FastAPI, Django, data pipelines, and ML-powered backend systems.
Fremen Consulting builds Python applications — FastAPI and Django backends, ETL and data pipelines with Airflow, ML model serving, and AI integration for data-heavy and AI-powered products.
Problems we solve for businesses like yours
Synchronous Django views or unoptimized FastAPI endpoints bottleneck under concurrent load without async patterns, connection pooling, or caching.
Data science teams build models in Jupyter that never reach production because there is no serving infrastructure or integration path with the application.
Cron-job ETL scripts without Airflow orchestration fail silently, produce stale data, and cannot be monitored or retried reliably.
Solutions tailored to your industry and growth goals
Production APIs with async FastAPI or Django REST Framework, Pydantic validation, auth, and OpenAPI documentation for frontend and third-party consumers.
Model deployment with FastAPI endpoints, batch inference pipelines, feature stores, and monitoring for prediction drift and latency.
Apache Airflow DAG design for ETL, data quality checks, and scheduled ML retraining with alerting and retry logic.
Technologies and platforms we work with in this space
Measurable outcomes from projects in this space
FastAPI model serving endpoint integrated into SaaS product, reducing prediction latency from batch overnight to real-time under 200ms.
Clear answers to common questions in this industry
We build FastAPI and Django backends, Airflow data pipelines, ML model serving infrastructure, AI and LLM integration, and data processing systems with Python.
FastAPI for high-performance async APIs, ML serving, and microservices. Django for full-stack applications needing admin panels, ORM, and batteries-included features.
Yes. We productionize Jupyter notebook models with FastAPI serving endpoints, input validation, monitoring, and CI/CD pipelines for model versioning and rollback.
Yes. We design Airflow DAGs for ETL, data warehouse loading, ML retraining schedules, and data quality validation with alerting on failure.
API backend MVP takes six to ten weeks. ML serving infrastructure or Airflow pipeline platform typically takes eight to fourteen weeks.
Tell us about your business and goals. We will recommend the right approach for your industry, timeline, and budget.