We realized that to scale our models, we needed to transition from pure research to a business model that could sustain the investment required for large AI models.

The company has developed custom AI models to achieve high accuracy in specific use cases, enhancing overall service delivery.

As AI becomes more integrated into various applications, we need to optimize for specific internal use cases, which may lead us to use other models if they better serve those needs.

The AI tools we build need to continuously deliver additional value as the baseline for foundation models improves.

As an application layer company, we must adapt to the rapidly changing landscape of AI model providers and ensure we are testing and integrating the best models available.

We quickly improved our training infrastructure and trained our own models, which allowed us to pull ahead in terms of quality and capabilities compared to GitHub Copilot.

As we gather more data and build a larger platform, we can create more granular and personalized evaluations of AI models.

Salesforce builds many models and has a phenomenal AI team, but we also leverage other models and regularly invest in various model companies.

The current AI models are impressive on their own, but they need to be integrated into applications that enhance user engagement and retention.