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.
Delivery Proof
Selected engagements that show architecture depth, execution quality, and measurable business impact.
Delivery Advantages
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.
Use Cases
Each use case links to a dedicated implementation page so teams can review architecture patterns in detail.
Unified API layer for multiple LLM providers — routing, rate limiting, caching, cost tracking, and fallback logic for Claude, GPT-4, and open-source models.
Document ingestion, embedding generation, vector search, and retrieval-augmented generation endpoints powering enterprise knowledge systems.
ETL APIs, webhook processors, file transformation endpoints, and batch processing services with Celery workers and progress tracking.
WebSocket APIs for sensor data ingestion, real-time dashboards, device management, and time-series data processing with async handlers.
Backend services for internal tools — user management, workflow engines, approval systems, and integration hubs connecting internal systems.
Multi-tenant API backends with tenant isolation, subscription management, usage metering, and webhook delivery for SaaS platforms.
Execution Framework
Define endpoints, data models, authentication strategy, and integration requirements with OpenAPI-first design
Develop FastAPI application, database models, external integrations, and background task processing
Comprehensive testing, load testing, query optimization, caching implementation, and security hardening
Containerized deployment, CI/CD pipelines, logging, APM integration, and production monitoring
FAQ
Explore related services
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.