What is Python development?
Python development is building software in Python, a general-purpose programming language with the widest set of libraries for AI, machine learning and data work. It is used for web services and APIs, data pipelines, automation scripts and the models and retrieval systems behind most AI features.
We use Python where the work is about data: cleaning and joining it, training or calling models, retrieving the right passages for an AI answer, or producing a report on a schedule. FastAPI wraps that work in a fast, documented API that the rest of your system can call, and Django suits back-office applications that need an admin interface out of the box.
AI goes in only where it earns its place. If a rule or a well-designed form solves the problem, we build that instead. When AI is the right answer, the records it depends on have to be reliable first.
- Which documents does someone read just to find one answer?
- Which report is rebuilt by hand every week?
- Which data is copied between systems on a schedule?
How a Python or AI build runs
AI output is checked against real cases before anyone relies on it.
1. Bottleneck review
We map the task, the data it depends on and who owns the result. We say plainly if AI is not needed.
2. Plan
Data sources, approach, how success is measured, timeline and cost, approved by you before the build starts.
3. Build in short cycles
A working pipeline early, tested against real examples your team provides, improved each cycle.
4. Run in parallel
The automated output is compared with the manual result until the team trusts it.
5. Go live and train
Deployed as a monitored service. Staff learn how to review, correct and escalate output.
6. Support and improve
A named support contact, regular checks of accuracy and updates as your data and needs change.
Why Python and AI projects stall
Most stall in the gap between a promising notebook and a service people rely on every day.
Stuck in the notebook
A model or script works on one laptop but never becomes a service with an API, logs and an owner.
Data not ready
AI is added before the records are clean and complete, so it gives confident answers from bad data.
No way to measure
Nobody agreed what a good answer looks like, so there is no way to tell if the AI is improving or getting worse.
AI where a rule would do
A model is used for a decision a simple rule could make more cheaply and more predictably.
Scripts nobody owns
Automation runs from someone's machine on a timer. When it fails or they leave, the work stops quietly.
Data sent without thought
Sensitive records sent to outside AI services without checking where they go or who can see them.
What we build with Python
Each service has a named owner, a measure of whether it is working and a person who reviews its output where it matters.
- 01
APIs with FastAPI, Django and Flask
Documented services that expose models, data and automation to your web and mobile apps.
- 02
RAG pipelines
Search over your own documents and records, so AI answers come with sources your staff can check.
- 03
AI agents and assistants
Assistants that draft, summarise, answer and route work, with a person approving anything that matters.
- 04
Machine learning models
Classification, forecasting and extraction models with PyTorch, TensorFlow or scikit-learn, trained and tested on your data.
- 05
Data processing and reporting
Pipelines that clean, join and summarise data into the reports and dashboards managers use every week.
- 06
Automation and integration scripts
Scheduled jobs that move data between systems, with logs, retries and alerts instead of someone's laptop.
Where Python fits, and where we'd choose something else
Python is our first choice for AI and data work. For other parts of a system, another tool is often simpler.
| Task | Our usual choice | Why |
|---|---|---|
| AI features, RAG and model serving | Python with FastAPI | The AI and data libraries are in Python, and FastAPI makes them callable by the rest of the system |
| Data pipelines and scheduled reports | Python | Strong libraries for cleaning, joining and summarising data |
| Back-office app with an admin panel | Django | Admin, auth and data model included, so less to build |
| Real-time staff screens and live status | Node.js and React | Better fit for many open connections and shared types with the frontend |
| A one-off integration between two SaaS tools | A workflow automation tool | Cheaper to set up and change when the logic is simple |
Ways to work with us
Pick the model that matches where your data or AI project is today.
| Model | Best when | How it is priced |
|---|---|---|
| Bottleneck review | A prototype or script works but is not trusted in daily use | Fixed fee for a short, defined engagement |
| Defined build | The data, use case and success measure are agreed | Fixed scope, paid against agreed milestones |
| Managed team | You need a Python and AI team for a set project and period | Monthly, based on the team composition you need |
We quote after the first conversation, once we know the scope. Every system is handed over with documentation and a named support contact.
Our Python toolkit
Chosen per project. Where data must stay with you, we use models that can run in your own infrastructure.
Web and APIs
- Python
- FastAPI
- Django
- Flask
- Pydantic
- Celery
AI and ML
- PyTorch
- TensorFlow
- scikit-learn
- Hugging Face
- LangChain
- LlamaIndex
Data
- pandas
- Polars
- PostgreSQL
- pgvector
- Airflow
Testing
- pytest
- Evaluation sets
- Ruff
- mypy
Deployment
- Docker
- GitHub Actions
- AWS
- Google Cloud
- Azure
Where to go next
RAG Development Services
RAG development services that let AI answer from your records, policies and manuals, citing sources, with controlled costs and data kept in systems you control.
Learn moreMachine Learning
Practical machine learning development: classification, demand and occupancy forecasting, anomaly detection and document reading, used only where ML beats a rule.
Learn moreWorkflow Automation
Workflow automation for follow-ups, hand-offs, approvals, data movement and reports. Every run is logged and a named owner is alerted when something fails.
Learn more
Frequently asked questions
FastAPI for APIs and AI services, Django for back-office apps that need an admin panel, Flask for small services or existing Flask code.
Yes. A RAG pipeline finds the relevant passages in your documents and the model answers from them, showing its sources so staff can check.
No. Where data must stay with you, we use models that run in your own cloud or on your own servers.
We agree a set of real test cases with your team before building, and measure every change against them.
Yes. We wrap it in an API, add tests, logging and monitoring, and deploy it so it runs without anyone's laptop.
Yes. The Python service usually sits behind a React or Next.js interface that we build or that your team already has.
Yes. If a rule, a report or a better form solves the problem, that is what we recommend.
Bring us your bottleneck.
If a process in your organisation depends on one person remembering to follow up, describe it to us. You will get a straight answer on whether it needs automation, software, a team, or none of these.
Or write directly to support@algonsolutions.com