Infrastructure SystemAI Infrastructure Platform

DANY Co-Pilot

Hybrid local + cloud LLM orchestration platform with RAG pipelines, tenant isolation, and dynamic model routing built for production-grade AI deployment.

DANY Co-Pilot hero screenshot

Overview

DANY is a production-grade AI orchestration platform designed for hybrid local and cloud LLM deployment. It provides enterprise-ready infrastructure for managing multiple AI models, implementing RAG pipelines with vector search, and maintaining tenant isolation across organizations.

Problem Context

Organizations deploying AI face a fragmented landscape: cloud-only solutions are expensive and raise data sovereignty concerns, while local-only deployments lack the flexibility and power of frontier models. Most platforms force a choice between the two, and none provide the multi-tenant architecture needed for SaaS-scale deployment.

System Design Decisions

  • Hybrid model routing architecture balances cost, latency, and capability across local (Ollama) and cloud (OpenAI) providers.
  • pgvector for RAG keeps the vector store co-located with relational data in Postgres, eliminating a separate vector database.
  • Multi-tenant isolation at the database level using Prisma with row-level security patterns.
  • Confidence scoring pipeline routes queries to the most appropriate model tier automatically.
  • Role-based access control built into the middleware layer, not bolted on after the fact.

Architecture

Three-layer architecture: an API gateway handling auth and routing, an orchestration layer managing model selection and RAG retrieval, and a data layer combining Postgres (relational + vector via pgvector) with Supabase for real-time capabilities. Model routing decisions are made per-request based on query complexity analysis, tenant configuration, and cost constraints.

Multi-tenantRAGVector searchHybrid LLM orchestrationRole-based authProduction-ready infrastructure

Stack Breakdown

Next.js App Router provides the application shell with server-side rendering for the admin dashboard. TypeScript enforces type safety across the full stack. Postgres with pgvector handles both relational data and vector embeddings. Prisma manages database access with type-safe queries. Supabase provides auth and real-time subscriptions. OpenAI and Ollama serve as the cloud and local model providers. Stripe handles subscription billing and usage metering. Deployed on Vercel with Docker containers for local model inference.

Next.js (App Router)TypeScriptPostgrespgvectorPrismaSupabaseOpenAIOllamaStripeVercelDocker

Screenshots

Strategic Impact

DANY demonstrates STRIX's ability to architect complex AI infrastructure from the ground up. The hybrid approach solves a real deployment constraint most AI platforms ignore. Multi-tenant architecture makes this a viable SaaS foundation, not just a single-use tool.

Future Roadmap

  • 1Fine-tuned model deployment pipeline
  • 2Advanced analytics dashboard for model performance tracking
  • 3Multi-region deployment for data sovereignty compliance
  • 4Plugin architecture for custom model integrations

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