Paydash
By improving existing infrastructure, we helped improve accuracy of analytics happening over millions of daily synced transactions.
Python
From MVPs toAI-Powered Platforms.All in Python.
We build production Python backends. AI features, data pipelines, and APIs that have to hold up under real load. Type-checked code, async where it pays.
RBPUX
4.9 on Clutch
Strategy, design, and engineering
Python powers production at
Python by the numbers
Why Python is the foundation of modern AI and data work.
#1
TIOBE Index
Python has held the top spot on the TIOBE Index throughout 2026, ahead of C, C++, and Java.
23M+
Python developers
SlashData's 2025 global survey puts Python's developer community at roughly 23 million.
75%
AI & ML workloads
Three out of four developers building AI and ML systems use Python as their primary language.
800K+
Packages on PyPI
PyPI hosts over 800,000 libraries. Most of the integrations you need already exist.
Is Python right for your project?
Python wins for AI-heavy products and data-driven backends. It struggles with hard real-time or CPU-bound work without careful design. Below are the cases where it pays off.
AI & MACHINE LEARNING INTEGRATIONS
PyTorch, scikit-learn, LangChain, Hugging Face. The serious ML libraries are written for Python first, often Python only. If your product needs vision, NLP, or scoring models, building in Python skips weeks of glue code.
HIGH-THROUGHPUT BACKENDS
Modern Python (FastAPI, async SQLAlchemy, Uvicorn) handles concurrent I/O cleanly. We've shipped APIs that hold thousands of requests per second on a single application server.
DATA PIPELINES & AUTOMATION
Celery for queues, pandas and Polars for transformation, Airflow or Prefect for orchestration. Python is the default language of the data stack, which makes hiring and integration easier downstream.
What our Python projects ship with
We build enterprise Python systems, not throwaway scripts. Every project leaves with the same engineering standards.
API Architecture
REST and GraphQL services on FastAPI or Django REST Framework. Pydantic validation at the edges, structured logging, and OpenAPI docs you can hand to frontend or partner teams without translation.
6wkAverage MVP time
AI Model Deployment
We wrap PyTorch, scikit-learn, and Hugging Face models behind versioned endpoints with batching, GPU pooling, and graceful fallbacks. Latency and cost stay predictable as traffic scales.
15+AI features shipped
Data Infrastructure
Async workers on Celery and Redis or RabbitMQ. ETL jobs that retry, backfill, and report. Heavy work moves off the request path so the API stays responsive.
10M+Reqs
Security & Compliance
ORM-only data access, JWT or session auth, secrets in vaults (not env files), and request-level audit logs. Work we ship has cleared SOC 2 reviews and HIPAA-aware deployments.
0Security incidents
DevOps & Containerization
Multi-stage Docker builds, Kubernetes or ECS deployment, and GitHub Actions CI with type checks, security scans, and tests gating every merge.
90%+Test coverage
Monolith Refactoring
Old Django and Flask monoliths broken into bounded contexts and services. We do strangler-fig migrations behind feature flags, so the legacy app keeps serving traffic while the new one comes online.
40%Lower eng cost
Python vs the alternatives, for business builders
Where Python is the right call, and where another language fits better.
AI / ML & data science
Python
Dominant (PyTorch, pandas)
Node.js
Limited
Java
Solid (DJL, Spring AI)
Ruby on Rails
Limited
Time-to-market
Python
Fast
Node.js
Fast
Java
Slower, verbose
Ruby on Rails
Fastest
Enterprise adoption
Python
Wide
Node.js
Wide
Java
Strongest
Ruby on Rails
Smaller pool
Hiring & onboarding
Python
Easy, readable
Node.js
Async takes practice
Java
Steep curve
Ruby on Rails
Easy
Python projects we've shipped
Selected work from our Python portfolio.
Paydash
By improving existing infrastructure, we helped improve accuracy of analytics happening over millions of daily synced transactions.
