Find your next wine

This is an OLAB demo for a wine recommendation engine. It is intended to be a proof of concept and to illustrate what can be achieved. We "only" have 15,000 wines listed so you might not find your favourite one!
Here is a random wine for a quick test: Zarachè Trebbiano d'Abruzzo, Pasetti

What is this demo for?

Data is an important asset for companies. Are you using yours to its full advantage? In this demo, we show an example of a recommendation engine that is built using the properties of wine in order to increase sales.

We would love to show you how we can optimise your sales through data. If you are interested in finding out more about us and how we can help you, please get in touch.
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How does it work?

Collect

Collect and process data

Each wine has several characteristics that are processed and stored in a GraphDB.

Connect

Calculate similarities

Wines are connected based on similarities such as grapes, structure and food pairing.

Search

Search wines on the graph

Wines can now be quickly clustered and searched based on similarities.

Other uses

The wine recommendation engine is only a demo, here are a few examples of how else it can be used.

Product sales

Add upselling recommendations based on sales data. Increase sales by adding a real-time recommendation engine based on user cart items.

User behaviour

Show the most interesting content to your users based on what they see, play, like or read. Understand their behaviour and suggest things that they would like.

Travel tips

Provide real-time recommendations based on the users' travels and interests. Increase the number of customers that endorse your services.

Product characteristics

Recommend what users would like based on similar products that they already know about. Based on what users prefer, products with similar characteristics will be suggested.

Why a recommendation engine?

People would be more interested in buying your products if you can recommend something that they would like.


35%

Amazon

35% of what consumers purchase on Amazon come from product recommendations.

[Source]
75%

Netflix

75% of what people watch on Netflix comes from the recommendation algorithm.

[Source]
+20%

Alibaba

+20% conversion on personalised landing pages compared to non-personalised pages.

[Source]
66%

Adobe

66% of consumers would stop making a purchase if they did not see personalised content.

[Source]

Frequently Asked Questions

Can we integrate the recommendation engine into our cart?

Yes, the engine provides recommendations that can be used anywhere on your website, product pages, homepage, cart etc…

How much time would you need to build a working demo for me?

From when we receive your data we would build a working demo in 1 week and a production ready service in 2 weeks.

Would you build it with your proprietary engine?

No, you won't pay any monthly fee for a proprietary product or service. We will build your recommendation engine with open source technologies.

Would you need access to our source code?

No, we won't need access to your source code. We will build a separate service that can be integrated with your code via API.

Should we send our data outside our infrastructure?

No, not if you don't want to. The recommendation engine can be hosted in the cloud or into your own infrastructure as well.

Would you maintain the recommendation engine?

Yes, if you want. We can offer a maintenance service after the release. Otherwise the recommendation engine will be based on open source code accessible to any developer in your team.

Why should I choose OLAB?

Because we can offer bespoke solutions with a strong background in web-based applications that collect, process and show data.

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