Documented REST APIs
Typed endpoints with OpenAPI docs generated for you, so front-end developers and partners can start straight away.
Overview
Leave your app alone and put the AI behind an API
Hardly anyone wants to rewrite their application just to add AI. A small Python service next to it is usually cleaner. Your app calls an endpoint, and the service deals with models, prompts and queues.
We write these in FastAPI, with typed schemas, authentication, rate limits, background jobs for slow model calls, streamed LLM responses and tests that run in CI. Your own developers can read it, extend it and run it.
Training a model and running it in production are different jobs. Around the API we set up Docker and Kubernetes serving, CI/CD that tests code and models, a model registry, repeatable training pipelines and monitoring for latency, errors and drift. Releasing a new model version stops being a big event.
Tools & platforms we use
- Python
- FastAPI
- Docker
- Kubernetes
- GitHub Actions
- MLflow
- Redis
What's included
A backend your team can own
Async & streaming
Background jobs and streamed responses, so a slow model call doesn't freeze the screen.
Auth, limits & security
Authentication, per-user rate limits and input validation. Secrets stay out of the code.
Scale & cost control
Caching, batching and queues keep response times and API bills predictable as usage grows.
Model serving & CI/CD
Models packaged in Docker and deployed on Kubernetes or something simpler, with automated tests and easy rollbacks.
Monitoring & retraining
We track latency, errors and data drift, alert the right people and keep a repeatable path for retraining.
Questions
Common questions about AI backends
6 questions
Why Python and FastAPI?
Most ML libraries are written for Python, and FastAPI gives fast, typed, well-documented APIs. We get the best tools without bending your main stack.
Can this work with our existing PHP, Node or .NET app?
Yes. The service is a plain HTTP API, so any stack can call it. Your app only has to make a request.
How do you handle slow model responses?
Background jobs, status endpoints or streaming, depending on the case, so users see progress and not a frozen page.
Do we need Kubernetes?
Often not. Plenty of workloads run fine on a simpler managed container service. We'd suggest Kubernetes only when your scale or existing platform calls for it.
Can you deploy a model someone else built?
Yes, and people ask us this a lot. We review the model and its dependencies, then package, serve and monitor it.
Where will it be hosted?
Wherever suits you: your cloud account, a managed platform or your own servers. We document the setup either way.
Quick question?
Ask us about AI Backend, APIs & MLOps
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Tell us what your app needs to call.
Describe your application and the AI feature you want. We'll scope the API and give you a fixed estimate.
We'll redo the first milestone at no cost if it doesn't match the brief.
NDA signed before we see anything. Delivered under your brand.