Service Detail

Enterprise AI Architecture & Governance

AI can be a competitive advantage — or a costly set of pilots that never scale. Nexinc helps you design an enterprise-grade AI architecture with governance, integration patterns, and MLOps so AI becomes a trusted capability across retail, manufacturing, and CPG.

Enterprise AI Blueprint Governance & Risk Controls MLOps Operating Model API + EDA Integration

Who this is for

CIOs, CDOs, product leaders, enterprise architects, and data/AI teams who need AI to scale safely into core operations — not stay in labs.

Typical triggers

• Many AI pilots, limited production impact.
• Data quality, access, and ownership issues blocking AI.
• Risk/compliance concerns (privacy, model risk, auditability).
• Need real-time decisions (inventory, demand, quality, pricing).

Engagement length

Typical engagements run 4–8 weeks, depending on current maturity, system landscape, and the depth of blueprint and governance required.

What we do in this engagement

• Translate business priorities into an AI opportunity portfolio tied to capabilities, value streams, and measurable outcomes.
• Design an Enterprise AI Reference Architecture: data foundation, feature layer, model lifecycle, serving patterns, monitoring.
• Define governance guardrails: data lineage, access controls, model risk controls, auditability, compliance-by-design.
• Establish a pragmatic MLOps operating model: CI/CD for models, versioning, approval gates, rollback, drift monitoring.
• Design integration patterns to embed AI into workflows: APIs, event-driven signals (EDA), and decision/automation loops.
• Produce an execution roadmap: enablers first (data/platform), then use-case waves with owners and KPIs.

What you get as concrete outputs

• An Enterprise AI Architecture pack (diagrams + patterns) you can socialize with leadership and teams.
• An AI governance framework: roles, decision rights, controls, model lifecycle gates, and runbook basics.
• An AI-ready integration blueprint (API + event-driven patterns) for trusted signals and automation.
• A prioritized AI use-case portfolio with value sizing, feasibility, and dependencies.
• A practical roadmap (waves) with owners, timelines, and measurable KPIs for impact tracking.
• A presentation-ready exec deck that aligns business and technology on what to build and why.

Examples in retail, manufacturing, and CPG

Retail

• Demand sensing & replenishment recommendations.
• Returns intelligence (reason clustering, fraud signals).
• Promotion analytics and markdown optimization.

Manufacturing

• Predictive maintenance from sensor/IoT telemetry.
• Defect detection and quality alerts integrated into MES.
• Throughput optimization with constraint analysis.

CPG

• Forecast improvements using demand signals and promos.
• Trade spend effectiveness and channel mix optimization.
• Product master syndication with AI enrichment.

We select use cases based on measurable outcomes, data readiness, process readiness, and risk — then design the architecture so success is repeatable across domains.

Outcomes you can expect

Pilot-to-production speed

A clear blueprint and operating model that prevents “rebuild in production” and accelerates AI adoption across teams.

Trust & governance

Practical controls for data access, lineage, approval gates, and monitoring — so leadership can scale AI with confidence.

Operational integration

AI embedded into real workflows using APIs and event-driven patterns, enabling automation loops (signals → decisions → actions).

How we typically work with you

1. Align on outcomes. We map priority business outcomes to capabilities and identify AI opportunities with measurable KPIs.
2. Assess readiness. We review data sources, quality, ownership, security constraints, and current platform/tooling to define what’s feasible.
3. Design the blueprint. We produce the enterprise AI reference architecture (data, features, models, serving patterns) plus integration patterns (API + EDA).
4. Govern and operationalize. We define roles, approval gates, monitoring, and the MLOps operating model — then build a phased roadmap to execute.

A practical principle:

We focus on “just enough architecture” to scale AI safely — without slowing down delivery teams.

Design AI that scales in your enterprise

In a 45-minute call we’ll discuss your AI ambitions, current landscape, and where architecture and governance can unlock real outcomes.

Schedule a Discovery Call

Or email us at info@nexinc.consulting.