Python development for APIs, automation, data and AI

Python is the language of data and AI, and one of the most practical for business automation and web back ends. We use it to build secure APIs, data pipelines, automation that takes repetitive work off people, and the code behind AI agents that have to compute an answer rather than guess one.

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What we cover

Why PythonOne language from a script to a production system
API developmentSecure REST APIs and web back ends
AutomationTake the repetitive work off people
Data engineeringPipelines and data processing
Data science and AIThe code behind AI that has to be right
Existing codeTaking over scripts that have become critical

Why Python

One language from a script to a production system

Python is readable, has a library for almost everything, and runs anything from a small script to a large production system. That range is what makes it useful to a business: the same language can automate a weekly report, serve an API, transform data in a pipeline and run a machine learning model, so one team can maintain all of it. We write Python the way production software should be written, typed, tested, reviewed and documented, so it does not become a pile of scripts only one person understands.

What we do

Typed, tested and reviewed code
Packaging and dependency management
Automated builds and deployment
Documentation your team can maintain

API development

Secure REST APIs and web back ends

We build REST APIs and web back ends in Python with Django and Flask: Django when an application needs everything included, such as users, an administration site and a database layer; Flask for lean services and APIs that need only what they use. Every API authenticates its callers, validates its input and documents itself, so the teams that use it do not have to guess. Back ends run in containers or serverless functions on Azure or AWS.

What we do

REST APIs in Django and Flask
Web back ends and administration sites
Authentication and input validation
Containers and serverless functions on Azure and AWS

Automation

Take the repetitive work off people

A lot of office work is copying data from one place to another, checking it and producing a report, and Python is good at all three. We automate the extraction from websites, portals, files and databases, the checks and the reporting, with logs and alerts so someone knows when a run fails. In one of our internal projects, a Python pipeline logs in to five government procurement portals each morning, reads about 2,000 listings, parses the bid documents and emails a short list of the 20 to 50 worth quoting.

What we do

Web and portal automation with Playwright
File, spreadsheet and report automation
Scheduled jobs with logs and alerts
Hand-offs to a person when a check fails

Data engineering

Pipelines and data processing

Python is the default language for data work. We write the pipelines that extract data from large databases and APIs, clean and reshape it, and load it into a warehouse or lake, using pandas for data that fits on one machine and Spark when the volume needs a cluster. Each pipeline checks what it loaded and reports what it could not, so a bad file does not quietly become a wrong report.

What we do

Extraction from databases, files and APIs
Transformation with pandas and Spark
Validation of every load
Scheduling and monitoring

Data science and AI

The code behind AI that has to be right

Language models are good at conversation and unreliable at arithmetic. When an agent has to give a figure, we have it call Python to compute the answer from the data instead of summarizing. For a national food cooperative, marketing staff ask about paid-media results in plain English: the agent reads campaign data from SharePoint, runs the calculations in Python on Azure Functions and returns charts and Word or Excel reports. We also build machine learning models in Python for forecasting, classification and document processing, and the services that run them.

What we do

Python tools that AI agents call
Machine learning for forecasting and classification
Document processing with Azure AI Document Intelligence
Models served on Azure or on local hardware

Existing code

Taking over scripts that have become critical

Many companies have a Python script that started small and now runs something important, written by someone who has moved on. We review it, add tests around what it does today, move it to a supported Python version, put it under version control with automated deployment and document it, so it can be changed safely. Then we decide with you whether it should stay a script or become a proper service.

Case studies

Python development in practice

Client names are withheld. We are happy to share details on a call.

All case studies

Delivered
KPIs on request
Food manufacturing
Application Software Development
Marketing performance agent for a national food cooperative
Marketing staff ask about paid-media results in plain English. The agent reads campaign data from SharePoint, runs the calculations in Python on Azure Functions rather than summarizing, and returns charts and Word or Excel reports.
- Campaign KPIs without waiting for an analyst
- Secure access with managed identity, no stored passwords
Read the case study →
Delivered
Every change, flagged
Food manufacturing
Application Software Development
Label comparison agent for a national food cooperative
Compares two versions of a product label, highlights every change, annotates the PDF and returns an audit-ready list of differences. We moved it from Copilot Studio to Azure Functions when the document processing outgrew the low-code platform.
- Audit-ready differences in one chat turn
- Rebuilt from low-code to cloud functions
Read the case study →
Internal project
2,000 → 50 a day
Public-sector sales
Application Software Development
Procurement triage on a local 30B model
Logs in to five government procurement portals each morning, reads about 2,000 listings, parses the bid documents and emails a short list of the 20 to 50 worth quoting. Every model call runs on one desktop AI system in our office.
- 2,000 listings down to about 50 a day
- 11× faster than a 120B model, same verdicts
Read the case study →

Questions

Common questions

What is Python used for in business?

Mostly for automation, data and AI: scripts and jobs that take over repetitive work, pipelines that move and clean data, APIs and web back ends, and the models and tools behind AI applications. Its readability and its libraries make it a practical single language for all of these.

Django, Flask or something else?

Django suits applications that need users, an administration site and a database layer out of the box. Flask suits small services and APIs where you want only what you use. We choose per project, and keep to one where we can, so your team has less to learn.

Can Python handle large volumes of data?

Yes. For data that fits on one machine, libraries such as pandas are fast and simple. For larger volumes we use Spark, which spreads the work across a cluster, on platforms such as Databricks and Microsoft Fabric.

Can you work on the Python code our team already has?

Yes. We start with a review and with tests around the current behavior, so we can improve the code without changing what it does by accident, and we can work alongside your developers or take it over.

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