Business automation
Scheduled scripts and workflows that fetch, clean, reconcile and send data — with alerts when something looks wrong.
Python & automation
Turn the spreadsheet grind into systems that run themselves.
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
- Best for
- Operations and data teams buried in manual work
- Start with
- Show us the process you want to automate
- Ways to work
- Fixed scope per automation, or retainer
- Where
- Lagos · remote, worldwide
Overview
Automation that is boring in the best way.
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.
Who it’s for
- Finance and operations teams rebuilding the same reports every week
- Businesses re-typing data between systems that do not talk
- Teams that need a dashboard on top of messy data
- Products that need a Python API or data service
What we deliver
What you get
Data pipelines
Reliable ETL from spreadsheets, databases and APIs into one clean store, ready for reporting.
APIs and microservices
FastAPI or Django services with authentication, documentation and tests, ready for your web or mobile app.
Dashboards and reports
Internal dashboards in Streamlit or a web front end, and automated PDF or spreadsheet reports delivered on schedule.
Integrations and web data
Connections to payment providers, CRMs, accounting tools and public data sources — scraping responsibly where no API exists.
Machine-learning features
Classification, forecasting and anomaly detection with scikit-learn where a simple model genuinely helps.
Signs you need this
This is for you if…
- Someone spends hours every week copying data between systems.
- Your monthly report is a ritual of downloads, formulas and prayers.
- Errors in manual data entry are costing you money or credibility.
- You have data in five places and no single view of the business.
- A key process depends on one person remembering to run a script.
- You need a Python back end for a data-heavy product.
Tech stack
Tools we reach for
Language and frameworks
Python 3FastAPIDjangoFlask
Data
PandasPolarsSQLAlchemyPostgreSQLDuckDB
Scheduling and workers
CelerycronGitHub ActionsDocker
Analysis and interfaces
scikit-learnStreamlitJupyterGoogle Sheets API
How we work
From manual process to reliable system.
Map the process
We sit with the people who do the work today and document each step, input and exception.
Prototype
A first script on real data, so you can check the output against what your team produces by hand.
Productionise
Error handling, logging, retries, alerts and secure storage of credentials — the parts that make automation trustworthy.
Hand over
Documentation, a runbook and a short training session, with the code in your repository.
Proof
Work we can point to
80%
Digital Twin routing agent
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
Pick the model that fits the work.
Fixed-scope packages
Clearly defined deliverables at a fixed price — ideal for websites, audits and well-understood builds.
See packagesCustom quote
For bespoke products and platforms: a short discovery, then a written proposal with milestones and a fixed or capped price.
Request a quoteMonthly retainer
Ongoing support, maintenance and improvement with a guaranteed response time and a set number of hours each month.
Discuss a retainerHourly / daily
Flexible time-and-materials help for troubleshooting, code reviews, consulting and team augmentation.
Book timeQuestions
Frequently asked 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”.
What kinds of tasks can be automated?
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.
Why Python?
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.
Where will the automation run?
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.
What happens when something fails?
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.
Can you work with our spreadsheets and existing tools?
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.
Is web scraping allowed?
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.
Do we need a data scientist?
Usually not. Most gains come from clean data and reliable automation. We suggest machine learning only where it clearly beats simple rules.
Have something in mind? Let’s scope it.
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.