A FastAPI service that holds up under load, a Django REST API your mobile app can rely on, a Flask endpoint that does one thing well — Levelbrook designs and ships production Python backends, typed and tested, billed corp-to-corp as a scoped project or staff augmentation.
A visual history pipeline for heavy-equipment rental: 20,000+ photos across 40 machines, embedded with DINOv2, reduced with UMAP, clustered with HDBSCAN, and auto-labelled zero-shot with CLIP — then served through a React viewer. A working, deployed Python ML system, not a notebook.
# 20k+ rental photos -> a browsable visual
# history, clustered by machine & viewpoint.
import torch, umap
from sklearn.cluster import HDBSCAN
feats = dinov2.encode(photos) # ViT-B/14
coords = umap.UMAP(n_neighbors=15).fit_transform(feats)
labels = HDBSCAN(min_cluster_size=12).fit_predict(coords)
# zero-shot names for each cluster via CLIP
names = clip.zero_shot(centroids(coords, labels),
prompts=EQUIPMENT_VIEWS)Python API development is one of the most common reasons teams reach for a Python contractor — and one of the easiest to get wrong under deadline pressure. Levelbrook builds FastAPI, Django REST Framework, and Flask services the way a production backend should be built: typed with Pydantic or DRF serializers, covered by real tests, documented with OpenAPI, and designed around the clients that will actually call it.
This is grounded in real API experience — the Rails side of the practice has shipped REST and GraphQL APIs powering web, mobile, and partner integrations, and the same discipline carries straight into Python. The deployed equipment-cluster project above also ships its data through a clean Python-generated manifest consumed by a React front end.
A well-defined API is a natural fixed-scope project with a quoted price; an evolving backend runs as hourly staff augmentation. Either way it's corp-to-corp through Levelbrook LLC, ships as real PRs, and is documented so your team owns it afterward.
Async, typed FastAPI with Pydantic validation and auto OpenAPI docs — the modern default for a new Python API.
Django + DRF when you want ORM, admin, and auth batteries included, with clean serializers and viewsets.
OAuth2, JWT, and API-key auth with sensible rate limiting and the security edges a reviewer looks for.
Celery / RQ for background work, pytest suites, and CI so the service stays reliable as it grows.
FastAPI is the modern default for a fresh typed API; Django REST when you want batteries included; Flask for lighter-weight endpoints. We'll recommend based on your team and constraints, not preference.
Yes — OpenAPI/Swagger from FastAPI or DRF, plus written-down decisions. Consumers should be able to self-serve.
Yes — OAuth2, JWT, API keys, and SSO, implemented for the edge cases rather than just the happy path.
Either. A well-scoped API is a good fixed-price project; an evolving backend runs hourly as staff augmentation. Both C2C through Levelbrook LLC.
Yes — a Python service next to a Rails monolith is a common, sensible architecture, and one engineer across both keeps the contract clean.
Tell us what the API needs to do and who'll call it. You'll get an honest read and a scope within one business day.