AI is here. Finally. After so many years of false starts and AI winters. This time is different. It certainly feels like it. And when AI becomes prevalent, what happens to this idea of “knowledge”? A discussion for another day, for today we’re talking about a practical need for tooling when building with AI.
The most important thing about building AI is the training data, everyone knows that. The most important thing about applying these AI models is data also, but in this case, we’re talking about the relevant user data needed by the AI to understand the context of a request.
So introducing Ontextual.
Ontextual is a new friend to AI developers, particularly folks applying various APIs to infuse their applications with artificial intelligence.
Want to mix and match AI APIs and bots? Want to create long-term memory and understanding of the user’s needs so that AI can be more helpful? Want to create long-running conversations for ongoing tasks and processes? Or manage long-running workflows with other users and AI agents? Ontextual helps gather and maintain context, while also providing a single interface to access various types of AI APIs in a semantically functional manner.
Where do we start with this?
The first use case is around personal shopping.
A new AI bot that handles shoppers, and acts like their personal shopper assistant. It has conversations, and over time understands the preferences of the user, and is able to get smarter and smarter, including the ability to make certain purchase decisions, as well as execute them.
Here we go!
