Python automation for recurring business work
Scheduled jobs, file and document processing, internal tools, and system glue written in Python and deployed in your cloud with tests and alerting.
Python automation means writing the scheduled jobs, internal tools, and back-office scripts that replace work someone currently does by hand every week. It is the right answer when a workflow is too specific for an off-the-shelf tool and too important to leave as a manual process. It is for teams where a person spends hours moving data between systems, cleaning files, or running the same steps on a calendar reminder.
The problem
Manual recurring work rarely gets escalated, because each instance only takes twenty minutes. The cost shows up as a person who cannot take a week off without something falling over.
- Someone downloads a report, reformats it, and re-uploads it somewhere else, every week.
- A workflow lives in a spreadsheet with formulas nobody remembers writing.
- The one automation that exists is a script on a laptop, and it breaks whenever a column moves.
- A no-code tool got the workflow eighty percent there and cannot express the last twenty percent.
- Nobody knows the job failed until the person downstream asks where the file is.
What we build
Scheduled jobs
Recurring work that runs on its own, in your cloud, with a schedule and a run history you can look at rather than a cron line on a laptop under a desk.
File and document processing
Parsing, validating, and reshaping the CSVs, PDFs, and exports that arrive from customers, vendors, and systems that will never offer an API.
Internal tools
Small applications that give a team a form and a button instead of a shared spreadsheet and a set of instructions.
System-to-system glue
The code between tools that no integration platform covers, with validation and error handling on both sides.
Error handling that surfaces
Retries for transient failures, clear logs for everything else, and an alert when a human genuinely needs to look at something.
Tests around the fragile parts
Coverage on the parsing and business rules, so a change to an input format fails a test instead of silently corrupting data.
What you get at handoff
- Code in your repository with a README that explains how to run it locally.
- Deployment through your own pipeline, into your own cloud account.
- A runbook covering the schedule, the failure modes, and the manual override.
- Tests and fixtures for the parts most likely to break.
- A walkthrough with whoever will maintain it.
Stack
Language
Python, Type hints, pytest
Scheduling
Airflow, AWS Lambda and EventBridge, Cloud Run jobs, Cron
Data
Postgres, Snowflake, BigQuery, S3 and GCS
Interfaces
REST, GraphQL, Webhooks, SFTP, Email
Operations
Structured logging, Retries, Alerting, GitHub Actions
Related services
FAQ
Why Python instead of Zapier or Make?
Use Zapier or Make when they fit. They are genuinely good for simple trigger-and-action work, and we will tell you when that is the right answer. Python earns its place when the logic branches, when you need to process a file properly, when the volume makes per-task pricing painful, or when you need real error handling and tests.
Where does the code run?
In your cloud account, on infrastructure you already pay for where possible. Lambda and EventBridge, Cloud Run jobs, or an existing Airflow instance all work. We avoid introducing a new platform just to run a scheduled script.
What happens when it breaks?
Transient failures retry on their own. Anything else logs with enough context to diagnose and alerts the person who needs to know. The runbook covers the manual override, because sometimes the right answer at 4pm on a Friday is to run the step by hand and fix the root cause Monday.
Can our team modify it later?
That is what the tests and the README are for. We write plainly, avoid clever abstractions, and keep dependencies boring. If your team has any Python capability, they will be able to change it. If they do not, the code is still readable enough for the next contractor.
Is this a good first automation project?
Usually yes. A single recurring workflow with a clear input and output is the safest place to start, and it is a good test of whether the surrounding data is as clean as everyone assumes. Start there before anything involving AI.
Ready to scope Python Automation?
Send the workflow, tool stack, or reporting problem. We will tell you what should be automated, what should stay manual, and what is worth building first.