Python and Node.js development services

We write and review Python and Node.js software: APIs, back ends, data services and integrations, with tests and documentation, in a form your own engineers can take over.

What we build

• APIs and back-end services. REST and event-driven services with clear contracts, authentication, validation and versioning.
• Data services. Ingestion jobs, ETL and ELT pipelines, schedulers and small internal tools that move data between systems reliably.
• AI feature integration. Wrapping models and LLM calls in services with timeouts, retries, caching, structured outputs and tracing. See NLP and LLM applications.
• Integrations. Connectors to payment, CRM, messaging and other third-party systems, written to tolerate their failures.
• Web back ends for products. Node.js with TypeScript for services that sit close to a JavaScript front end, and Python where the data and ML ecosystem matters.
• Refactoring and rescue. Adding tests, structure and typing to a codebase that has grown without them, then making it safe to change.

How an engagement runs

1. Specify. A short document covering behavior, interfaces, constraints and what is out of scope, written so that it can be checked against the finished work.
2. Set up the foundation. Repository structure, linting, typing, test harness, containerized local environment and a CI pipeline, before feature work starts.
3. Build in small increments. Pull requests that are reviewed, tested and deployable to a staging environment, so you see progress in working software.
4. Test at the right levels. Unit tests for logic, integration tests against real dependencies or realistic doubles, and contract tests for external APIs.
5. Deploy and observe. Release with health checks, logging and metrics, and a rollback path.
6. Hand over. Documentation, architecture notes and a code walkthrough with your team.

Engineering practices

• Types and validation at the edges. Pydantic models in Python and TypeScript types plus runtime schemas in Node, so bad input fails early with clear errors.
• Boring dependencies. Maintained, widely used libraries, pinned and scanned, with a note on why each non-obvious one was chosen.
• Secure by default. Secrets outside the code, least-privilege credentials, input handling that assumes hostile input. Related reading: security controls for personal data.
• Privacy-aware design. Collect less, log less, and make deletion possible. If your service stores personal data, see how teams approach data subject request automation and data mapping.

Typical stack

• Python with FastAPI, Pydantic, SQLAlchemy, Celery or a queue worker; pytest for tests.
• Node.js with TypeScript, Express, Fastify or NestJS; Vitest or Jest for tests.
• PostgreSQL, Redis and object storage; message brokers or cloud queues where events are needed.
• Docker for packaging, GitHub Actions for CI, and Terraform for infrastructure; see DevOps.
• OpenAPI specifications so front-end teams and partners can work against a stable contract.

Deliverables

• Source code in your repository, with a readme that lets a new engineer run it in minutes.
• Automated tests and a CI pipeline.
• API documentation generated from the code.
• Deployment configuration and a runbook.
• An architecture note describing the main decisions.

Fixed scope or ongoing work

Some projects have a clear boundary, such as an API with a defined set of endpoints or a connector between two systems. Others evolve as you learn what users need. We can work either way, and we will say which we think fits. For well-defined work, the written specification doubles as the acceptance checklist. For evolving work, we agree on a cadence, review priorities regularly and keep every increment deployable so that you can stop or redirect at any point without a half-built system left behind.

Whichever shape the work takes, you should always be able to see the current state: a running staging environment, a list of merged changes and the tests that cover them. We think visible progress is the most honest status report. We also keep estimates grounded. We will describe the uncertain parts of a project as uncertain, and we will not give a promised delivery date before understanding the work.

If you already have engineers, we can focus on the parts you would rather not build, such as the data ingestion layer or the AI integration, and coordinate through the interfaces both sides define. If you do not, we can own more of the design while keeping the code simple enough for a future hire to understand.

Frequently asked questions

See all services, or machine learning if the software centers on a model.