solution
Python Development .
iSkylar builds Python-powered web applications, REST and GraphQL APIs, automation systems, data pipelines, and AI/ML backends, using Django, FastAPI, and the Python ecosystem to deliver production-grade software that performs under real-world load. From your first MVP to an enterprise platform processing millions of records, our Python team writes code your engineers will confidently inherit.
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What We Build
Django Web Applications
Full-featured web applications built on Django — admin interfaces, multi-tenant SaaS backends, content platforms, and operational tools that leverage Django's ORM, authentication, and admin framework without fighting against them.
FastAPI & REST/GraphQL APIs
High-performance API backends built with FastAPI or Django REST Framework — async where performance demands it, fully typed with Pydantic, documented with OpenAPI, and tested with pytest before any deployment.
Data Pipelines & ETL
Python-built data engineering pipelines using Pandas, Polars, Celery, and Airflow — extracting from APIs and databases, transforming to your schema, loading into data warehouses, and running reliably on schedule.
Process Automation & Scripting
We automate the manual, repetitive work your operations team does daily — report generation, file processing, web scraping, API polling, email workflows, and system integrations — eliminating overhead at scale.
AI/ML Backend Integration
Python backend services that serve trained ML models via API — FastAPI model serving, async prediction pipelines, batch inference jobs, and the monitoring infrastructure that catches model drift before users notice.
Python Performance Optimisation
We profile slow Django views, fix ORM N+1 queries, implement Redis caching, convert CPU-bound tasks to async or worker queues, and tune database indexes — measurably improving response times on existing Python applications.
AI-POWEREDLearn how we work
Delivered faster with AI tooling
We don't just write code; we orchestrate intelligence. By integrating Copilot-assisted generation and AI-driven automated testing, we reduce time-to-market by up to 40%.
Copilot-Assisted Generation
LLM-backed development to eliminate boilerplate delays.
AI Predictive Testing
Anticipating edge cases before they enter production.
ENGINEERED FOR PERFORMANCE
The iSkylar Tech Stack
Python 3.12
Django 5
FastAPI
PostgreSQL
Redis
Celery
Pandas / Polars
SQLAlchemy
Pydantic
pytest
Docker
Kubernetes
AWS Lambda
Airflow
GitHub Actions
Our Development Journey
A streamlined, transparent path from vision to launch.
01
Discovery & Architecture
1–2 weeks
We define your data models, API contracts, async requirements, and queue architecture — and produce a technical spec with stack decisions documented before development begins.
02
UI/UX Design
1–3 weeks
Wireframes and high-fidelity Figma designs for user-facing screens. For API-only backends, we produce an OpenAPI specification reviewed and agreed with your frontend or mobile team before implementation.
03
Development
4–14 weeks
Two-week sprints with a working staging environment from sprint one. pytest coverage built alongside features, async patterns applied where throughput demands it, and a weekly demo each sprint.
04
QA & Performance Testing
1–2 weeks
End-to-end API tests, database query profiling, load testing with Locust, security review covering OWASP Python-specific concerns (SQL injection, SSRF, dependency vulnerabilities), and coverage gate enforcement.
05
Deployment & Go-Live
3–5 days
Docker containers deployed to AWS ECS, Kubernetes, or your existing infrastructure, Gunicorn/Uvicorn workers configured, Celery queues running, monitoring live, and your team trained on the deployment process.
06
Post-Launch Support
Ongoing
Dependency security updates, performance profiling, Django/FastAPI version upgrades, and feature iteration. GitHub Actions CI runs your test suite on every pull request automatically.
Our Proven Track Record
IMPACTMETRICS
PROJECTS DELIVERED
90+
Python web applications and APIs delivered across SaaS, fintech, and enterprise categories
PERFORMANCE GAIN
71%
Reduction in API response time after Django ORM optimisation, query profiling, and Redis caching implementation for a SaaS platform
TEST COVERAGE
>88%
Test coverage achieved on Python codebases we build from scratch using pytest
PIPELINE SPEED
4x
Data pipeline processing time improvement after migrating from Pandas to Polars for a data-heavy analytics backend
Flexible Engagement
Partnership structures tailored to your scale and velocity.
MVP API
Core Python backend — models, endpoints, authentication, and deployment. Fixed scope, clear timeline, production-ready API your frontend or mobile app can integrate against.
- Defined Scope
- Fixed Budget
POPULAR
Full Product Build
Complete delivery — architecture, design (if applicable), Django or FastAPI backend, database, integrations, full pytest suite, CI/CD, and 3 months of post-launch support. Our most popular engagement.
- Full Integration
- Infinite Scalability
Dedicated Python Team
One or more senior Python engineers on a monthly retainer — Django or FastAPI specialists, data engineers, or a mixed squad. You own the roadmap; we own code quality and test coverage.
- Defined Scope
- Fixed Budget
"
"iSkylar transformed our legacy banking app into a modern, lightning-fast experience. Their AI-driven approach saved us months in engineering time."
JD
James Donovan
CTO, GlobalFin
"
"The level of technical precision and UI craftsmanship is unmatched. They don't just build apps; they build business advantages."
SL
Sarah Lin
Product Head, LuxeRetail
Frequently Asked Questions
When should I choose Django versus FastAPI?+
Django is the right choice when you need a full-featured web framework — built-in admin, ORM, authentication, forms, and a mature ecosystem. FastAPI is better when performance is the primary concern — async throughput, API-first design, and automatic OpenAPI documentation from type hints. We advise on the right choice after understanding your product type and traffic expectations.
Do you write tests?+
Yes — pytest from day one, not retroactively. We target >88% coverage on new builds, test every API endpoint with parametrised test cases, and use factory_boy for fixture management. Tests are part of the Definition of Done on every sprint, not an afterthought.
How long does Python development take?+
An MVP API is typically 6–12 weeks. A full-featured product with complex business logic and integrations runs 14–22 weeks. Fixed milestone timeline after discovery.
What does Python development cost?+
MVP APIs start from $10,000. Full product builds from $25,000. Pricing depends on complexity, async requirements, and integration scope. Fixed-scope quote before signing.
Can you optimise our existing slow Django application?+
Yes. We profile using Django Debug Toolbar and py-spy, identify N+1 queries, add Redis caching to expensive view logic, convert synchronous bottlenecks to Celery tasks, and tune database indexes. We benchmark before and after so improvements are measurable, not theoretical.
Can you build data pipelines and automation alongside the web application?+
Yes. Python's strength is precisely this combination — web API, data pipeline, and automation in one codebase and one team. We regularly build Airflow-orchestrated ETL pipelines alongside Django or FastAPI backends for the same client.
Who owns the source code?+
You do. All Python source, test suites, migration files, Celery task definitions, and deployment configuration are fully transferred at project close. No retained rights.
Do you handle deployment and hosting?+
Yes. We typically deploy Python applications on AWS (ECS or Kubernetes), DigitalOcean App Platform, or your existing infrastructure — using Docker, Gunicorn/Uvicorn, and GitHub Actions for CI/CD. We configure the environment and train your team before handover.
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