case studies

Vaillancore

Vaillancore

2023

A scalable AI infrastructure that powers context-aware agents and integrates seamlessly with third-party apps via API.

Challenges

  • •Designing a multi-tenant AI architecture with secure API access
  • •Reducing latency in AI responses while maintaining long-term memory
  • •Supporting dynamic vector search with scoped namespaces per customer
  • •Managing efficient context retrieval from Redis, MongoDB, and Pinecone
  • •Handling low-cost, real-time message caching and token-based auth for external API calls

Solutions

  • •Built a production-ready AI service from scratch in under a week
  • •Deployed scalable backend on AWS EC2 with NGINX, Gunicorn, and Docker
  • •Integrated Auth0 and custom API key management for secure multi-tenant access
  • •Implemented hybrid memory system using Redis (short-term) and Pinecone (long-term)
  • •Created a flexible queue system for async message embedding and upsert
  • •Added observability using Prometheus, Grafana, and Uptime Kuma
  • •Developed single endpoint for user chat input that handles full memory lifecycle
  • •Set up API Gateway and CloudFront for scalable public access

Results

  • •Enabled beta customers to integrate AI agents within minutes using one API
  • •Reduced AI response latency to under 300ms using caching and smart chunking
  • •Scaled to support multiple customer namespaces with isolated knowledge bases
  • •Achieved >90% context relevance accuracy in retrieved AI responses
  • •Launched fully operational SaaS MVP and onboarded initial paying users
Next.jsTypeScriptTailwind CSSFlaskRedisMongoDBPineconeAuth0DockerAWSNGINXOpenAI API