No published Python case yet
Python is the best tool when the core job is reading, cleaning, joining and analyzing data, or wrapping an API around that data. We help you decide whether it’s the right language for your project, what the architecture would look like and who will be able to maintain it.
If several of these sound familiar, Python probably has something to offer:
Every week someone exports files from two or three systems and reconciles them by hand in Excel.
Reports take days, and when they arrive nobody fully trusts the numbers.
Information arrives in PDFs or spreadsheets and has to be keyed in again.
You want to forecast something (demand, inventory, churn) and today it’s a gut call.
Your analysts already work in Jupyter notebooks and need that work to run on its own.
We haven’t published a client project built in Python, and we’d rather tell you than dress it up. Our production products, the AI publishing platform and the multi-tenant CRM, are written in TypeScript on Node.js because they’re mostly web integrations; we use Python for internal tooling, such as extracting text from documents and processing audio files. So we won’t pitch Python because it’s trendy. We recommend it when the problem is about data and its ecosystem saves real work: pandas or Polars for reshaping tables, scikit-learn for classic models, and mature libraries for reading PDFs, Excel files and scientific formats.
We pick a language based on the job and on who will maintain it, not on personal taste.
Illustrative example, not a client: a regional distributor exports sales from its ERP and orders from its Shopify store every Monday, and someone spends half the morning reconciling them. A Python script can read both files, normalize the SKUs, flag gaps between what was sold and what shipped, and drop the report into a shared folder before the team logs on. If other systems later need those numbers on demand, the same code becomes an API.
Not every Python project is an application. Often the first column is all you need.
General guidance as of October 2026. FastAPI and Django are the two Python frameworks we most often put on the table; the final pick depends on the project.
The same standards we hold our own products to, applied to Python.
Pinned versions of Python and every library, so the project runs the same on your laptop, on the server and two years from now.
Every file or request is checked against a schema before processing. Malformed data gets rejected with a clear message instead of breaking the report.
Automated tests on real, anonymized files, so a change can’t quietly alter the results.
Runs at set times, with a log of what was processed and a retry when a source doesn’t respond.
How to run it, where the data comes from and what to do when it fails, written for whoever inherits it.
You share real files, anonymized if needed. Without seeing the data, any estimate is a guess.
A script that does the job once, end to end, to confirm the output is correct and useful.
Configuration, validation, tests and whatever runtime the case calls for: a scheduled job, an API or an app.
We put it into production, review the first runs with you and document how to maintain it.
Real reviews our clients left on Google.
Viviana Hidalgo Alvarado
October 2026
Dayanna david tapias
October 2026
Daniel Gómez
October 2026
Andrés Felipe Pájaro Martinez
October 2026
Sergio Andres Pájaro Martinez
October 2026
Johnattan Rodriguez
December 2025
Evelintoya1011 Montoya
December 2025
Diana C
November 2025
Rafael Castaño
November 2025
IVAN BLANCO
November 2025
Juan Jose Cano Giraldo
November 2025
Litografia Azabache
April 2024
Tomás Vélez
February 2024
Adriana Hernandez
February 2024
It depends on the main job. For analyzing data, training models or processing documents, Python’s libraries are more mature. For connecting APIs, handling events and responding in real time, Node.js is usually more direct, which is why our own integration products run on it. Some projects use both: Node.js for the web layer and Python for the analysis.
When what you need is mainly an interactive web interface, a service-to-service integration or a feature inside WordPress. Also when nobody on your team or your vendor’s team can maintain Python code: a system only one person understands is a liability.
Not yet. We use Python for internal tooling, but we don’t have a client project in Python we can show publicly. If that’s a requirement for you, we’ll say so in the first conversation.
We review it first: Python version, dependencies, tests, documentation and how it’s deployed. From that review we tell you what risks we see and whether it makes sense to continue it, stabilize it or replace parts of it. We don’t take on a codebase we haven’t read.
Not necessarily. Using models like OpenAI’s only takes an API call, and our AI assistants are written in TypeScript. Python becomes necessary when you’ll train or fine-tune your own models, or crunch large volumes of data before using them.
When your Python project needs user accounts, permissions and an admin interface.
For integrations, APIs and real-time work.
Processes that run on their own, with human review where it counts.
Marketing and sales measurement and reporting, before anyone writes code.
Tell us what information you handle, where it comes from and what decision you want to make with it. We’ll tell you whether Python is the right tool and how we’d start.
If you can, attach a sample file (no sensitive data) and an example of the report you build by hand today.
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