Python Development for Data and Beyond

From ML-powered features to high-throughput data pipelines, our AI-managed teams deliver production Python services with milestone-based delivery.

Technology: Python (Backend Language)

Python is our go-to for data-intensive backends, ML integrations, and automation services. Its rich ecosystem of libraries makes it ideal for projects that combine web APIs with data processing, machine learning, or scientific computing. Our teams build production Python services with FastAPI, Django, and Flask.

What We Build

  • Data-Driven APIs & Backends: APIs that process, transform, and serve data with built-in analytics, reporting, and data pipeline integration.
  • ML & AI-Powered Features: Integrate machine learning models into your product — recommendation engines, NLP, image processing, and predictive analytics.
  • Automation & Workflow Services: Automated data pipelines, ETL processes, scheduled jobs, and integration middleware using Celery and Airflow.

Expertise

  • Python Projects Delivered: 120+
  • ML Models Deployed to Production: 40+
  • Average Pipeline Throughput: 10K+ records/sec
  • Frameworks Used: FastAPI, Django, Flask

Sample Projects

  • Recommendation Engine API: Collaborative filtering recommendation system serving personalized suggestions for an e-commerce platform with 500K+ products. (300 hours)
    • Collaborative and content-based filtering
    • Real-time model inference via FastAPI
    • A/B testing framework
    • Model retraining pipeline
  • Document Processing Pipeline: Automated pipeline for extracting, classifying, and indexing data from PDFs, images, and scanned documents. (240 hours)
    • OCR with Tesseract and cloud vision APIs
    • NLP-based document classification
    • Structured data extraction
    • Searchable document index with Elasticsearch

Frequently Asked Questions

When do you use Python over Node.js?
We recommend Python when the project involves data processing, machine learning, scientific computing, or integration with Python-ecosystem libraries (pandas, scikit-learn, TensorFlow). For pure web APIs, Node.js with TypeScript is often a better fit.
Do you use Django or FastAPI?
FastAPI for high-performance APIs and microservices; Django for full-featured web applications with admin panels, ORM, and built-in authentication. We choose based on project requirements.
Can you deploy ML models to production?
Yes. We handle the full ML deployment pipeline — model serving with FastAPI or TorchServe, containerization with Docker, monitoring, and automated retraining.