Powering a Production-Ready AI Platform on AWS for an Emerging AI Company

When Ndara AI engaged Vontech Group, they were operating a fragmented early-stage infrastructure across AWS LightSail instances, adequate for development but not built for the demands of a production AI platform. Key challenges included:

• Unscalable Compute Foundation: Frontend, backend, and database services ran on LightSail instances with no auto-scaling capability.
• GPU Readiness Gap: AI voice agent workloads required dedicated GPU compute, but no structured pathway existed for deploying and managing these safely in production.
• No AI Inference Architecture: FastAPI AI services, LLM orchestration, RAG pipelines, and embedding generation had no dedicated infrastructure layer, creating resource contention with backend services.
• Security and Network Gaps: No VPC segmentation, no caching layer, no secrets management strategy, and no formal approach to protecting data in transit or at rest.
• Zero Operational Visibility: Without centralized logging, monitoring, or alerting, the team had no real-time insight into system health, latency, or error conditions.
• No Disaster Recovery Plan: With a production launch imminent, the absence of a DR strategy posed significant business continuity risk.

Vontech Group designed and deployed a secure, multi-tier AWS architecture purpose-built for AI workloads at scale:

• Amazon CloudFront and AWS WAF: Established an edge layer that reduces latency through CDN caching and protects against SQL injection, XSS, and bot traffic.
• Application Load Balancer (ALB) with ACM: Serves as the single secure entry point into the VPC, terminating TLS and routing traffic to backend and AI services across multiple Availability Zones.
• Isolated VPC with Three-Tier Subnet Design: Strict public/private/data subnet segmentation. The ALB and bastion host sit in the public subnet, compute runs in private subnets, and all databases are fully isolated in data subnets with no public route.
• EC2 Auto Scaling Groups: Hosts Node.js (PM2) and Django (Gunicorn) backend services with elastic scaling policies that respond to real-time demand.
• Dedicated AI Inference Layer: FastAPI AI services handling LLM inference, RAG pipelines, embedding generation, and LangGraph agent orchestration run on GPU-enabled and compute-optimized EC2 instances (g5, c7i), independently scalable from the backend.
• Amazon ECS and ECR: Containerized AI microservices covering orchestration, industry classification, sentiment and intent analysis, and AI response generation.
• Amazon RDS PostgreSQL with pgvector: Multi-AZ relational storage for application data and vector embeddings, enabling native similarity search within the same managed database cluster.
• Amazon ElastiCache (Redis): Multi-AZ caching for session storage, AI response caching, and pub/sub communication between services.
• Amazon S3: Object storage for knowledge base documents, media, logs, and backups, with versioning, lifecycle policies, and cross-region replication.
• Comprehensive Security Stack: GuardDuty, Security Hub, AWS Config, Inspector, Shield, CloudTrail, VPC Flow Logs, and Secrets Manager layered across the entire environment.
• CI/CD Pipeline: GitHub Actions integrated with ECR and ECS/EC2 for automated, consistent deployments.
• Amazon CloudWatch: Centralized metrics, logs, and alarms across all infrastructure layers.