Business automation
Scheduled scripts and workflows that fetch, clean, reconcile and send data — with alerts when something looks wrong.
Python & automation
We write Python that moves data, generates reports, talks to other systems and flags problems early — so your team spends its time on decisions, not copy and paste.
PythonFastAPIDjangoPandasCeleryStreamlit
Overview
Most businesses have at least one process that runs on copy and paste: exporting a report, cleaning it in a spreadsheet, pasting it into another system and emailing the result. It works until the person who does it is away, or a small mistake slips through.
We replace those processes with Python that does the same steps reliably, every time, and tells someone when something looks wrong. We start by mapping how the work is done today, prove the automation on real data, then add the logging, alerts and documentation that make it safe to depend on.
The same skills power larger systems too: APIs for web and mobile apps, data pipelines feeding dashboards, and the back ends of AI features.
What we deliver
Scheduled scripts and workflows that fetch, clean, reconcile and send data — with alerts when something looks wrong.
Reliable ETL from spreadsheets, databases and APIs into one clean store, ready for reporting.
FastAPI or Django services with authentication, documentation and tests, ready for your web or mobile app.
Internal dashboards in Streamlit or a web front end, and automated PDF or spreadsheet reports delivered on schedule.
Connections to payment providers, CRMs, accounting tools and public data sources — scraping responsibly where no API exists.
Classification, forecasting and anomaly detection with scikit-learn where a simple model genuinely helps.
Signs you need this
Tech stack
Python 3FastAPIDjangoFlask
PandasPolarsSQLAlchemyPostgreSQLDuckDB
CelerycronGitHub ActionsDocker
scikit-learnStreamlitJupyterGoogle Sheets API
How we work
We sit with the people who do the work today and document each step, input and exception.
A first script on real data, so you can check the output against what your team produces by hand.
Error handling, logging, retries, alerts and secure storage of credentials — the parts that make automation trustworthy.
Documentation, a runbook and a short training session, with the code in your repository.
Proof
80%
An agent that classifies each question and routes it to the cheapest capable model — 80% lower cost in testing than always using the largest one.
Ways to work
Clearly defined deliverables at a fixed price — ideal for websites, audits and well-understood builds.
See packages →For bespoke products and platforms: a short discovery, then a written proposal with milestones and a fixed or capped price.
Request a quote →Ongoing support, maintenance and improvement with a guaranteed response time and a set number of hours each month.
Discuss a retainer →Flexible time-and-materials help for troubleshooting, code reviews, consulting and team augmentation.
Book time →Questions
Can’t see your question? Ask us directly — you’ll get a straight, practical answer, even if it’s “you don’t need us for this”.
Anything repetitive with clear rules: moving data between systems, reconciling payments, generating reports, sending reminders, checking stock or prices, and processing files that arrive by email. If a person follows the same steps every time, it is a good candidate.
Python has excellent libraries for data, integrations and machine learning, reads clearly and is easy to hire for. It is a practical default for automation and data work.
On a small server or cloud function you own, on an existing server, or as scheduled jobs in your CI system. We choose the simplest option that is reliable and secure.
Good automation fails loudly. We add logging, retries for temporary errors and alerts by email, Slack or WhatsApp when a run fails or the data looks unusual, so problems are caught before they spread.
Yes. Many automations start with Excel or Google Sheets. We can keep a spreadsheet as the place people review results while the heavy lifting happens in code.
It depends on the site’s terms, the data involved and how it is used. We prefer official APIs, respect robots rules and rate limits, avoid personal data, and will tell you plainly if a scraping idea is risky.
Usually not. Most gains come from clean data and reliable automation. We suggest machine learning only where it clearly beats simple rules.
Tell us what you’re building, or what’s broken. We’ll come back with questions, a suggested approach and the simplest sensible next step — no obligation.