Python
Invopass
Invoices ETL pipeline — extracts, transforms, and loads invoice data. Built as an alternative to Doctrails.
Python
Veriscreen
UK chargeback identification system that flags incorrect chargebacks and automates the dispute workflow.
Python
Penningmeester
Personal finance manager with predictive budgeting — tracks spending and forecasts your actual budget automatically.
Python
Oxyproxy
Faster, affordable and stable proxies.
Python
Stefen Project For PlotlyDash Analytics
Migrated an existing Flask application to Django, implemented authentication, integrated a Django theme-based admin panel, and prepared the initial PRD.
Python
The Python problems we solve every week
We're usually called in when a Python codebase has stopped scaling cleanly.
The Problem
Slow Execution Speed
Synchronous request handlers blocking on I/O, and CPU-bound loops running inside web workers where they don't belong.
Dependency Drift
Pinned-but-not-locked requirements files. CI passes, then production breaks a week later because a transitive dependency shipped a change.
Tangled Django and Flask Code
Fat models and fat views that own database access, business rules, and HTTP concerns at once. Every change risks four other things.
Our Solution
Async I/O Where It Pays
asyncio, FastAPI, and Uvicorn for I/O-bound paths. CPU-heavy work moves to Celery workers backed by Redis. The hot path stops blocking.
Lockfiles and Reproducible Builds
uv or Poetry for resolution, pinned lockfiles in version control, and the same image promoted from CI to production.
Service Layer Separation
Business logic moves into Python services with clear inputs and outputs. Models become persistence concerns. Tests stop needing a database.
The tech stack we use
Tools we work with daily on Python projects.
Django
FastAPI
Flask
Redis
Docker
PostgreSQL
Pydantic
Nginx
Why reverseBits for Python?
We don't ship Python scripts. We ship type-checked services that run in production for years.
10+
Python projects shipped
10
yrs custom software experience
4.9
Clutch rating
01
Senior Backend Engineers
Every project has at least one senior Python engineer with production experience under load. No junior-led builds.
02
Real ML Deployment Skill
We've moved models from notebook to production with batching, monitoring, and rollback. Not every Python team has done that part.
03
Type Checking by Default
Pydantic at the API edges, mypy or Pyright in CI. Refactors stop being scary; reviews stop catching avoidable bugs.
04
Security as a Baseline
Parameterized queries, CSRF on Django sessions, rate limits, and dependency scanning in CI. The basics, done every time.
Stop fighting scale and data bottlenecks. Build your next backend in Python with engineers who've done it before.
1Stack across AI, web, and data
AsyncDefault for I / O paths
4.9Clutch rating
Python questions, answered.
Specific technical questions before committing to Python? Quick answers below.
Ready to build your Python product?
Django if you need an admin panel, built-in auth, or a content-shaped product. FastAPI if you're building an API for a separate frontend or mobile app, or anything that benefits from async I/O. We pick per project; sometimes both run in the same system.
The GIL only matters for CPU-bound concurrency. Most production bottlenecks are I/O. We solve those with async (FastAPI, asyncio), background workers (Celery), and good caching. For real CPU work, we drop into NumPy, Cython, or Rust extensions.
Usually we port the logic to Python (NumPy, pandas, scikit-learn) so it runs in the same process as the API. If the R model is too costly to rewrite, we wrap it behind a small service and call it from Python with stable contracts.
Both, depending on the client. REST with FastAPI or DRF for most internal and partner APIs. GraphQL with Strawberry or Ariadne when frontend teams need flexible queries against a complex domain.
Containers built in CI, deployed to Kubernetes (EKS, GKE) or ECS. Gunicorn or Uvicorn workers behind a load balancer, horizontal autoscaling on CPU and queue depth, Redis for caching and session state, Postgres with read replicas where reads dominate.
Python questions, answered.
Specific technical questions before committing to Python? Quick answers below.