NodeNova - AI Infrastructure Implementation

AI INFRASTRUCTURE SERVICES

End-to-end deployment of production AI systems with security and compliance built in from day one.

Model Serving Infrastructure: Production deployment using vLLM and SGLang for optimized inference. Kubernetes-native architecture with horizontal autoscaling, load balancing, and GPU-optimized scheduling. Support for Llama, Mistral, and custom fine-tuned models with sub-200ms latency targets.
Self-hosted / On-prem Deployment: Air-gapped and private cloud deployments that keep your data within your infrastructure. VPC isolation, network policies, and private endpoints. Offline model updates and secure weight distribution for sensitive environments.
RAG System Implementation: Hybrid retrieval pipelines combining dense and sparse methods with cross-encoder reranking. Vector database integration (Qdrant, Milvus, pgvectorscale) with optimized embedding pipelines. Citation generation and source attribution for auditable responses.
Inference Optimization: GPU utilization optimization with continuous batching, KV-cache management, and speculative decoding. Quantization strategies (AWQ, GPTQ) for cost-efficient deployment. Model routing for budget-aware workload distribution across model tiers.
Enterprise Observability: Full-stack monitoring with Prometheus, Grafana, and custom AI metrics. Token-level cost tracking, latency percentiles, and throughput dashboards. Anomaly detection with automated alerting and incident response integration.

INFRASTRUCTURE ARCHITECTURE

Production AI systems deployed entirely within your infrastructure with complete data sovereignty.

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        }}%%
        flowchart TB
            subgraph YourInfra["Your Infrastructure"]
                subgraph Client["Client Layer"]
                    API[API Gateway]
                    Auth[Authentication]
                end
                
                subgraph Serving["Model Serving Layer"]
                    vLLM[vLLM / SGLang]
                    Router[Model Router]
                    Cache[KV Cache]
                end
                
                subgraph RAG["RAG Pipeline"]
                    Embed[Embedding Service]
                    VectorDB[Vector DB
Qdrant / Milvus / pgvectorscale]
                    Rerank[Cross-Encoder Reranker]
                end
                
                subgraph Infra["Compute Infrastructure"]
                    K8s[Kubernetes]
                    GPU[GPU Cluster]
                end
                
                subgraph Observability["Observability Stack"]
                    Metrics[Prometheus]
                    Dash[Grafana Dashboards]
                    Alerts[Alerting & Incidents]
                end
                
                subgraph Security["Security Layer"]
                    VPC[VPC Isolation]
                    Encrypt[Encryption
AES-256 / TLS 1.3]
                    RBAC[RBAC & Audit Logs]
                end
            end

            API --> Auth
            Auth --> Router
            Router --> vLLM
            Router --> Cache
            vLLM --> GPU
            API --> Embed
            Embed --> VectorDB
            VectorDB --> Rerank
            Rerank --> vLLM
            K8s --> GPU
            vLLM --> Metrics
            Metrics --> Dash
            Dash --> Alerts
            VPC --> Auth
            Encrypt --> vLLM
            RBAC --> API

OUR IMPLEMENTATION APPROACH

Structured pilot-to-production methodology designed for on-prem deployments in regulated environments.

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        flowchart LR
            subgraph Phase1["Discovery"]
                D1[Threat Modelling]
                D2[Data Audit]
                D3[KPI Definition]
            end
            
            subgraph Phase2["Pilot"]
                P1[60-90 Day Scope]
                P2[Eval Harness]
                P3[Stakeholder UAT]
            end
            
            subgraph Phase3["Production"]
                Pr1[SLO Enforcement]
                Pr2[Autoscaling]
                Pr3[Runbooks]
            end
            
            subgraph Phase4["Operational"]
                O1[Team Training]
                O2[Handover]
                O3[Independence]
            end
            
            Phase1 --> Phase2
            Phase2 --> Phase3
            Phase3 --> Phase4

1. Discovery & Planning

Threat modelling, data audit, and workflow mapping. We assess your infrastructure requirements, compliance constraints, and performance targets. Baseline metrics definition and success KPIs aligned to your business objectives.

2. Pilot Implementation

60–90 day structured pilot with constrained scope and clear success criteria. Evaluation harness development, security review, and production planning from day one. Stakeholder UAT and iterative refinement based on real workload testing.

3. Production & Scale

SLO definition and enforcement, autoscaling configuration, and comprehensive runbook development. Model routing implementation, cost optimization, and continuous evaluation pipelines. Handover documentation and team training for operational independence.

Infrastructure Security

Every AI deployment follows ISO 27001-aligned security practices with complete data sovereignty:

Network Isolation

VPC deployment with private subnets and no public endpoints. Zero-trust network policies. Service mesh encryption with mTLS. Air-gapped deployment support for highest-security environments.

Data Protection

Encryption at rest (AES-256) and in transit (TLS 1.3). PII detection and redaction pipelines. Prompt logging with configurable retention policies. HSM integration for key management.

Access Control

OIDC/SAML SSO integration with your identity provider. RBAC with namespace isolation. API key rotation and secrets management via Vault or cloud KMS. Comprehensive audit logging.

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AI Infrastructure Implementation for Regulated Industries