Python product scope
We define what the Python layer should own: APIs, data processing, automation, scraping, reporting, internal workflows or a focused MVP backend.
Service · Python Development
Python development for data tools, APIs and focused web MVPs.
99 Francs® builds focused Python services for startups and product teams that need data workflows, FastAPI backends, automations, market-data tools, integrations or backend logic behind a polished web interface.
Web product
Secondself
Mobile app
Digsdy
Search tool
Open Company Search
150+
shipped projects
$32M+
raised by clients
9,000+
tasks delivered
Direct answer
99 Francs provides Python development services for focused web products that need data, automation or backend logic.
The service covers Python scoping, FastAPI services, API integrations, data automation, lightweight pipelines, dashboard support, market-data workflows, QA and handoff for startup MVPs and product surfaces.
What you get
Development that keeps the website fast, editable and faithful to the design.
The build should not flatten the design or create a maintenance problem. We keep implementation decisions tied to performance, content ownership, responsive behavior and the launch goal.
FastAPI and API services
We can build lean Python services for product frontends, dashboards, market-data tools, form workflows and integrations with external APIs.
Data automation and pipelines
We structure scripts, jobs, data cleanup, enrichment, scheduled tasks and lightweight pipelines so repeated work becomes reliable software.
Launch-ready handoff
The build includes environment notes, endpoint assumptions, data models, QA checks, deployment guidance and clear boundaries for future engineering.
Best fit
Use this service when the website has to become a real launch surface, not just a mockup.
The website needs real backend logic
Use Python when the project needs APIs, data processing, automations, calculations, integrations or workflows that should not live inside a static website builder.
The product depends on data
Python is a practical fit for market data, analytics, dashboards, enrichment, reporting, alerts and tools where the value comes from structured data handling.
The MVP should stay focused
We keep the Python scope tight: the smallest useful service, job, endpoint or workflow that proves the product without becoming a large backend platform.
The frontend is already planned
Python works well behind a React/Next.js frontend when the interface needs controlled data flows, custom endpoints or automation behind the visible product.
Process
From build scope to live website.
The process keeps design, content, CMS, integrations, QA and launch readiness in one line so the final site is easier to ship and maintain.
Define the data job
We review the product goal, users, data sources, APIs, update frequency, edge cases, compliance limits and what the first useful Python service must prove.
Map the service shape
We define endpoints, jobs, data models, scheduled tasks, integrations, error states, logging needs and the frontend contract before implementation starts.
Build the Python layer
We implement the focused service, automation, pipeline or API logic with practical structure, typed assumptions and clear integration points.
Test and prepare handoff
We verify inputs, outputs, failures, deployment assumptions, data quality, documentation and the next engineering steps after launch.
Related work
Product and data-heavy interface work that can connect to a Python layer.
Until a dedicated Python case is published, these examples show the kind of product, analytics and data-interface thinking that Python development usually supports.
Search tool
Open Company Search data
B2B analytics platform for evaluating financial health and company credibility. Advanced filtering, credit scoring, and financial performance graphs for professional users.
Web app design
StakerTaker crypto
Crypto investment platform with redesigned key screens to make complex analytics more accessible. Investment dashboard with APY charts, personalized alerts, and automated tracking.
Python build
If the product depends on data, APIs or automation, give the interface a backend layer that matches the job.
Python · FastAPI · APIs · Data workflows · Automation · MVP
Start Python development→Python development FAQ
Questions teams ask before adding Python to a web product.
99 Francs offers focused Python development for FastAPI services, data tools, automations, API integrations, dashboards, market-data workflows, reporting systems and startup MVP backends.
Use Python when the project needs backend logic, data processing, external API integrations, scheduled jobs, scraping, calculations, reporting, automation or a data layer behind a frontend.
Yes. Python is often a good backend or automation layer behind a React or Next.js frontend when the visible interface needs custom data, APIs or workflows.
Not as the default offer. This service is strongest for focused MVP services, data workflows, integrations and launch-ready product layers that can later be extended by a backend team.
Yes, when the scope is focused. Python is a practical fit for market data ingestion, API integrations, analytics, reporting, alerts and internal tools that support trading or prediction-market workflows.
Indirectly, yes. Python can power data-backed pages, dashboards, structured datasets, programmatic content workflows and API-fed tools, while the public frontend still needs crawlable SEO and answer-friendly content.