data & operations agents

Deploy Fabric data agents as domain experts for complex questions in plain language, and operations agents that monitor live signals, detect anomalies, and take precise, explainable actions.

Establish a governed OneLake estate with clear domains, lineage, and capacity planning—prerequisites before layering IQ semantics and agents.

• Workspace and capacity topology
• OneLake shortcuts, lakehouses, and warehouse boundaries
• Security, Purview lineage, and entitlements
• Semantic model inventory and ownership

Shape your first ontology from Power BI semantic models or OneLake tables. Define entities, relationships, and action surfaces agents can safely use.

• Semantic model extension strategy
• Ontology authoring workshops with domain owners
• Validation against source-of-truth datasets
• Versioning and change management

Enable graph exploration for dependency analysis, impact assessment, and cross-domain discovery—complementing traditional BI with connected intelligence.

• Graph query patterns for key use cases
• Integration with operational metadata
• Lineage from OneLake through semantic layer to IQ

Connect planning workflows to the same trusted Fabric datasets used in analytics—reducing reconciliation friction between finance, ops, and data teams.

• Plan model design on Fabric IQ Plan
• Scenario and forecast templates
• Governance for plan vs. actual reconciliation

Deploy data and operations agents with enterprise guardrails: entitlements, observability, human-in-the-loop for high-impact actions, and integration with Microsoft Foundry where required.

• Agent grounding on ontology + graph context
• Prompt and tool policies per domain
• Evaluation harnesses and regression suites
• Production monitoring and cost controls

Fabric IQ implementations succeed when data platform maturity, governance, and agent readiness are scored upfront. We map your engagement to Cognara AI Readiness vectors—data, architecture, governance, talent, and value realization.

• Pre-implementation readiness assessment
• Gap analysis against L1–L5 maturity targets
• Prioritized roadmap tied to Fabric IQ phases
• Ongoing governance and risk monitoring

Treat Fabric IQ as a semantic and agent layer—not a standalone product rollout. Start with one high-value domain, prove ontology + graph + agent value on real OneLake data, then expand domains with shared governance.

Pair implementation with AI Readiness scoring and ontology best practices so intelligence scales without fragmenting meaning across teams.