Arvest Wine :

See how we supported Arvest Wine's tech team to grow its data collection from 250 to more than 850 sources.

Python
Scraping
Data collection

Client: Arvest Wine
Sector: Data Analytics / Market Intelligence
Our role: Extension of the tech team for data collection
Deliverables: Scraping bots and new raw data sources
Collaboration model: On-demand development, mission by mission
Expertise involved: Scraping, data collection
The Arvest Wine platform: arvestwine.com/analytics

In the fine wine market, information is everywhere but unusable as it stands. It is scattered across a multitude of sources, in very different formats that are rarely comparable. Professionals therefore build their buying strategies and manage their stock without an overall view of the market.

Their goal? Bring this data together in a single, reliable reference base to track prices and trends, and turn it into a real decision-making tool. Arvest Wine is where in-depth knowledge of the wine market meets data expertise.

When our collaboration began, Arvest Wine's CTO had already built the first collection bots and the platform that presents the data. At the time, it relied on around 250 data sources.

To cover more of the market, the team had to multiply its sources quickly, without technical work taking over their core business: refining, analysing and presenting the data.

They needed a complementary, responsive team able to blend into their existing team and speed up raw data collection.

Because the platform, the UX, the marketplace and data processing remain Arvest Wine's core business, we stepped in as an extension of their tech team. We worked on one specific part of their data ecosystem: data collection.

1.Building new scraping bots
We designed scrapers (targeted or large-scale) that complement the ones built in-house by their CTO, to bring in new data sources.
2.Collection that holds up over time
Sources evolve, change format or disappear. We make sure raw data keeps flowing in, reliably.
3.Raw data ready to be processed
Once collected, the data is passed to Arvest Wine, which refines it, reconciles it with its matching algorithms and delivers it to its clients. That is where their added value lies.

On-demand development

Arvest Wine calls on us as needs arise, mission by mission, on purchase orders. The collaboration adapts to their pace and nothing is set in stone: we evolve with their project.

A direct line

We talk every day in a WhatsApp group. A question, a source to add, a bot to fix: the answer comes fast.

Two startups that understand each other

A young, flexible team that adapts to the priorities of the moment: it's easy because we speak the same language. That closeness is what Arvest Wine highlights about our collaboration.

For our part, data collection:

LanguagePython
Scrapingtargeted and large-scale collection bots
HostingAWS, owned by Arvest Wine

From 250 to more than 850 data sources collected (October 2026)

Goal: 7,000 sources in the long run

With three times more sources, Arvest Wine has a much broader raw database to feed its market reference base. By entrusting us with part of the collection, their team can focus on what sets them apart: knowledge of the wine market, refining and matching the data, and the analyses they deliver to their clients. We provide the technical muscle, they keep control of their know-how.