Greenfield services
New APIs designed around your domain — schema, auth, versioning, rate limits and deployment pipeline included.
Services
Python work, from first commit to production support.
Everything below is delivered by the same senior engineers who scoped it. No handing your project to a junior team after the contract is signed.
Backend & API development
Services that carry real traffic and keep carrying it at 3am. We build with FastAPI when latency and typed contracts matter, Django when you need the batteries, and we're equally comfortable extending whatever you already run.
Async & background work
Celery, RQ or asyncio task systems for jobs that outlive a request: imports, exports, notifications, scheduled reconciliation.
Integrations
Payment providers, CRMs, logistics APIs, internal legacy systems — with retries, idempotency and a clear failure story.
Performance work
Profiling, query tuning, caching layers and connection management when the service is slow and nobody knows why.
Data engineering
Getting data from where it is created to where decisions are made, on a schedule, without a person babysitting it. Pipelines that fail loudly rather than quietly.
ETL & ELT pipelines
Orchestrated with Airflow, Prefect or Dagster; idempotent, backfillable and instrumented from the first run.
Warehouse modelling
Raw sources shaped into tables analysts can actually query, with dbt tests guarding the assumptions.
Data quality
Freshness, volume and schema checks that page someone when an upstream source silently changes.
Migrations
Moving between databases or warehouses with a dual-write cutover, so there is never a day without data.
Automation & internal tooling
The highest-return Python work is usually the least glamorous: the recurring manual process that costs a team six hours a week and occasionally goes wrong.
Process automation
Spreadsheet-and-email workflows converted into scheduled jobs with audit logs and exception reports.
Internal tools
Admin panels, CLIs and dashboards that let your team do the thing without filing a ticket to engineering.
Document generation
Invoices, statements and regulatory reports produced from source data, correct every time.
Data collection
Resilient scrapers and API harvesters, built to respect rate limits and survive markup changes.
Legacy rescue & ML integration
Two problems that look different and are actually the same job: making unfamiliar code safe to change.
Version upgrades
Python 2 to 3, ancient Django to current LTS, unpinned requirements to a reproducible lockfile.
Characterization tests
Test suites written around code that has none, so the first refactor is not also the first outage.
Model serving
Notebooks turned into inference APIs with batching, versioned artifacts and latency budgets that hold.
LLM integration
Retrieval pipelines and model-backed features with evaluation, cost controls and sane fallbacks.
Not sure which of these you need?
That is a normal place to start. Describe the symptom and we'll help you name the problem.