The Framework is how the Standard becomes reality. Each phase builds on the previous, with defined outputs that feed into the next. Skip a phase, and you'll pay for it later. Cut corners on governance, and you'll fail compliance. This is the discipline that makes sovereign AI actually work.
Discovery & Data Audit
Before architecture comes understanding. What data exists? Where does it live? What's sensitive? What regulations apply?
Data Inventory
Map every data source the AI will touch. Databases, documents, APIs, user inputs. Classify by sensitivity level. Identify PII, PHI, proprietary, public.
Regulatory Mapping
Which regulations apply? HIPAA, GDPR, LGPD, SOC 2, industry-specific requirements. Map each data class to its compliance obligations.
Use Case Definition
What will the AI actually do? Define inputs, outputs, user interactions, success metrics. Vague use cases produce vague systems.
Risk Assessment
What happens if the AI fails? Wrong answer in healthcare vs. wrong answer in customer support. Quantify the stakes to calibrate the safeguards.
Deliverables
Architecture Design
Select components, define routing logic, choose models. The architecture determines what's possible—and what's protected.
Component Selection
Which Stack components are needed? SmartHub or Contextual RAG? Full IVX or Chatbot Core? Match capabilities to requirements, not features to wishlist.
Model Strategy
Which models for which tasks? Local Llama 4 for sensitive reasoning, cloud API for generic summarization. Define the multi-model routing logic.
Router Configuration
Define classification rules. What triggers local vs. cloud? Keyword patterns, data types, user roles, time of day. The Router is the enforcer—configure it precisely.
Infrastructure Planning
Where will it run? On-prem GPU cluster, private cloud, hybrid? Size the compute, plan the networking, define the air-gaps.
Deliverables
Governance Layer
Compliance isn't a checkpoint. It's infrastructure. Build the logging, the audit trails, the explainability—before a single inference runs.
Logging Architecture
What gets logged, where, for how long? Input, output, model version, routing decision, latency, errors. Design for the audit you'll eventually face.
Explainability Framework
How will decisions be explained? To users, to auditors, to legal. Define the explanation format for each use case and risk level.
Compliance Rules Engine
Encode regulations as executable rules. "Never process EU citizen data on US servers" becomes enforced policy, not just documented policy.
Incident Response Design
When (not if) something fails, what happens? Alerting, rollback, forensics, communication. Design the response before you need it.
Deliverables
Integration & Legacy
AI doesn't exist in a vacuum. It connects to ERP, CRM, databases, workflows. The integration is 80% of the work.
API Design
How will other systems talk to the AI? Define endpoints, authentication, rate limits, error handling. The API is the contract.
Legacy System Connectors
SAP, Epic, Salesforce, Bloomberg—whatever exists. Build the bridges that let modern AI work with decade-old infrastructure.
Workflow Integration
Where does AI fit in human processes? Before approval? After review? Define the handoff points, the escalation paths, the human-in-loop triggers.
Data Pipeline Construction
How does data flow in and out? ETL, real-time streaming, batch processing. Build pipelines that respect the sovereignty boundaries.
Deliverables
Deployment
On-prem, private cloud, hybrid, edge. Deployment is where sovereignty becomes real—or fails.
Environment Setup
Provision infrastructure. Configure networks, security groups, access controls. Verify air-gaps. Test isolation before anything runs.
Model Deployment
Load models, configure inference servers, set up caching. Verify the model runs locally, produces expected outputs, meets latency requirements.
Governance Activation
Turn on logging, enable compliance rules, activate monitoring. Run test scenarios through the governance layer. Verify everything captures.
Staged Rollout
Pilot group first, then wider. Monitor for drift, failures, unexpected behavior. Have rollback ready. Production isn't the place to discover problems.
Deliverables
Evolution
Launch is the beginning, not the end. Models improve. Requirements change. Regulations evolve. So does the system.
Performance Monitoring
Track accuracy, latency, cost, user satisfaction. Set baselines, detect drift, alert on degradation. You can't improve what you don't measure.
Model Updates
New model drops. Evaluate against your benchmarks. If it's better, swap it in. LLM agnosticism means updates take days, not months.
Continuous Compliance
Regulations change. Run compliance checks continuously, not annually. When new rules drop, know your exposure immediately.
Feedback Integration
User corrections, edge cases, failures—feed them back. PEFT to improve, prompts to adjust, rules to refine. The system learns from itself.
Deliverables
See the Framework in action
Industry-specific Blueprints show how the Framework adapts to healthcare, defense, finance, and more.
View Blueprints Explore the Stack