Python development
From high-concurrency FastAPI services to data extraction and automation tooling, we build robust Python backends—with tests, strict typing, and observability.
How we approach Python development.
Python is where most of the work behind an AI or automation product actually lives: the API the front-end calls, the pipeline that moves and reshapes data, the workers that run long or scheduled jobs, and the integration layer that talks to everything else. We build all four, with the operational concerns treated as part of the deliverable rather than something to add later.
For APIs we default to FastAPI with Pydantic models at every boundary. Requests are validated before they reach business logic, responses are typed, and the OpenAPI schema is generated from the code rather than maintained separately, so the contract your front-end or your client integrates against is always the contract the service actually enforces.
Work that does not belong in a request cycle goes to a queue. Document parsing, model calls, third-party syncs, report generation, and scheduled jobs run as background tasks with explicit retry policies, idempotency keys, and dead-letter handling, so a transient failure in someone else's API does not corrupt your data or silently drop a job.
Every service ships with the things that make it operable: structured logging with request correlation, health and readiness endpoints, database migrations under version control, a test suite covering the paths that carry money or data, and a container image that runs the same way on a laptop and in production.
What We Deliver
- FastAPI and Django backend APIs
- ETL and data ingestion pipelines
- AI and machine learning service integration
- Unit tests, typing, and Dockerization
How we build and deploy.
Structured engagement from initial process audit to live production monitoring.
Contract and data model first
We define the API contract and database schema before implementation, so integration work on both sides can start immediately and in parallel.
Core service implementation
Typed endpoints, business logic isolated from transport concerns, and migrations checked in alongside the code that needs them.
Asynchronous and scheduled work
Long-running and periodic jobs move to a queue with retries, idempotency, and dead-letter handling so failures are recoverable rather than silent.
Hardening and handover
Load testing against realistic traffic, structured logging and metrics, containerised deployment, and documentation an on-call engineer can follow.
Operational challenges we eliminate.
A script that worked once is now business-critical infrastructure
We turn it into a real service: tests, typed interfaces, error handling, logging, scheduled execution, and a deployment path that does not depend on one person's machine.
Jobs fail silently and nobody notices until a customer does
We add structured logging, failure alerting, and a retry policy with dead-letter capture, so failed work is visible, diagnosable, and replayable.
The service falls over under concurrent load
We profile the actual bottleneck, which is usually connection handling or blocking calls in an async path, then fix it with pooling, caching, and moving heavy work to background workers.
Under the hood.
Deep architectural rigor built for software engineers and technical decision-makers.
Validation at every boundary
Pydantic models validate inbound requests, outbound responses, and third-party payloads, so malformed data is rejected at the edge instead of corrupting state downstream.
Async where it pays
IO-bound work uses async endpoints and connection pooling to hold high concurrency on modest hardware; CPU-bound work is moved off the event loop rather than blocking it.
Idempotent background jobs
Queue tasks are safe to retry, with idempotency keys and exponential backoff, so a network blip does not produce a duplicate charge or a double-sent email.
Migrations and reproducible environments
Alembic migrations under version control and pinned, containerised dependencies mean the environment that passed tests is the environment that runs.
Where this applies.
Backend APIs for web and mobile clients
The service layer behind a product, with authentication, permissions, and a documented contract your front-end team can build against.
Data ingestion and ETL pipelines
Scheduled extraction from APIs, databases, and files, with transformation, validation, and loading into a warehouse or operational store.
AI and model serving backends
The service that sits between your application and a model provider: prompt construction, schema validation, caching, cost control, and fallback handling.
Automation and integration services
Middleware that connects systems which were never designed to talk to each other, with the retry and reconciliation logic that keeps them consistent.
Verified delivery standards.
Underwriting Acceleration
Finject MCA brokerage CRM with AI statement parsing
Brand-Compliant Social Reach
PostAutoPilot distributed social automation platform
Client Value Delivered
Over 200+ projects shipped across SaaS, AI, and workflow automation
Intellectual Property Guarantee
Clients own 100% of all custom code, prompt pipelines, and databases upon launch
Common questions about Python development.
Q.FastAPI or Django?
FastAPI for APIs, AI backends, and integration services, where typed contracts and async concurrency matter most. Django where you need its admin, ORM, and batteries-included ecosystem, typically for content-heavy or internally administered applications. We will tell you which one your project actually needs.
Q.Can you work on an existing Python codebase?
Yes. A large share of our Python work is stabilising code someone else wrote: adding tests around current behaviour, introducing typing incrementally, untangling blocking calls, and getting it deployable before changing features.
Q.How do you handle background and scheduled jobs?
Celery or RQ with Redis for most workloads, or a managed queue where the infrastructure already exists. Tasks are written to be idempotent, with retries, backoff, and dead-letter handling so failures are recoverable.
Q.Do you deploy it, or do we?
Either. We deliver a containerised service with CI configuration and deployment documentation. We can run the deployment to your cloud account, or hand it to your platform team to run themselves.
Real software we have shipped.
HomeSchool Matchmaker: Multi-Role Community & Field-Trip Marketplace
A role-aware homeschooling ecosystem built on Bubble.io uniting family discovery, vetted educator matching, field-trip venue bookings with Stripe Connect escrow, and recurring memberships.
SuperCoaches: 2-Sided Marketplace for Athletic Coaching & Academic Tutoring
A peer-to-peer booking and discovery marketplace connecting families with vetted youth athletic coaches and academic tutors with real-time scheduling and escrow payouts.
Have a process that should work better?
Bring us the bottleneck, the brittle build, or the idea. We'll give you a direct read on what to do next.