Enterprise GenAI Factory & Production MVP Deployment: 10-16 Weeks
Celebal Technologies Private Limited
Many organizations identify promising AI use cases but struggle to operationalize them due to infrastructure gaps, fragmented governance, inconsistent deployment practices, and limited visibility into model performance. As a result, AI initiatives often remain isolated pilots rather than scalable business capabilities.
As part of Microsoft's Azure Accelerate AI Transformation Offer (ATO), this implementation engagement focuses on establishing the enterprise foundation required to deploy, operate, and scale AI solutions in production using Microsoft's Azure AI, data, governance, and cloud platform capabilities. Celebal Technologies designs and implements AI-ready Azure environments, establishes GenAIOps capabilities, deploys a production-grade AI solution, and creates a repeatable GenAI Factory model that enables future AI use cases to be delivered with greater speed, consistency, and control.
What Gets Delivered
Architecture & Platform Foundation
- AI Architecture Blueprint and Component Interaction Design
- Azure Estate Assessment and Target-State Architecture
- AI-Ready Azure Landing Zones with secure Development, Test, and Production environments configured using Azure governance, networking, security, and infrastructure best practices
- Azure AI Foundry Configuration and Supporting Services
- Identity, Connectivity, and Security Configuration Documentation
- Policy-as-Code Governance Baseline
Data & Operational Readiness
- AI-Ready Data Foundation including ingestion pipelines, governance controls, cataloging, lineage, and access management capabilities
- GenAIOps Maturity Assessment Report
- Maturity Advancement Roadmap and Priority Action Plan
- GenAIOps Priority Framework covering automation, monitoring, compliance, Responsible AI controls, and continuous evaluation practices
- Operational Monitoring and Observability Framework including KPI dashboards, alerting, model performance tracking, and Responsible AI metrics
Production AI Deployment
- MVP Selection Framework including use-case qualification, success criteria definition, KPI alignment, and implementation planning
- Production-Deployed AI MVP
- CI/CD Pipelines for AI Model and Application Deployment
- Responsible AI Monitoring and KPI Dashboards
Factory Enablement
- GenAI Factory Cycle Playbook defining standardized intake, design, build, release, monitoring, governance, and continuous improvement processes
- Future AI Use Case Pipeline and Expansion Roadmap
Implementation Approach
Foundation Architecture
Establish the target-state architecture, assess the current Azure environment, define security and governance requirements, and design the infrastructure needed to support enterprise AI workloads.
Platform Deployment
Deploy and configure Azure-based AI environments, establish connectivity and identity controls, implement governance guardrails, and enable the supporting data ecosystem.
GenAIOps Enablement
Assess maturity, define advancement priorities, establish lifecycle management practices, and prepare the selected use case for implementation.
Production MVP Delivery
Deploy a production-grade AI solution, implement automation and observability capabilities, configure Responsible AI controls, and validate business outcomes through KPI-driven measurement.
Factory Operationalization
Establish standardized processes for use-case intake, development, deployment, monitoring, governance, and continuous improvement to support future AI use cases.
Final Outcome
At the conclusion of the engagement, your organization will have the operational capabilities, governance structure, and deployment discipline required to transform AI initiatives from isolated projects into scalable, repeatable business solutions. The engagement also establishes a production-ready GenAI Factory model, enabling future use cases to move from concept to business value through a consistent, governed, and measurable delivery approach.
Ready to get started? Contact mea.enterprise@celebaltech.com to discuss your requirements and determine the right engagement approach for your organization.
*Final pricing depends on the target deployment architecture, environment requirements, integration complexity, operational controls, and implementation scope.