SAP to open-stack ERP migration
Manufacturer, 500+ employees. Phased migration off SAP ERP onto an open stack with custom modules — run in stages while the plant kept working.
- 0 downtime
- 8 mo migration
- −40% on licences
Python development: from data pipelines to production services
Python is where the analysis happens and, increasingly, where the product does too. The gap between those two states — notebook and service — is most of the work we get asked for.
What we can show you
Overview
From notebook to service
Python is where the analysis happens and, increasingly, where the product does too. The gap between those two states is most of the work.
The gap between a notebook that works and a service you can run
A notebook that produces the right number once is not a system. Turning it into one means pinned dependencies, migrations, configuration that is not hard-coded, structured logging, retries, monitoring and a deployment somebody other than the author can run.
We quote that conversion honestly: it is usually more work than the original analysis, and it is the difference between a result and a product.
When Python is the right call
Data-heavy products, AI features, integration and automation work, and teams whose analysts already write Python. If your team is standardised on TypeScript end to end, Node keeps the maintenance surface smaller.
Signals a Python project is ready to become a service
- a notebook or script is now business-critical;
- somebody re-runs it by hand on a schedule;
- the output feeds another system that expects reliability;
- nobody but the author can run it;
- failures are discovered by users rather than by monitoring.
Price
From $7,990
Timeline
3 weeks to first release
Payment
50/50 by milestone
Comparison
Django or FastAPI
Two good answers to different questions — and sometimes both in one product.
Criterion
DjangoFastAPI
FastAPIDjango
Admin panel out of the box
Yes, and it is good
Build it or use another tool
Typed API surface
Add-on
Native, with generated docs
Async workloads
Supported, less natural
What it is designed for
Batteries: auth, ORM, migrations
Included
Assembled from libraries
When it wins
Products needing an admin and conventions
Typed APIs and async services
Scope
What we build with it
Where this stack earns its place in a project, and what we would use instead if it did not.
Services on Django or FastAPI
Django when you want batteries and an admin; FastAPI when you want a typed API surface and speed. Both with migrations and tests.
Data pipelines and ETL
Ingestion, transformation and scheduling that can be re-run safely, with failures that are visible rather than silent.
Automation of internal processes
The scripts holding your operations together, turned into something with logging, retries and an owner.
AI plumbing
Retrieval, evaluation harnesses and cost control around language models — see AI development.
Fit
When it is the right call — and when it is not
The best stack is usually the one your team can maintain after we leave.
Right for
Data-heavy products, AI features, integration and automation work, and teams whose analysts already write Python.
Not right for
Teams standardised on TypeScript across the stack — then Node keeps your maintenance surface smaller, and we will recommend that instead.
Rates
$28–55/hour by role
Fixed builds
From $7,990
Discounts
Dedicated −10% · partners −15%
How we work
Script versus service: where the notebook ends
The part that is specific to this stack rather than true of every project.
Why us
Why teams bring us in for this stack
Four things we can prove, not four adjectives.
Notebook-to-service is a named scope
We price the productionisation separately instead of hiding it inside a feature estimate — that is where these projects usually go wrong.
Inference costs modelled up front
For AI work, cost per request is estimated during scoping and capped in code, not discovered on the first invoice.
Your data scientists keep the modelling
They own the model, we own the service, and the interface between the two is written down.
Pipelines that fail loudly
Retries, dead letters and monitoring, because a silent data pipeline is worse than none.
Industries
What we buildfor your industry
Python powers our data and AI work across logistics, e-commerce and SaaS products.
- IT companies and digital agencies
- Insurers and brokers
- Lenders and microfinance
- E-commerce
- Wholesale and distribution
- Manufacturing
- Freight and forwarding
- Clinics and medical centres
- Dental
- Car dealers and service centres
- EdTech
- Legal and consulting firms
- Equipment rental
- Real estate agencies
- Property developers
- Travel and events
- Beauty and wellness
- Construction and renovation
FAQ
Questions we get about this stack
Answered by the people who would do the work, not by a sales team.
Django or FastAPI?
Django when you need an admin, auth and conventions out of the box; FastAPI for typed APIs and async workloads. Mixing both in one product is sometimes right and we will say when.
Can you turn our notebooks into a production service?
Yes — that is a common engagement. Expect the productionisation to cost more than the analysis did; we quote it as its own scope rather than hiding it in a feature price.
How do you calculate AI inference costs?
Modelled during scoping from expected volumes, then enforced with a monthly cap in code. Inference is passed through at cost with no mark-up.
Do you do data engineering as well as web?
Yes: pipelines, warehouses and the reporting layer, with the same review discipline as application code.
Can you work with our data scientists?
That is the usual setup — they own the modelling, we own the service, and the interface between the two is agreed in writing.
Do you offer Django development services separately?
Yes. Django development services — admin, auth, migrations and the reporting layer — are the most common shape of Python work we take, with FastAPI added where the API surface needs to be typed and async.
2–4×
faster on the code-writing share
in 3 weeks
from idea to MVP
230+
projects delivered
17 years
of engineering experience
Work
Work we can show you
SMARTWILL
Digital inheritance service (Web3)
AIRDEVELOPPA
Indoor air quality finder driven by sensor data
CAR5
Car-sharing app
APP.DENTAL
Cloud CRM for dental clinics
NEXORA
Warehouse and logistics management platform
As-built docs
Digital as-built documentation
EX211
Telegram Mini App for currency exchange
SMARTWILL
Digital inheritance service (Web3)
Need this stack with a senior review included?
Tell us what exists today — a green field, a legacy codebase, or a team that needs two more pairs of hands. We answer with a scope, a price and a date.
We do not take work on a stack we cannot review internally at senior level.