Having run roadmaps for tier-1 iGaming operators for a combined >15 years, we are all too familiar with the permanent pressure of large scale initiatives and regulatory must-haves on operator roadmaps.
To support operators overcome such difficulties to free up resources, Vaix, from the outset, focussed on easy integration with little or almost no dev team use:
Easy, minimal effort, integration
Our only integration points are data collection & results presentation
- Results presentation: we support integration via REST API, web interface, write to DB in any standard internal or 3rd party platform, upload on SFTP and other solutions. We have built front-end plugins to platform providers like kambi for a seamless integration of our results to the operator’s website.
- Data integration: we support both batch-based (e.g. SFTP, DB tables/views) and real-time (e.g. Kafka, Rabbit MQ, Amazon SQS), methods.
Scalable, mature architecture
- Modular, RESTful API and web services support up to 1 million active users/monthly per installation, at peaks of over 20k requests per minute.
- Our infrastructure, deployed across Europe, US and Asia, runs in our data center, all major cloud providers (e.g. Microsoft Azure), on request even on the operator’s infrastructure.
Deep Learning, affordable
In AI, Deep Learning has shown to beat other algorithms in relevance and performance.
However, building Deep Learning requires heavy resource & infrastructure investment before seeing results, as well as specialized skills for ongoing optimization, keeping operators’ core dev resources away from other key initiatives.
Having built our own infrastructure, Vaix can offer Deep Learning AI at prices no provider can offer via AWS or comparable.
Deep Learning, fast and constantly improving
Using our experience, you can:
- Launch fast: from data to production-ready model & API in 5 days, bridging the adoption gap to other tech leaders.
- Iterate quickly, and
- Benefit from ongoing optimization of the AI
…while allowing you to focus your teams on your core competence.