AI-Enabled Power BI Consulting Services

End-to-end Power BI consulting services cover analytics strategy and assessment, data modelling and DAX development, Power Query and data transformation engineering, report and dashboard design, Power BI Embedded integration, Microsoft Fabric architecture, governance and Centre of Excellence setup, performance optimisation, migration from legacy BI platforms and ongoing managed analytics operations. The best engagements also include a data source audit and stakeholder decision mapping before any model work begins.

A focused Power BI engagement, a dashboard build or a data model migration: $15,000 to $60,000. A mid-complexity Power BI programme across multiple departments or data sources: $60,000 to $200,000. An enterprise Power BI platform with Microsoft Fabric integration, embedded analytics and Centre of Excellence governance: $200,000 to $750,000+. Ongoing managed analytics operations typically run $5,000 to $30,000 per month. We provide detailed estimates after a proper discovery. We won't quote a number before we can stand behind it.

A focused dashboard build: 3-6 weeks. A mid-complexity data model and report set across multiple business areas: 2-4 months. An enterprise programme with Fabric integration, migration and governance: 6-18 months, delivered in phases. We provide milestone-based timelines in writing before any development begins.

Microsoft Fabric is Microsoft's unified data platform that brings together data engineering, data science, real-time analytics, data warehousing and Power BI under one roof with OneLake as the single storage layer. For Power BI users, Fabric's most immediate impact is Direct Lake mode, which lets Power BI report query data directly from a Lakehouse at import-like speed without copying data into a dataset. Organisations that design for Fabric now avoid expensive re-platforming when it becomes the default.

Power BI Desktop is the free authoring tool where models and reports are built. Power BI Pro is the per-user licence for publishing and consuming reports in the Power BI service. Power BI Premium Per User or Premium capacity unlocks larger dataset limits, paginated reports, deployment pipelines, XMLA endpoint access and advanced AI features. Microsoft Fabric is the full data platform layer that includes Power BI as its reporting surface plus compute for data engineering, data science and real-time analytics. The right licensing model depends on your user count, your data volumes and your governance requirements. We help you land on the right architecture before you commit.

Yes. Our Dedicated Power BI Team model lets you hire dedicated Power BI developers who work exclusively on your analytics environment. Hand-picked from our senior engineering bench, embedded in your sprint cadence and accountable to your roadmap, with AI-assisted delivery built into how they work. You get the depth of a senior in-house analytics team without the recruiting cycle, the overhead or the runway risk of full-time hires.

Power BI Embedded lets you integrate Power BI reports and dashboards directly into your own web applications, SaaS products or enterprise portals, so your users see analytics in context without navigating to a separate BI tool. It uses service principal authentication, Row-Level Security for multi-tenant data isolation and the Power BI REST API for programmatic control. If your product needs to surface data to end customers or internal users in a branded, integrated experience, Embedded is almost certainly the right approach.

Governance at enterprise scale means workspace naming conventions and ownership policies, dataset certification and endorsement workflows, deployment pipelines for promoting content through dev, test and production environments, row-level and object-level security architectures, sensitivity labels aligned to Microsoft Purview, tenant-level admin settings and the usage analytics practice that tells you which reports drive decisions and which are accumulating refresh failures no one noticed. We design Power BI Centre of Excellence frameworks that turn governance from a one-time audit into an ongoing operational discipline.

Yes. We've migrated from SSRS, Crystal Reports, Tableau, Qlik, Looker and legacy Power BI deployments to modern Power BI and Microsoft Fabric architectures. The migration approach covers legacy measure logic mapped and rewritten in DAX, report parity validated before cutover, data source connections modernised and governance controls applied so the new environment starts clean. We phase the migration to keep production reporting running throughout.

Performance optimisation starts with the data model, not the visual. Query folding audit in Power Query, storage mode review, aggregations for large DirectQuery tables, DAX query plan analysis in DAX Studio, Vertipaq Analyser for model size reduction, calculation group efficiency and report-level profiling to identify slow visuals and their causes. Most performance problems in Power BI trace back to model design decisions made early in the project. We address them at the root, not with workarounds.

Power BI natively supports Smart Narratives for AI-generated text summaries, Anomaly Detection for time-series visuals, Q\&A for natural language queries and Decomposition Tree for exploratory analysis. Beyond native features, we integrate Azure Machine Learning models as report visuals, embed Python and R visuals for custom statistical modelling, configure Power BI Copilot for conversational analytics and connect Azure Cognitive Services for text analytics, sentiment and image recognition inside your reports. If your analytics needs to tell users what to do, not just what happened, AI integration is where that capability lives.

Yes. Managed Power BI operations covers dataset refresh monitoring, failure alerting and resolution, gateway maintenance, performance reviews, security audit, usage analytics reporting, stakeholder enablement and quarterly model review sessions. You can hand over the operational load entirely or run it alongside your in-house team. SLAs are defined per engagement and reported monthly.