Python development
Python, for the work that has to be read as well as run.
Python is where data work, automation and back-end services meet. It is readable enough that the person maintaining it in two years can follow the intent, which matters more on this kind of code than raw speed does.
SEECO's website and Duncan Farms' system are both built in Python.
Overview
Python, the way we do it
Python earns its place in the parts of a system that get read as often as they get run: the job that reconciles two datasets, the service that processes an upload, the script that quietly holds a business process together. When something goes wrong at month end, the person opening that file needs to understand it quickly.
It is also the language the data and AI ecosystem is written in, so the same team can move between a reporting job and a model without switching stack. We use it for back-end services, automation and data work, and we type and test it like anything else we expect to maintain.
What's included
What you actually get
- Back-end services and APIs, with types and tests like any other code we keep
- Data processing, reconciliation and reporting jobs
- Automation of the manual steps that currently need a person
- The same language across data, AI and back-end work
- Deployed on infrastructure we configure and monitor
How we work
Five steps, no surprises
- 01
We talk
A call or a WhatsApp thread. You tell us what is not working; we tell you honestly whether we are the right people.
- 02
We scope it
A written plan with what you get, what it costs and how long it takes. Fixed, so there are no surprises later.
- 03
We build it
You see it as it goes, not at the end. Changes are cheap while it is still being built.
- 04
We put it live
On infrastructure we set up and secure, tested before the launch date.
- 05
We keep it running
Updates, monitoring and someone who answers. Most clients stay on a monthly agreement.
Questions
About python
Is Python fast enough?
For almost everything a business system does, yes. The bottleneck is nearly always the database or the network. Where raw speed genuinely matters we would suggest Go for that piece rather than rewriting everything.
Django or FastAPI?
Django when you want a full application with an admin interface out of the box; FastAPI when you are building an API and want it lean. We will explain which fits before starting.
Can you take over a Python codebase?
Yes. We read it first and report what we find, including the dependency and test situation, before quoting on new work.
Also
Related work
- Quality AssuranceFind it before your customers do.
- UX DesignWork out what it should do before anyone builds it.
- UI DesignScreens people can use without being trained.
- Design SystemsOne set of rules, so the tenth screen looks like the first.
- Mobile ApplicationsApps people keep on the first screen.
- iOSiPhone apps built the way Apple expects.
- AndroidAndroid apps that work on the phones people actually own.
- FlutterFlutter, when one team has to cover both stores.
- AIAI that does one job, properly.
- Machine LearningModels that earn their place in the business.
- Data ScienceAnswers from the data you already have.
- LLMsLanguage models, kept on a short leash.
- Generative AIGeneration with a human still holding the pen.
- Back-EndWhere an order becomes an order.
- DatabaseThe part of your system that is hardest to fix later.
- Node.jsNode.js, for the systems that have to answer quickly.
- GoGo, where it has to be fast and stay simple.
- .NET.NET, for the systems a business runs on.
- JavaJava, for systems measured in decades.
- Front-EndThe half of your product people actually see.
- Web DevelopmentCompany sites, customer portals, and web systems that hold up.
- ReactReact, built so the next team can still work on it.
- AngularAngular, for systems that have to last.
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