Python is our default choice when a project mixes web APIs with data, machine learning or heavy integration work. It lets a single team move from the request handler down to the model, the ETL job and the analytics notebook without switching languages, which is why most of our AI/ML engagements and a large share of internal enterprise builds run on it.
What we build in Python
Django 5+ with PostgreSQL is the workhorse for anything content-heavy or admin-driven — portals, back-office consoles, member and dealer systems, regulator-facing dashboards. We use it batteries-included: the ORM, the admin, Django auth with SSO adapters (SAML, OIDC, Azure AD), permissions and audit trails. Django REST Framework handles versioned REST APIs, with drf-spectacular generating OpenAPI so front-end and mobile teams work off a real contract.
For latency-sensitive services, streaming endpoints, webhooks and internal microservices we use FastAPI on Uvicorn/Gunicorn, with Pydantic v2 for schema validation. FastAPI is also our default for wrapping ML models behind an HTTP or gRPC interface — a Django monolith fronting the domain, FastAPI services around it for inference, embeddings, document parsing and third-party integration.
Async work — email, PDFs, statement generation, model retraining, nightly reconciliations — runs on Celery with Redis or RabbitMQ as broker, and Celery Beat for schedules. For heavier pipelines we reach for Apache Airflow or Prefect, and for streaming ingest we pair Python consumers with Kafka or Redis Streams.
Data, AI and ML
A large share of our Python work is data and AI/ML rather than plain web. On the classical side that means scikit-learn, XGBoost and LightGBM for tabular problems — credit scoring, propensity, churn, fraud triage, claims severity — with pandas, Polars and DuckDB doing the feature engineering. Models are versioned and tracked in MLflow, packaged with BentoML or served through FastAPI, and monitored for drift with Evidently.
For deep learning and generative work we use PyTorch and Hugging Face Transformers, and LangChain or LlamaIndex where a retrieval-augmented pattern makes sense. Vector storage sits in pgvector, Qdrant or Weaviate depending on scale, and we lean on open embedding models where data residency rules out sending content to a foreign API. Where clients have picked Anthropic Claude, OpenAI, Azure OpenAI or Bedrock, we integrate against those instead, with prompt evaluation, guardrails and cost telemetry built in from the start rather than bolted on.
Computer vision and document AI show up regularly in BFSI and public-sector work — KYC document classification, signature verification, cheque and form OCR using PaddleOCR, Tesseract or layout-aware models like LayoutLM. See the BFSI practice for how this plugs into onboarding and claims.
Where Python fits in a Redian stack
Python rarely stands alone. In a typical engagement it lives next to a Java or Node core-banking module, a Zoho or Odoo business layer, and a React or Angular front end.
- Behind a channel front end (React, Next.js, Flutter) exposing REST and WebSocket APIs.
- Alongside a core banking or policy admin platform, handling risk scoring, dedupe, sanctions screening and analytics feeds.
- As the integration tier between CRM & ERP implementation platforms and everything else — Zoho, Odoo, Salesforce, Dynamics — via their REST APIs, with Python taking care of transformation, retries and idempotency.
- As the model-serving layer for AI features embedded into existing web apps or Zoho / Odoo modules.
Postgres is the default database — schemas, partitioning, logical replication, pgvector, PostGIS where geography matters. We use SQLAlchemy 2.x for services outside Django, Alembic for migrations, and Redis for cache and rate limiting.
Engineering practice
Every Python service ships with the same baseline: typed code (mypy or pyright), ruff and black for lint and format, pytest with coverage, pre-commit hooks and a CI pipeline (GitHub Actions, GitLab CI or Azure Pipelines) that runs tests, builds the container and pushes a signed image.
Runtime is containerised — Docker images built on slim Python base images, deployed to Kubernetes, ECS or plain VMs depending on client footprint. Configuration is 12-factor, secrets sit in AWS Secrets Manager, Azure Key Vault or HashiCorp Vault, never in the repo. Observability is OpenTelemetry end-to-end: traces to Tempo or Jaeger, metrics to Prometheus, logs shipped to Loki, ELK or CloudWatch, with Sentry for errors.
Security is not an afterthought. We follow OWASP ASVS for web tiers, enforce dependency scanning through pip-audit and Snyk, run SAST with Bandit and Semgrep, and add rate limiting, input validation and audit logging by default. For regulated workloads under RBI, IRDAI, CBK, IRA, NAICOM or FCA supervision, we align logging, retention and access control to whatever the local supervisor expects.
Why Redian for Python
The Python team is not a separate silo — the same engineers who write Django services also work across our BFSI practice, the Advanced Zoho Partner practice and our Odoo and Salesforce delivery. That means a Python API written for a bank in Nairobi or an insurer in Lagos is designed knowing exactly how it will be consumed by a CRM, a channel app or a regulator report, not in isolation.
We staff Python engagements in three shapes: full delivery of a product or platform, embedded squads inside a client's own team through IT staff augmentation, and targeted AI/ML builds that plug into systems we did not originally build. Recent work is written up on /case-studies; to scope a Python or AI/ML build, talk to us.