Python Development Services — Design Mockup | Dreams Technologies

Python Development Services for Data‑Driven, AI‑Ready Applications

From APIs that serve machine learning models in production to full-stack platforms with complex data models, Dreams Technologies builds Python systems that hold up past the prototype stage. Headquartered in London with a world-class engineering team in India, delivering for clients across the UK, US, Europe, and Asia for over a decade.

Trusted by clients across
UK & EuropeUnited StatesJapan & AsiaMiddle East
500+ Clients
model_api.py
Running
1# Dreams Technologies · Python API Layer 2from fastapi import FastAPI 3from pydantic import BaseModel 4 5app = FastAPI() 6 7class PredictionRequest(BaseModel): 8 features: list[float] 9 10@app.post("/predict") 11async def predict(req: PredictionRequest): 12 result = await model.infer(req.features) 13 return {"prediction": result}
15–20K
req/s typical
Auto
generated API docs
Zero
downtime deploys
Inference Request Flow
ClientAPI GatewayFastAPIResponse
PythonDjangoFastAPI FlaskDockerPostgreSQL
What We Build

Python Solutions We Deliver

AI & Machine Learning Backends

Python is still the default language teams reach for when machine learning meets production. We build the API layer around your models — wrapping PyTorch, TensorFlow, or scikit-learn with FastAPI for clean, concurrent inference and built-in request validation.

Data Engineering & Analytics Pipelines

Using Pandas, NumPy, and Polars, we build the pipelines that turn raw, messy data into something your team can act on — ETL jobs, scheduled reporting, and transformations that feed dashboards reliably.

Full-Stack Web Platforms

When your product needs built-in authentication, an admin interface, and a relational data model, Django remains the strongest choice — especially for content-heavy platforms and applications in regulated industries.

High-Throughput APIs & Microservices

For API-first products, FastAPI's async-first design lets a single service handle many concurrent requests while waiting on databases or external calls — a strong fit for mobile backends and integrations that need to scale efficiently.

Workflow Automation & Internal Tooling

Python's standard library and ecosystem make it well-suited to internal tools — scripts and services that automate repetitive, manual processes across finance, ops, and reporting.

Legacy System Modernization with Python

Older Python 2 codebases and aging Django installs don't always need a rewrite. We assess, upgrade dependency by dependency, and modernize the architecture incrementally — keeping the system running throughout.

Why Us

What Makes Us a Dependable Python Development Partner

Over a decade of shipping Python systems and ML-backed platforms across 500+ clients globally. Here's what that experience means for your project.

03

AWS & Microsoft Azure Certified Partners

Your Python infrastructure is built by a team vetted by the platforms you already trust — direct partner support, early feature access, and deep cloud expertise baked into every deployment.

Certified Partners
04

Framework Choice Based on Your Project, Not Habit

Django, FastAPI, and Flask solve different problems. We pick the one that fits your data model, team, and performance needs — and tell you which one that is before we start building.

Pragmatic Architecture
05

Compliance Ready by Default

GDPR, HIPAA, and SOC 2 controls — encryption, role-based access control, and audit logging — are designed in from the first architecture decision, not patched in later.

Security & Compliance
06

A Proven Python Stack, Not a One-Off

Python and Django sit alongside Node.js and React in the stack we list across our own published case studies — this isn't a technology we're learning on your project, it's one we ship with regularly.

Battle-Tested
Our Process

From First Call to Production Deployment

01
1–2 Weeks

Discovery & Architecture Planning

We map your data model, integration points, and compliance requirements, and decide upfront whether Django, FastAPI, or Flask fits the system you're building.

02
1–3 Weeks

Prototype & Technical Validation

We build a focused working slice — a real endpoint, a real data pipeline — to validate the architecture and surface integration risks before full development starts.

03
Sprint-Based

Development & Integration

The full system is built across data layer, services, and integrations, managed in sprints with weekly progress reports and continuous testing.

04
90-Day Support

Launch, Monitoring & Optimization

We deploy with a controlled rollout, configure monitoring and alerting, and actively tune performance based on real usage data for the first 90 days.

Tech Stack

Technologies We Work With

Every tool chosen for performance, maintainability, and fit — not familiarity.

Core & Runtime
Python 3.14
Latest release
Python 3.12
Stable baseline
Free-Threaded Builds
No-GIL mode
Web Frameworks
Django
Full-stack framework
FastAPI
Async-first API
Flask
Lightweight micro
Data & Machine Learning
Pandas
Data wrangling
NumPy
Numerical compute
scikit-learn
Classical ML
PyTorch
Deep learning
Polars
Fast dataframes
Databases & Caching
PostgreSQL
Relational DB
MySQL
Relational DB
MongoDB
Document store
Redis
In-memory cache
Cloud & DevOps
AWS
Certified partner
Azure
App Service
Docker
Containerisation
Kubernetes
Orchestration
GitHub Actions
CI/CD pipelines
Testing & Tooling
Pytest
Testing framework
mypy
Static typing
Ruff
Linting & formatting
uv
Package manager

Ready to Build a Python Backend That Scales With Your Data?

If you have a system in mind — or a model that needs a production-grade API around it — start with a conversation. We'll tell you what's feasible, what it will take, and which framework actually fits.

Book a Discovery Call
Latest Insights

From Our Blog & Knowledge Base

Suggested topic
Backend June 2026

Django vs. FastAPI vs. Flask in 2026: Choosing the Right Python Framework for Your Project

Read More →
Suggested topic
Releases June 2026

Python 3.14 and Free-Threading: What It Actually Means for Production Workloads

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Suggested topic
AI / ML May 2026

Why FastAPI Has Become the Default Serving Layer for AI and ML Models

Read More →
FAQ

Frequently Asked Questions

What kinds of Python projects do you take on?
AI/ML backends, data engineering pipelines, full-stack Django platforms, high-throughput FastAPI services, workflow automation, and legacy Python/Django modernization. Our experience spans retail, healthcare, hospitality, and service-based businesses.
Which Python framework will you use for our project?
It depends on what you're building. Django for full-stack platforms that need an admin panel and a relational data model out of the box. FastAPI for API-first products or anything serving ML models at scale. Flask when you need a lightweight, minimal-footprint service.
Is Python fast enough for our use case?
For I/O-bound work — APIs, web apps, data pipelines — yes, especially with FastAPI's async support. For genuinely CPU-bound, latency-critical workloads, we'll tell you honestly if a hybrid approach makes more sense.
Can Python power our AI/ML features directly?
Yes — Python remains the default language for machine learning. We build the FastAPI or Django layer around models built with PyTorch, TensorFlow, or scikit-learn, handling validation, concurrency, and monitoring around the model itself.
Can you work with our existing Python or Django codebase?
Yes. We regularly take over existing Python codebases — auditing dependencies, identifying outdated packages, and modernizing incrementally rather than recommending an unnecessary rewrite.
What happens after the application is launched?
We include 90 days of post-launch support covering monitoring, bug fixes, and performance tuning. After that, we offer optional retainers for feature development and Python version upgrades.
10+
Years of Proven Success
500+
Happy Clients Worldwide
15+
Products We Have Built
120+
Technical Team Members