Tech stack
Infrastructure we wire into every build
These aren't logo slides. They're the integrations, IaC modules, and SDKs we wire up daily for billing, auth, inference, observability, and automation.
Our AI tech stack includes support for Retrieval-Augmented Generation (RAG), semantic search, hybrid vector + BM25 indexing, agent MCP servers, and multi-tenant safety. This foundation lets teams build production-grade AI features without the risk of cross-tenant leakage or unbounded agent behavior.
Where this stack shows up
Example architectures
AI platform baseline
Next.js + Supabase + Neon/pgvector, Clerk SSO/SCIM, Stripe usage billing, and RAG with semantic search. See the
architectures page.Agent/MCP pattern
Agent → MCP server → tools/APIs → RAG with auth scopes, tenant filters, and audit logging. More in the Agent & MCP service.
Security rationale
Choices map to the AI Platform Security Guide and RAG Architecture Guide so tenant isolation and retrieval safety are enforced across the stack.
Billing, metered usage, marketplace payouts, and revenue automation.
Content-first frontend architecture with hybrid SSR + islands for blazing-fast sites.
Zero-trust access, Workers, and global edge security.
Cloud infrastructure, storage, compute, and serverless workloads.
GPU inference, model deployment, training infrastructure, and scalable compute.
OpenAI ChatGPT/Codex APIs for RAG modeling, embeddings pipelines, tool execution, and automated regression testing on model updates.
Claude Code workflows, MCP integrations, and parallel agent execution with guardrails designed for enterprise-grade AI deployments.
Google Gemini multimodal reasoning, long-context windows, and high-performance inference for complex RAG and agentic workflows.
Containerized developer experience, local parity, and secure image pipelines.
High-performance caching, rate limiting, session management, and job queues.
Scalable Postgres with row-level security and branching for multi-tenant data layers.
Error tracking, performance monitoring, and production debugging.
Version control, CI/CD pipelines, code security, and automation workflows.
End-to-end testing infrastructure, auth flows, and production regression coverage.
Product analytics, feature flags, and event capture for AI platforms without wiring new tools.
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