Sovereign AI Infrastructure
for the Enterprise
We design, deploy, and govern private AI systems — enabling organizations to capture the operational value of large language models without ceding control of their data, costs, or compliance posture.
Core Engineering Capabilities
End-to-end sovereign AI — from infrastructure through governance.
Private LLM Infrastructure Deployment
Design, deploy, and optimize self-hosted large language model infrastructure using open-weight models. Includes GPU cluster sizing, inference optimization via vLLM and TensorRT-LLM, and Kubernetes orchestration for production-grade availability.
Agentic AI Systems Engineering
Design and deployment of autonomous AI agents that execute multi-step workflows — from document processing and compliance review to supply chain optimization. Agents operate within governed boundaries with human-in-the-loop oversight at configurable checkpoints.
RAG & Knowledge Architecture
Retrieval-Augmented Generation systems connecting language models to proprietary data. Vector databases, embedding pipelines, and relevance scoring — engineered for accuracy, not just fluency. Multi-source context fusion for complex enterprise queries.
AI Strategy & Roadmap Advisory
Board-level AI readiness assessment and multi-year technology roadmap development. Capability gap analysis, build-vs-buy evaluation, vendor landscape assessment, and organizational AI maturity modeling aligned to business outcomes.
Legacy System AI Integration
Integration of AI capabilities into existing enterprise systems — ERP, MES, CRM, LIMS — without requiring platform replacement. API-first integration approach with middleware orchestration for systems lacking modern interfaces.
AI Governance & Compliance
Model versioning, audit trail architecture, input/output logging, and explainability frameworks. Built for ISO 27001, HIPAA, SOC 2, and sector-specific regulatory requirements. Enterprise-grade RBAC and role-governed model access.
Sovereign Infrastructure Stack
Six-layer architecture designed for complete organizational control. Air-gapped deployment available for classified environments.
Deployment Models
All models include GPU infrastructure sizing, network architecture design, and security hardening as part of the initial architecture phase.
On-Premise
Regulated industries with strict data residency. Edge & operational integration.
Private Cloud
Multi-site enterprises requiring centralized AI with regional isolation.
Hybrid
Mixed workloads — sensitive data on-premise, elastic scaling in private cloud.
Air-Gapped
Defense, critical infrastructure, and environments requiring complete network isolation.
Enterprise AI Governance Framework
Nine pillars of governance embedded at the infrastructure level — not bolted on after deployment.
Model Lifecycle
Versioned registry with controlled promotion pipelines and rollback capability.
Update Control
Explicit approval workflows with staging validation before production deployment.
Human-in-the-Loop
Configurable checkpoints for high-stakes decisions with full override capability.
Explainability
Interpretable reasoning for regulated use cases with attribution tracing through RAG.
Bias Monitoring
Continuous output analysis for demographic and operational bias with drift detection.
Audit Trail
Complete I/O logging with timestamps, user identity, model version, and session context.
Access Control
Granular RBAC across models, data sources, and capabilities integrated with enterprise IAM.
Disaster Recovery
Multi-zone failover with RPO/RTO aligned to enterprise SLA requirements.
Encryption
AES-256 at rest, TLS 1.3 in transit, HSM integration, zero-trust compatible.
Industry Deployment
Production-validated AI systems across regulated and high-throughput industries.
Healthcare & Pharma
IT & B2B Procurement
Real Estate
Hospitality
Total Cost of Ownership
Sovereign infrastructure eliminates per-token pricing volatility. Baseline comparison for ~500K daily inference requests.
Engagement Model
Structured delivery — from readiness assessment through production governance. Typical deployment: 16–20 weeks to production-ready.
AI Readiness Assessment
Weeks 1–4Organizational AI maturity evaluation, infrastructure audit, data readiness, governance posture, stakeholder interviews, scored readiness report.
Architecture Design
Weeks 4–8Infrastructure architecture, model selection, GPU sizing, network design, governance framework specification.
Infrastructure Deployment
Weeks 8–16Hardware provisioning, software stack, model optimization, integration, performance benchmarking, security hardening.
Governance & Control Setup
Weeks 14–18Access controls, audit logging, model versioning, bias monitoring, compliance reporting, governance team training.
Optimization & Scaling
OngoingPerformance optimization, model fine-tuning, multi-model scaling, quarterly governance reviews, continuous improvement.
Technology Ecosystem
Your Data. Your Infrastructure. Your AI.
Schedule a technical assessment to evaluate sovereign AI deployment for your organization's specific requirements.