AI Enablement

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We bring the infrastructure, MLOps discipline, and governance needed to turn AI from a demo into a durable business outcome.
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AI Enablement Solutions

End-to-end AI enablement across model development, deployment, and operations, built with engineering discipline, governance, and responsible AI controls from day one.

AI Strategy & Use Case Selection

Structured use case planning ranks AI initiatives by feasibility, value, data readiness, and operational fit before investment moves forward.

AI Infrastructure & Platform Design

Infrastructure design that defines compute, storage, networking, and orchestration so AI workloads scale reliably from pilot to production.

Model Deployment & Machine Learning Ops

Deployment planning that validates serving, monitoring, retraining, and operational patterns before models move into live workflows.

Generative AI & Foundation Models

Architecture patterns for generative AI and foundation models with grounding, evaluation, security, and cost controls from the start.

Cost Management & AI Economics

Rightsizing, capacity planning, and cost allocation practices that keep AI spend tied to usage, performance, and business value.

Responsible AI, Risk & Governance

Governance, risk, and policy controls built into AI workflows so models stay secure, explainable, compliant, and trusted.

Client OutcomeHow Paragon Micro Delivers

A large enterprise used an acquisition integration project to organize business data, strengthen tenant structure, and create a cleaner foundation for future AI enablement.

The Situation

The customer needed to move users, mail, and documents from an acquired Google environment into its primary Microsoft tenant.
The customer also needed secure access across Teams and SharePoint while supporting collaboration across multiple subsidiaries, tenants, and business units.

The Outcome

Paragon Micro Solution Architects supported discovery, migration planning, source review, domain integration, cutover setup, testing, and documentation.
The customer gained cleaner data structures, improved source connectivity, stronger access control, and a more reliable foundation for future analytics, automation, and AI use cases.
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Tenants Connected
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Data Migrated Into Microsoft Tenant
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Foundation Established
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Tenants Connected
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Data Migrated Into Microsoft Tenant
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Foundation Established

Components: Acquisition Integration | Google Cloud To Microsoft Migration | Mail Migration | Document Migration | Microsoft Tenant Integration | Intune | Autopilot | B2B Guest Access | Teams | SharePoint | BitTitan User Migration Bundle | Microsoft Licensing | Testing | Documentation

Customer Success Highlight

“Paragon Micro helped turn an acquisition integration challenge into a cleaner data foundation, giving our teams better access, stronger collaboration, and a practical path toward future AI enablement.”
https://solutions.paragonmicro.com/wp-content/uploads/2026/04/Paragon-Micro-logo_black-320x100.png

How We Help Build the Right AI Solution for You

Our AI specialists turn use case priorities, infrastructure gaps, and governance requirements into a practical enablement plan built around your data, models, workflows, and goals, without wasted pilots, platform sprawl, or a one size fits all approach.
Pat Engle
SOLUTIONS AREA DIRECTOR

Pat Engle

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Mitch Krombach
SOLUTIONS ARCHITECT

Mitch Krombach

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Jeremy Wise
SOLUTIONS ARCHITECT

Jeremy Wise

Biography
Shane Painter
SOLUTIONS ARCHITECT

Shane Painter

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Through dependable partnerships with AI, data, cloud, and infrastructure leaders, Paragon Micro delivers practical AI enablement solutions built for production.
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NVIDIA
Microsoft Azure AI
Databricks
Hugging Face
NVIDIA
Microsoft Azure AI
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Hugging Face

FAQsAI Strategy & Use Case Selection

How do we prioritize AI use cases at scale?

Rank each use case by business value, data readiness, risk, feasibility, and deployment path. The best AI projects solve a real operational problem and have the data, ownership, and workflow fit to reach production.

When should predictive AI give way to generative approaches?

Use predictive AI when the goal is scoring, forecasting, classification, or pattern detection. Use generative AI when the goal involves language, summarization, content creation, knowledge retrieval, or assisted decision support.

How do we align existing standards with AI architecture?

Map current standards for data, security, access, compliance, and operations to the AI environment. Keep standards that reduce risk, update what blocks scale, and retire controls that no longer fit.

FAQsAI Infrastructure & Platform Design

How do we design AI infrastructure for different business units?

Start with shared infrastructure standards, then tailor compute, storage, access, and deployment patterns around each business unit’s workloads, data sensitivity, and performance needs.

How do we design AI infrastructure for different business units?

Define approved platforms, reusable architecture patterns, cost controls, access rules, and deployment paths before teams build separately. Sprawl starts when each team builds its own AI infrastructure.

Should centralized AI infrastructure give way to federated platforms?

Federated AI platforms make sense when business units need flexibility, but central IT still needs governance, cost visibility, and security control. The right model balances shared standards with local execution.

FAQsModel Deployment & MLOps

How do we identify model dependencies before deployment?

Map training data, feature pipelines, APIs, storage, runtime environments, security rules, monitoring needs, and downstream workflows before release. Models should not enter production until dependencies are tested and owned.

How do we deploy models when accuracy drift is not acceptable?

Use controlled rollout, baseline testing, drift monitoring, approval gates, and rollback paths. Critical models need ongoing validation, not a one-time launch review.

What do we do when models fail validation after deployment?

Pause expansion, isolate the failure, compare expected and actual behavior, review inputs and model logic, then retrain, rollback, or redeploy based on risk. A validation failure requires a controlled response plan.

FAQsGenerative AI & Foundation Models

Is the generative AI portfolio strategy or just sprawl?

It is a strategy when use cases have owners, guardrails, measurable outcomes, and approved platforms. It is a sprawl when teams launch disconnected tools without governance, cost control, or security review.

How do we unify predictive and generative AI operations?

Use shared governance, monitoring, access controls, deployment standards, and cost visibility across both model types. Predictive and generative AI differ technically, but both need disciplined operations.

Is model portability realistic or over engineered?

Model portability is useful when it supports risk reduction, vendor flexibility, or deployment choice. It becomes over-engineered when portability is pursued without a business or operational need.

FAQsCost Management & AI Economics

How do we turn AI cost recommendations into action?

Tie each recommendation to a workload owner, budget, performance target, and approval path. AI cost control works when finance, IT, and business teams act from the same usage data.

How do we handle GPU capacity without overcommitting?

Plan GPU capacity around real workload demand, utilization history, growth forecasts, and deployment timelines. Reserve what is predictable, keep flexibility for what is still experimental.

How do we make AI cost allocation change behavior?

Show AI spend by team, model, workload, environment, and outcome. When teams see what they consume and what it costs, better design and usage decisions follow.

FAQsResponsible AI, Risk & Governance

How do we unify AI policy enforcement without disruption?

Start with baseline controls for data use, access, model approval, monitoring, and human oversight. Then, phase enforcement by risk level so governance improves without blocking useful work.

How do we handle multiple AI regulations without duplicate controls?

Map regulatory requirements to a shared AI control library. One well-designed control should satisfy multiple obligations and make evidence easier to manage.

How do we keep governance baselines current as AI capabilities evolve?

Review policies, model behavior, access, data use, logging, and risk controls regularly. AI governance has to evolve with the systems it protects.

DISCUSS YOUR NEXT DECISION

Connect with Paragon Micro to plan, design, and deliver AI enablement solutions aligned to your data, infrastructure, workflows, and future use cases.