Sukhandeep Nahal
My background
Sukhandeep Nahal is a Product Marketing Manager in AI Research, specializing in translating complex AI advancements into impactful strategies and messaging that resonate with diverse audiences. With a keen understanding of the AI landscape, Sukhandeep works closely with researchers and product teams to bring cutting-edge AI solutions to market, focusing on practical applications for enterprises and developers. She is dedicated to highlighting the transformative potential of AI tools, driving adoption, and empowering customers to harness the full capabilities of AI.
My expertise
AI Research, Developer Productivity
Sukhandeep's latest articles
Blog
We’re excited to share some major upgrades to these models: xLAM now supports multi-turn, natural conversations, enabling more complex, real-world agentic tasks. We’ve also expanded the model portfolio to increase accessibility and deployment flexibility across diverse enterprise environments.
Blog
How Well Do AI Models Understand You? PersonaBench Puts Them to the Test
In this blog, we’ll dive into the challenges of AI personalization, why current systems fall short, and how PersonaBench helps bridge the gap—paving the way for smarter, more reliable AI assistants.
3 authors
Blog
The LLMs behind Agentforce for Developers
Now generally available, Agentforce for Developers represents a significant step in Salesforce's mission to drive innovation and deliver intelligent development tools. Let’s explore how Agentforce, powered by Salesforce AI Research’s large language models, is transforming the way you code.
3 authors
Blog
Actions Speak Louder Than Words: Introducing xLAM, Salesforce’s family of Large Action Models
We've introduced xLAM, our family of in-house Large Action Models, designed for function calling, reasoning, and planning. These models are designed to streamline and simplify the integration of AI into your workflows, reducing the complexity often associated with LLMs.
3 authors
Blog
Salesforce AI Research’s SFR-Embedding, The Top Performing Text-Embedding Model
How can a computer discern the meaning of a sentence?
