EPC Group's framework for multi-model AI orchestration + governance-enforced canonical answer. Six pillars + fixed-fee implementation tiers.
Last updated by Errin O'Connor, Founder & Chief AI Architect, EPC Group
Multiple Models, One Truth is EPC Group's enterprise AI methodology. Enterprises will run multiple AI models (Copilot + Copilot Studio + Custom Engine + third-party ChatGPT/Claude Enterprise) — but need a single governed canonical source of truth. Six pillars: canonical data (Fabric OneLake + Purview), approved-model list, unified policy (labels + DLP + Insider Risk + BYOAI), consolidated audit, response quality framework, cost + ROI attribution. Six adoption triggers: post-pilot rollout, BYOAI discovery, data quality initiative, regulatory pressure (EU AI Act, NAIC), cost optimization, board reporting. EPC Group tiers: Discovery, Foundation, Advanced, a monthly vCAIO Retainer and the EU AI Act Compliance Program — each a fixed fee set at scoping.
Multiple Models, One Truth is EPC Group's enterprise AI methodology that acknowledges enterprises will run multiple AI models simultaneously (Microsoft Copilot + Copilot Studio + Custom Engine Agents on Azure AI Foundry + third-party approved-list tools like ChatGPT Enterprise or Claude Enterprise) while establishing a single governed canonical source of truth for enterprise data + prompts + audit trails. The tagline captures the paradox: enterprises need model diversity (best-of-breed capabilities, resilience against vendor lock-in, cost optimization) AND a single authoritative source (governed data, consistent policy enforcement, one audit log, one incident response).
Six drivers for multi-model AI enterprise deployment: (1) Best-of-breed — different models excel at different tasks (Copilot for M365 productivity, Claude for reasoning + code, GPT for creative + general, Gemini for multi-modal). (2) Vendor resilience — no single-vendor dependency for AI capability. (3) Cost optimization — cheaper models for high-volume tasks + premium models for high-value tasks. (4) Feature latency — some models ship features first (OpenAI often leads; Anthropic often leads reasoning + coding). (5) Compliance profile — some models fit specific regulated deployments better. (6) User cohort fit — technical users may prefer different models than business users. Enterprises without multi-model strategy end up with shadow AI (uncontrolled BYOAI).
Six enforcement mechanisms: (1) Canonical data layer — Microsoft Fabric OneLake as the single enterprise data source, Purview data governance. (2) Sensitivity labels — Purview labels applied to content regardless of which AI model queries. (3) DLP + Insider Risk — data protection layer independent of AI model. (4) Approved-tool list + BYOAI policy — enforced via Purview Cloud App Discovery + Endpoint DLP. (5) Audit trails — consolidated audit log across all AI use (Copilot audit + Custom Engine logging + third-party enterprise contract logging). (6) Response quality governance — same evaluation framework applied to Copilot, Copilot Studio, Custom Engine, and approved third-party responses.
EPC Group Multiple Models, One Truth six pillars: (1) Canonical Data — Fabric OneLake + Purview governance as single source. (2) Approved-Model List — governed inventory of Copilot + Copilot Studio + Custom Engine + third-party tools. (3) Unified Policy — sensitivity labels + DLP + Insider Risk + BYOAI apply to all models. (4) Consolidated Audit — single audit log across all AI use for compliance + investigation. (5) Response Quality Framework — consistent evaluation + monitoring across models (bias, hallucination, drift, safety). (6) Cost + ROI Attribution — unified cost + value tracking across model mix. Each pillar has technical implementation + policy documentation + ongoing governance.
Models change constantly — new model releases, deprecations, capability upgrades. Framework handling: (1) Model registry — canonical inventory of approved models + version + capability + cost. (2) Model evaluation cadence — quarterly review of new model releases against use cases. (3) Deprecation planning — schedule model transitions with user communication + retraining. (4) A/B testing — evaluate new models on real workloads before commitment. (5) Vendor risk monitoring — track vendor roadmap + pricing + policy changes. (6) Model performance dashboards — monitor drift + quality per model. Framework isolates enterprise from model volatility while capturing best-of-breed value.
Six triggers for adopting Multiple Models, One Truth: (1) Copilot rollout past pilot — enterprise has moved beyond single-tool experimentation. (2) BYOAI discovered — Purview + Cloud App Discovery reveals employees using ChatGPT/Claude/Gemini for work. (3) Data quality initiative — enterprise realizes AI response quality depends on data quality + governance. (4) Regulatory pressure — EU AI Act, NAIC AI Bulletin, state privacy laws demand documented AI governance. (5) Cost optimization — enterprise AI spend growing without governance = uncontrolled expansion. (6) Board reporting — executive team needs unified AI governance narrative. Typical enterprise adopts framework in 12-18 months following initial Copilot rollout.
EPC Group Multiple Models, One Truth implementation: (1) Discovery + Design (4 weeks) — current-state AI use inventory + gap analysis + framework roadmap. (2) Foundation Deployment (12 weeks) — canonical data layer + approved-model list + unified policy + audit consolidation + baseline dashboards. (3) Advanced Implementation (12 weeks) — response quality framework + cost/ROI attribution + advanced monitoring + red-teaming program. (4) vCAIO Retainer — monthly, scoped to the mandate — ongoing framework governance + quarterly reviews + regulatory tracking. (5) EU AI Act Compliance Program (16-24 weeks) — full EU AI Act readiness within the framework. Each engagement is a fixed fee quoted after a scoping call. All led by Chief AI Architect Errin O'Connor and senior compliance architect team.
4-week fixed fee: current-state + gap analysis + framework roadmap. Call (888) 381-9725.
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