EPC Group Pioneers Multi-Model AI Architecture for Power BI, Solving What Was Called an Unsolvable Problem

EPC Group’s six-layer AI architecture—fresh off perfect G2 Spring 2026 scores in Business Intelligence Consulting—turns your data into AI-ready intelligence.

HOUSTON, TX, UNITED STATES, March 26, 2026 /EINPresswire.com/ -- EPC Group, the AI and business intelligence consulting firm behind the Multi-Model AI Power BI solution that generated widespread industry attention within its first week of announcement, today revealed expanded capabilities of its six-layer AI architecture that are driving 70% faster user adoption rates, significant licensing cost reductions, and a fundamentally new approach to making enterprise data AI-ready across Microsoft Fabric and Power BI environments.

The announcement comes on the heels of EPC Group earning perfect G2 Spring 2026 scores in Business Intelligence Consulting, including a Net Promoter Score of 100, a 100 percent Expertise of Team rating, and a top five Market Presence ranking on the G2 Grid for the category. Earning a perfect Net Promoter Score of 100 means every client surveyed said they would recommend EPC Group, reinforcing the firm’s position as a trusted partner for complex analytics and AI initiatives.

This new Multi-Model AI update also follows EPC Group's initial architecture release, which was covered by technology media outlets across North America and generated a 480% spike in industry mentions with exclusively positive sentiment, according to Brand24 AI monitoring data. Enterprise inquiries surged as organizations recognized the gap between what Microsoft Copilot delivers out of the box and what a purpose-built multi-model AI architecture can achieve when layered on top of it.

"Everyone bought Copilot licenses expecting magic. What they got was a starting point," said Errin O'Connor, Founder and Chief AI Architect of EPC Group, recognized as one of the top AI architects in North America. "Most organizations are paying for Copilot licenses across their entire user base and only seeing a fraction of the value. We built the other five layers that turn Power BI from a reporting tool into an enterprise decision intelligence platform. The result is measurable: our clients are seeing 70% faster user adoption because people can actually interact with their data through natural language across multiple AI models — not just one. The perfect G2 scores are external validation that this approach works in the real world."

The licensing impact is substantial. Organizations deploying Power BI at enterprise scale typically face a critical licensing decision: purchase Pro licenses at "xyz" dollars per user per month for every viewer, or invest in Premium or Microsoft Fabric capacity to eliminate per-user viewer costs entirely. EPC Group's architecture is designed from the ground up for Microsoft Fabric capacity, enabling organizations to shift from per-user licensing models to capacity-based models that can reduce total Power BI licensing spend by 50 to 70% for organizations with large viewer populations. For a company with 500 report viewers, this translates to annual savings exceeding "xyz" dollars in licensing costs alone — before factoring in the productivity gains from AI-augmented analytics.

"We're not just implementing dashboards," O'Connor continued. "We're restructuring how organizations license, govern, and consume analytics. When you architect Power BI correctly from the start — optimized for Fabric capacity, governed for multi-model AI, and designed for self-service at scale — the licensing math changes completely. Our clients stop overpaying for licenses that users weren't even utilizing."

The six-layer architecture addresses the single biggest barrier enterprises face with AI-powered analytics: their data is not AI-ready. EPC Group's methodology begins with a comprehensive data readiness assessment that evaluates semantic model design, data warehouse architecture, metadata quality, lineage documentation, and governance maturity. Without this foundation, AI models — whether Copilot, Claude, Perplexity, or OpenAI — produce unreliable or hallucinated outputs because they lack the structured, governed data context required for accurate enterprise analytics.

Layer 1 deploys Microsoft Copilot for Power BI as the natural language interface, enabling business users to query datasets conversationally.

Layer 2 standardizes native AI visuals including Key Influencers, Decomposition Trees, and Smart Narratives through a governed pattern library that ensures explainable AI across every dashboard.

Layer 3 embeds automated machine learning directly into Microsoft Fabric Dataflows, allowing business units to build churn prediction, revenue forecasting, and demand planning models without dedicated data science teams.

Layer 4 is where the architecture diverges from anything Microsoft offers natively. EPC Group integrates Azure OpenAI, Claude, Perplexity, and open-source frameworks including Meta Llama and Mistral through secure API orchestration and vector search. This enables retrieval-augmented generation across enterprise knowledge bases, allowing executives to ask complex, multi-step questions that span structured data in Power BI and unstructured data in SharePoint, Teams, and Azure Data Lake.

Layer 5 adds Microsoft Cognitive Services for entity extraction, sentiment analysis, and document intelligence, transforming qualitative data from support tickets, customer feedback, and contract documents into quantifiable business metrics.

Layer 6 delivers automated insights, anomaly detection, and predictive forecasting through executive dashboards that proactively surface business risks and opportunities before leadership teams even ask the question.

The 70% improvement in user adoption stems directly from this multi-model approach. When users can interact with data through whichever AI model best suits their query — Copilot for quick report generation, Claude for nuanced analytical reasoning, Perplexity for research-backed market context, and OpenAI for complex data transformation — adoption barriers collapse. Users who previously ignored Power BI dashboards because they could not write DAX or understand data models now engage daily through natural language interfaces that meet them where they are.

EPC Group has completed over 1,500 Power BI implementations and more than 5,200 Microsoft platform deployments across Fortune 500 companies, federal agencies, healthcare systems, and mid-market enterprises. The firm's consulting services span Power BI architecture, Microsoft Fabric implementation, enterprise data modeling, AI governance frameworks, licensing optimization, and scalable deployment automation.

For more information about EPC Group's Multi-Model AI Power BI solution, G2-recognized Business Intelligence Consulting services, and Microsoft Fabric consulting capabilities, email contact@epcgroup.net or visit epcgroup.net/power-bi-consulting or call (888) 381-9725.

About EPC Group

EPC Group is a Houston-based AI and Microsoft Cloud consulting firm founded by Errin O'Connor, a recognized AI thought leader and author of multiple best-selling Microsoft Press books including Microsoft Power BI Dashboards Step by Step. The firm specializes in enterprise AI architecture, Multi-Model AI for Power BI, Microsoft Fabric, Power Platform development,

Michelle Stevens
EPC Group
+1 888-381-9725
contact@epcgroup.net
Visit us on social media:
LinkedIn
Bluesky
Instagram
Facebook
YouTube
TikTok
X
Other

Are Your Employees Hiding AI Secrets? Why EPC Group Is the Leading AI Consulting Firm Uncovering What Others Can’t

Legal Disclaimer:

EIN Presswire provides this news content "as is" without warranty of any kind. We do not accept any responsibility or liability for the accuracy, content, images, videos, licenses, completeness, legality, or reliability of the information contained in this article. If you have any complaints or copyright issues related to this article, kindly contact the author above.