The default language of AI
Every model, framework and vector database ships Python bindings first. Building AI in Python means using the tools the way their authors intended, not through a second-class wrapper.
Technologies
Python
AI services, data pipelines and production APIs. The language our AI work ships in, written by senior engineers and reviewed like code that has to run for years.
What we build with Python
Python is where most of our AI and data engineering ships. We build LLM applications and RAG systems, model serving and evaluation infrastructure, the data pipelines that feed them, and the FastAPI or Django services that expose it all to the rest of your stack. If the project touches machine learning, its core is almost certainly Python.
It is the right choice when the work involves AI, data processing or fast-moving product APIs: the ecosystem (PyTorch, LangGraph, pandas, Airflow) is unmatched and senior Python engineers are easy to hire. It is the wrong choice for CPU-bound hot paths with strict latency budgets or for teams standardized on the JVM; in those cases we say so and recommend Java, Node.js or a mixed architecture instead.
We develop AI-accelerated: engineers use AI tooling to move fast, and every line still passes senior review before it merges. Type hints and mypy across the codebase, tests wired into CI from day one, and tracing, metrics and structured logs in place before go-live. Speed comes from the tooling; reliability comes from the discipline.
Why Python
From idea to API in days
FastAPI plus the Python ecosystem turns a well-scoped feature into a documented, tested endpoint fast. Product teams iterate weekly, not quarterly.
One language across the data path
Ingestion, transformation, training, serving and the API in front of it can share one language, one test suite and one deployment story. Fewer handoffs, fewer bugs at the seams.
An honest performance profile
Python is fast enough for the vast majority of API workloads, and we prove it with load tests. Where it is not, we isolate the hot path and move it, instead of pretending the problem is not there.
Technologies
How we work
01
Scope and architecture
Before any code, we agree what the service must do, at what latency and volume. Architecture decisions (framework, data stores, async or not) are written down with the reasons.
02
AI-accelerated build
Engineers use AI tooling for the mechanical parts and their judgment where it matters. Every pull request is reviewed by a senior engineer before merge, no exceptions.
03
Tests and types as guardrails
Type checking and tests run in CI on every commit. Data pipelines get contract tests, model-facing code gets evaluation runs; regressions are caught before deploy, not after.
04
Observability and handover
Structured logs, metrics and tracing ship with the service, not after it. We hand over runbooks and walk your team through the codebase, so you are not dependent on us.
What you get
Production services in FastAPI or Django, typed and senior-reviewed
Data pipelines with orchestration, monitoring and alerting
A test suite and CI/CD pipeline that gate every change
OpenAPI documentation your teams can build against
Dashboards, tracing and structured logging from day one
Runbooks and a recorded handover for your engineers
Common questions
Is Python fast enough for production APIs?
For most workloads, yes, and we verify it with load tests against your actual traffic profile rather than benchmarks from the internet. Async FastAPI handles thousands of requests per second on modest hardware. When a specific path is genuinely CPU-bound, we isolate it and move that piece, not the whole system.
FastAPI or Django?
FastAPI for API-first services and anything touching ML; Django when you need the batteries: admin, auth, ORM and years of proven patterns for content-heavy products. We pick per project and tell you why in writing. Both are boring, well-supported choices, which is exactly what you want.
Can you take over an existing Python codebase?
Yes. We start with a short audit: dependency health, test coverage, typing, deployment. You get a prioritized list of risks before we commit to a roadmap. Most codebases need less rewriting than their owners fear; we fix what blocks delivery and leave the rest.
Does AI-generated code lower the quality bar?
No, because the bar is enforced by process, not by who typed the code. Everything passes senior review, type checks and tests before merge. AI tooling changes how fast a candidate change appears; it changes nothing about what we accept.
Planning a Python build?
Book a free 30-minute call with a senior engineer. You leave with an honest read on whether Python fits and what the first milestone should be.
Talk to an engineer