The 90-Day AI Capability Center Blueprint
From Zero to Production AI in 90 Days
Building an AI capability center is an engineering challenge, not a research project. The outcome is specific: a team, a platform, and a model in production within 90 days. This blueprint comes from dozens of deployments across industries. Every phase has clear deliverables and defined roles.
Schedule an AI Readiness AssessmentThe 6-Phase Journey
Phase 1: Discovery & Assessment
This phase defines what success looks like before any code is written.
What happens
- We assess your current AI maturity, data infrastructure, and organizational readiness through structured interviews and technical review
- Use cases are ranked by business impact, data availability, and technical feasibility. The top three move forward.
- KPIs are tied to revenue, cost, or efficiency gains. Each one gets a baseline measurement so progress is trackable from week one.
- A gap analysis maps what exists today against what the capability center requires
Deliverables
- AI Maturity Assessment Report
- Prioritized use case roadmap (ranked by impact and feasibility)
- KPI framework with baseline measurements
- Infrastructure gap analysis
- Preliminary team composition plan
Allerin's role
Lead assessments, facilitate stakeholder interviews, deliver the assessment report and roadmap.
Your role
Provide access to data systems, stakeholders, and business context. Designate an internal sponsor.
Success Criteria
Stakeholder alignment on the top 3 use cases and KPI targets for the first 12 months.
Phase 2: Architecture & Design
The technical foundation designed here needs to support years of use cases, not just the first project.
What happens
- Design the MLOps pipeline end-to-end: training, validation, deployment, monitoring, retraining
- Make infrastructure decisions covering cloud vs. hybrid, compute sizing, storage, and networking
- Select tooling for the model registry, experiment tracking, feature store, CI/CD, and monitoring
- Map security and compliance requirements: data governance, access controls, audit trails
- Define integration points between the AI platform and your existing systems
Deliverables
- MLOps Architecture Document (pipeline diagrams, tooling decisions, integration points)
- Infrastructure specification and cost model
- Security and compliance plan
- Data pipeline design
- Technology decision log with rationale
Allerin's role
Design the architecture, evaluate tooling options, deliver technical documentation.
Your role
Review and approve architecture decisions. Provide infrastructure access and security requirements.
Success Criteria
Architecture approved by your technical leadership. Infrastructure provisioning initiated.
Phase 3: Team Assembly
Building a team that understands your domain, your data, and how you define success takes deliberate effort beyond just filling seats.
What happens
- Roles are defined against the architecture and roadmap: ML engineers, data engineers, MLOps specialists, domain analysts
- Candidates come from Allerin's engineering network in Mumbai, screened for production AI experience
- Each hire is technically vetted against your specific stack and domain
- Onboarding covers domain training, codebase orientation, process alignment, and stakeholder introductions
- Communication protocols, sprint cadence, and reporting structure are established before Sprint 1
Deliverables
- Finalized team roster with role descriptions and reporting lines
- Completed onboarding for all team members
- Domain knowledge transfer documentation
- Communication and escalation protocols
- Sprint zero planning (first sprint backlog defined)
Allerin's role
Source, screen, hire, onboard, and train the team. Manage the ramp-up.
Your role
Participate in final candidate interviews. Provide domain knowledge transfer sessions. Designate a product owner or technical lead.
Success Criteria
Full team onboarded, domain-trained, and ready to execute Sprint 1.
Phase 4: Platform Setup
By the end of this phase, the MLOps platform is operational and accepting model development workloads.
What happens
- Provision compute clusters, storage, networking, and security controls
- Implement the MLOps pipeline: CI/CD for ML, automated testing, deployment automation
- Stand up the model registry with versioning, lineage tracking, and metadata management
- Deploy monitoring infrastructure including performance dashboards, drift detection, and alerting
- Set up the feature store if applicable for centralized feature management
- Run end-to-end integration tests with sample data to validate the full pipeline
Deliverables
- Operational MLOps platform with all components deployed and tested
- CI/CD pipeline for model training and deployment
- Model registry with versioning and access controls
- Monitoring dashboards with alerting rules
- Platform runbook (operational documentation)
Allerin's role
Build, configure, test, and document the entire platform.
Your role
Provide infrastructure access and data access. Review and approve monitoring thresholds.
Success Criteria
End-to-end pipeline test passes. A sample model can be trained, validated, deployed, and monitored through the full pipeline.
Phase 5: First Production Deployment
The first model goes through the full pipeline and reaches production. KPI gates determine whether it ships.
What happens
- The team builds the first production model against the top-priority use case from Phase 1
- Before deployment, the model must clear predefined KPI gates. If it misses a threshold, it goes back for iteration.
- Deployment uses a reversible rollout. If real-world performance degrades, rollback is instant and automatic.
- Real-time monitoring tracks model predictions, business KPIs, and data quality from the first hour
- Results are presented to business stakeholders with metrics tied directly to the KPI framework
Deliverables
- First production model deployed and serving predictions
- KPI gate validation report
- Production monitoring dashboard (live)
- Rollback procedures tested and documented
- Stakeholder presentation with initial results
Allerin's role
Lead model development, manage the deployment, present results.
Your role
Provide production data access. Participate in stakeholder review. Validate business outcomes.
Success Criteria
Model in production, meeting KPI gates, with stakeholder sign-off on initial results.
Phase 6: Governance Handoff
Ownership transfers in this phase. The center operates independently from this point forward.
What happens
- All documentation is finalized: architecture docs, runbooks, operational procedures, escalation paths
- Your leadership and team receive deep technical walkthroughs covering every system component
- The governance framework goes live: model review cadence, retraining triggers, compliance checkpoints
- Operations shift from Allerin-led to Allerin-supported
- The next 6 use cases are mapped, prioritized, and ready for the team to execute independently
Deliverables
- Complete documentation package (architecture, operations, governance)
- Knowledge transfer completion certificates
- Governance framework with review cadence and compliance checkpoints
- 12-month use case roadmap
- Post-launch support plan (optional ongoing engagement)
Allerin's role
Deliver documentation, conduct knowledge transfer, define the ongoing support model.
Your role
Designate internal capability center leadership. Participate in knowledge transfer. Approve the governance framework.
Success Criteria
Internal leadership confident and ready to operate. All documentation delivered and reviewed. Support model agreed upon.
What Happens After Day 90
With the team in place, the platform operational, and the first model in production, your center is ready to tackle the remaining use cases on the roadmap. Most organizations deploy 3–5 additional models in the first 6 months after launch.
Allerin offers optional ongoing support, from embedded technical advisors to quarterly architecture reviews, scaled to your needs. Some clients operate independently after the initial buildout. Others maintain a long-term partnership.
Ready to Start?
The first step is a 60-minute AI Readiness Assessment. We'll evaluate your current state, discuss your use cases, and determine whether the 90-day model is the right fit for your organization.
Schedule Your AI Readiness Assessment