Your AI Model Needs a Production Backend. We Build It.
AI API development and backend engineering builds the server-side infrastructure that makes AI models accessible, reliable, and scalable in production. We build FastAPI and Node.js backends that handle inference endpoints, streaming responses, rate limiting, authentication, caching, and LLM request orchestration for AI-powered applications.
Most AI projects have a working model but no production-grade backend. The model runs in a notebook or a simple script. It cannot handle concurrent users, has no error handling, no authentication, no cost controls, and no monitoring. We build the backend that turns a working model into a production service.
What We Build
- Inference API endpoints - RESTful and WebSocket endpoints that serve model predictions with proper request validation, error handling, and response formatting.
- Streaming responses - Server-Sent Events (SSE) endpoints for real-time token streaming from LLMs, giving users the familiar ChatGPT-style typing experience.
- LLM orchestration - Backend logic that chains multiple LLM calls, manages context windows, implements retry logic, and handles fallback between model providers.
- Rate limiting and cost controls - Per-user and per-tier rate limiting, token budget enforcement, and usage tracking to keep API costs under control.
- Authentication and authorization - API key management, JWT authentication, OAuth integration, and role-based access control for multi-tenant AI applications.
- Caching and optimization - Response caching for repeated queries, embedding caching for RAG systems, and batch processing for high-throughput workloads.
Our Backend Stack
FastAPI (Python) for ML-heavy backends with async support. Node.js (Express/Fastify) for JavaScript-native teams. PostgreSQL for relational data. Redis for caching and rate limiting. Docker and Kubernetes for containerized deployment. AWS, GCP, or Azure for cloud hosting.
Build Your AI Backend
Book a free architecture review. We will assess your current AI prototype, identify production gaps, and design the backend infrastructure to take it live.