Build lightning-fast APIs and AI backends with FastAPI. We develop async Python services with automatic documentation, Pydantic validation, and production-grade infrastructure. The framework behind modern AI/ML serving.
Proof-First Delivery
What We Offer
Each module is designed as a production block with integration boundaries, governance hooks, and measurable outcomes.
REST and GraphQL APIs with automatic OpenAPI/Swagger documentation, Pydantic request/response validation, dependency injection, and async endpoint handling. Type-safe Python that generates its own docs.
Serve machine learning models in production — LLM APIs, embedding endpoints, image classification, NLP pipelines. FastAPI handles concurrent inference requests with async I/O while keeping latency low.
Lightweight FastAPI microservices with message queues (RabbitMQ, Kafka, Redis Streams), service-to-service communication, distributed tracing, and container orchestration with Docker and Kubernetes.
FastAPI backends powering LangChain applications — RAG pipelines, agent APIs, chain execution endpoints, streaming responses, and conversation memory management. The API layer for your AI stack.
PostgreSQL with SQLAlchemy/SQLModel, MongoDB with Motor, Redis caching, Celery/Dramatiq task queues, Alembic migrations, and connection pooling. Production database patterns for Python backends.
Pytest test suites with async test support, integration testing, load testing with Locust, Docker containerization, CI/CD with GitHub Actions, and cloud deployment on AWS/GCP/Azure.
We use FastAPI idiomatically — dependency injection, background tasks, middleware, WebSockets, and lifespan events. Not Django developers writing FastAPI like it is Django.
We build the API layer for AI systems — model serving, RAG backends, agent APIs, and ML pipelines. Python for data science, FastAPI for production serving.
Proper async/await patterns, connection pooling, non-blocking I/O, and concurrent request handling. FastAPI performance that actually delivers on its benchmarks.
FastAPI backend paired with Next.js or React frontend. Auto-generated TypeScript clients from OpenAPI specs. End-to-end type safety across the stack.
Delivery Proof
Selected engagements that show architecture depth, execution quality, and measurable business impact.
Delivery Advantages
REST and GraphQL APIs with automatic OpenAPI/Swagger documentation, Pydantic request/response validation, dependency injection, and async endpoint handling. Type-safe Python that generates its own docs.
Serve machine learning models in production — LLM APIs, embedding endpoints, image classification, NLP pipelines. FastAPI handles concurrent inference requests with async I/O while keeping latency low.
Lightweight FastAPI microservices with message queues (RabbitMQ, Kafka, Redis Streams), service-to-service communication, distributed tracing, and container orchestration with Docker and Kubernetes.
FastAPI backends powering LangChain applications — RAG pipelines, agent APIs, chain execution endpoints, streaming responses, and conversation memory management. The API layer for your AI stack.
FAQ
Tell us about your API requirements — we'll design a FastAPI architecture with the right data models, integrations, and deployment strategy for your use case.