Python development for data pipelines and AI features.
We build pipelines that reconcile numbers across systems, read documents into the system of record, and check language models against real cases, in typed Python your analysts can read and extend. It runs beside the Node.js services and reports every failure.
- InterfaceReact · Next.js
- MobileReact Native
- ServicesNode.js · TypeScript
- DataPostgreSQL · Python pipelines
- AIOpenAI · Anthropic · chosen per task
- CloudAWS · Azure · Google Cloud
What we build with it
Where Python fits.
Data pipelines
Extracting, validating, reconciling, and loading records from operational systems into a data layer.
Document processing
Reading invoices, forms, and contracts with models and rules, with review of the uncertain cases.
AI feature integration
The code around language models: retrieval, evaluation, prompts, and the controls that make them safe to rely on.
Analysis and reporting logic
Calculations, reconciliations, and reports that are easier to express and test in Python.
Use cases
Applications we build with Python.
Forecasting and demand planning
Models that project demand, staffing, or inventory needs from historical records, with the assumptions written down and results fed into planning screens.
Data migration and cleanup
Scripts that extract, clean, deduplicate, and map records from a legacy system into a new one, with a reconciliation report for sign-off.
Recurring file and export automation
Recurring spreadsheets, exports, and files for customers, carriers, or regulators produced from live records on a schedule instead of by hand.
Search over company documents
Indexing of manuals, policies, and past work so staff can search by meaning, with results filtered by what each person may see.
Data quality monitoring
Scheduled checks that flag missing, duplicate, or out-of-range records in operational systems and send each issue to the person who can fix it.
Scheduling and optimization logic
Routing, allocation, and scheduling calculations built with established optimization libraries, returning suggestions that a planner can review and adjust.
We choose it when
- The work is data-shaped: many records, transformations, reconciliation, evaluation.
- A model or library is available only in Python, or is best supported there.
- Analysts on your side already work in Python and will maintain the code.
We reach for something else when
- The interface and its API: those stay in TypeScript on Node.js.
- A simple integration between two systems: a Node.js service is usually enough.
- A vendor tool already does the pipeline well: configure it and connect to it.
How we use it
We use Python for pipelines, processing, and model work, with typed code, tests on the transformations, and scheduled runs that report failures. Results land in PostgreSQL or the data layer, where the Node.js services and the reports read them. It deploys through the same pipeline as the rest of the application.
What it means for you
- Typed, tested code on an open-source language, organized so the next engineer can follow it.
- Analysts who know Python can read and extend the work.
- Every scheduled job has monitoring and a runbook.
Engineering practice
How we build with Python.
Type hints and static checks
Functions carry type hints checked by tools such as mypy or Pyright on every build, which catches errors before code reaches a scheduled run.
Tests on every transformation
Each transformation is tested with small, representative sample records, including the awkward cases, so a change in logic cannot quietly alter results.
Pinned and reviewed dependencies
Library versions are pinned and scanned for known vulnerabilities, and upgrades are tested against sample data before they reach production runs.
Restartable pipeline runs
Pipelines can be rerun from any step without duplicating records, so a failed run is fixed and resumed rather than started over by hand.
Validation at each stage
Records are checked against expected shapes and rules as they enter and leave each stage, with failures reported to a named owner.
Questions
Questions about Python.
What business and technology leaders ask us about it.
Why two languages?
Each is best at a different job. We use TypeScript on Node.js for the interface and services and Python for data and model work. Mixing them is normal, and each part is owned by the team that will maintain it.
Do you build machine learning models?
We integrate models, evaluate them on your data, and build the controls around them. Training bespoke models is a separate conversation and rarely the first step.
Can our analysts work on the pipelines?
That is one of the reasons to use Python. The handover includes the structure, the tests, and how to add a source.
Where do the pipelines run?
They run in the cloud on a schedule, with monitoring, alongside the rest of the application.
Is Python a good choice for production systems, not just analysis?
Yes, when it is written like production software: typed, tested, packaged, deployed through a pipeline, and monitored. Python runs data pipelines and model services at large organizations across many industries. We keep interfaces and APIs in TypeScript, and use Python where its libraries for data and models give it a clear advantage.
How do you handle Python version upgrades?
Projects run on a supported Python release with pinned library versions. Before a version reaches the end of support, the code is upgraded in a branch, tested against sample data and the full suite, and released through the normal pipeline. Library updates follow the same path on a regular schedule, so upgrades stay small and predictable.
Can Python handle large volumes of data?
Yes, with the right tools for the size. Most operational pipelines run well on standard Python with a PostgreSQL database. Larger workloads move heavy processing into the database or into libraries such as Polars, DuckDB, or Spark, and jobs process records in batches so volume can grow without a rewrite.
Scope a data or AI project.
Tell us about the application and the systems around it.We’ll come back with what we would build first, and which tools we would use.