Python is the default choice for teams that need to move fast without giving up structure — web backends, internal automation, data-heavy APIs, and the glue code behind AI features. We build with Django or FastAPI depending on what the project actually needs, not whichever framework is trendy this year.
Python is forgiving to write and easy to under-architect. A dedicated team keeps it maintainable past the first six months.
Django for content-heavy platforms and admin-driven apps, FastAPI for lean APIs and services — decided by requirements, not habit.
REST and GraphQL endpoints with real documentation, versioning, and auth — built for mobile clients, partner integrations, and internal tools alike.
Scripts and pipelines built with logging, error handling, and tests, so they survive past the person who wrote them leaving.
Python's ecosystem makes it the natural home for LLM integrations, embeddings, and data pipelines feeding AI features — wired into a stable backend, not a notebook.
We take over aging Django 1.x/2.x codebases, unpinned dependencies, and undocumented scripts without a rewrite unless one is genuinely cheaper.
The people writing your services are the people who scoped the architecture — no handoff gap between sales and delivery.
Scoped to what your product needs — not a fixed package of features you'll pay for and never use.
Full web platforms with admin panels, auth, and content models where Django's batteries-included approach pays off.
Lean, typed, high-throughput APIs and services for products that don't need a full web framework.
Standalone API layers for mobile apps, partner integrations, or decoupled frontends — designed, documented, and versioned.
Internal tools, ETL jobs, and scheduled tasks that replace manual, error-prone processes.
Upgrading old Django/Flask apps, unpinned dependencies, and Python 2-era code to a supportable, current stack.
Senior Python engineers embedded in your existing team for a defined engagement or ongoing capacity.
It comes down to the team and the workload, not raw benchmarks. Python fits data-heavy, automation-heavy, or AI-adjacent backends where developer velocity matters. Node.js fits real-time, I/O-bound apps sharing code with a JS frontend. Go fits high-concurrency services where raw performance and low memory overhead matter more than development speed. We recommend based on your actual requirements, not a default.
It depends heavily on scope — a focused API or internal automation tool starts lower than a full customer-facing platform with complex integrations. We give a fixed estimate after a short scoping call rather than a generic range.
Yes. Many engagements are exactly this — a standalone API layer serving a mobile app, a separate frontend, or a partner integration, with no web UI attached at all.
Yes, though modernization doesn't always mean rewriting in Python — sometimes it means upgrading the existing PHP or .NET codebase, and sometimes it means a partial migration to Python for new services while the legacy core stays put. We scope the approach around your constraints, not a fixed answer.
Both. Some clients want a full team to own delivery end to end; others want one or two senior backend engineers embedded in their existing team. We scope the engagement around which one fits.
Tell us what you're building or what's slowing down your current Python backend — we'll scope an approach and a fixed estimate.