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🔦 Deal in the spotlight -

🔦 Deal in the spotlight -

(by Aarzoo Sharma) raised a €6mn Series A round led by Upfront Ventures. Key facts about the deal:

  • Value Proposition: SaaS platform for any business user to create and use actionable AI    
  • Founded: September 2016 / HQ: Paris  
  • Stage: Series A / Funding: €6mn (€7.5mn total) / Estimated ARR: €2mn  
  • Investors: Upfront, Net Ventures, SNCF Digital Ventures Fund (managed by HI INOV), Bpifrance

Prevision is on a mission to help businesses unlock the capabilities of AI through their auto-ML platform meant for data scientists, IT teams, and citizen developers(business users who do not have data/ coding expertise). Their platform uses client data and ML models to help users with task automation and forecasting across use cases. They do this via a hybrid business model that incorporates elements of SaaS and Marketplace businesses. The supply-side of the marketplace consists of a community of ‘makers’ (developers) and partners (cloud providers, consultants, and other tech partners).  

🚀 Why you should really care about this deal

  • Prevision’s horizontal approach (the platform covers the entire app-development lifecycle and can cater to use cases across functions) is interesting in light of the rise of Vertical SaaS (Salesforce Ventures).
  • Their modular/ lego-block approach (they have an ‘App Store’ for models backed by a community of makers) likely lends a great deal of flexibility to how users can build and scale projects while cutting costs of development in the long run.  
  • They’re offering ‘White Box AI’ which is key given the growing importance of explainability in AI models (here’s a16z's coverage on the topic).
  • Prevision encourages collaboration across teams while optimizing the experience for IT (with their functionality around migration, monitoring, documentation, and scalability) which probably help boost their acquisition and expansion metrics since IT teams often act as gatekeepers in enterprise sales.  

🤔 What you need to believe in

  • Developing a fluid experience for business users. Prevision's long-term prospects hinge upon a wider-spread adoption in the customer organization. If the learning curve for citizen developers is too steep, the platform is at risk of being marginalized within the data science/ IT team. We'd like to see Prevision implement tools that aid communication between end-user and IT-department to make the set-up of models fluid rather than silo'ed, and provide options for citizen developers to set up models independently with crisp coaching and guidance from the platform.
  • Tackling data processing. Building and maintaining ML models require large amounts of data (check a16z’s take) and the knowledge to pre-process it - something that is comparatively easy to achieve in a developer-driven organization. The main target segment for Prevision’s solution, however, are those companies that either lack those capabilities or organizational set-up: traditional (read: non-tech) SMEs and large enterprises. Prevision will have a real shot at capturing significant market share if they provide value in the entire AI value chain and unlock data-related network effects through their platform.

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