Inside the platform enterprises use to build AI agents with Rasmus Hauch, CTO at Boost.ai
Rasmus Hauch, CTO of Boost.ai, opens up the platform layer beneath enterprise AI agents: testing, guardrails, liability and voice at scale.
Are LLM chatbots ready for prime time? Or is intent-based NLU still king?
Everyone’s talking about LLM chatbots, but few are asking the hard questions. Are they actually ready for the pressure of real-world use?
AI in financial services: lessons from DNB and MSUFCU
Discover how leading financial services organisations are finding success with AI.
AI vs BPO: Foundever’s transformative journey with Guillaume Laporte
In this episode, we talk to Guillaume Laporte, Chief AI Officer at Foundever – a $4 billion BPO giant with 150,000 employees worldwide.
Designing for generative AI: The UX evolution at Prudential with Alex Shin
In this episode, we’re joined by Alex Shin, Senior Product Designer at Prudential Financial, to talk about how UX is evolving in the age of LLMs.
AI is smart enough – so are you ready?
Instead of needing ever-smarter LLMs for most business processes, the real impact now comes from how we use them.
The mad genius of using LLMs as classifiers with Katherine Munro, Swisscom
In this episode, Kane Simms is joined by Katherine Munro, Conversational AI Engineer at Swisscom, for a deep dive into what might sound like an odd pairing: using LLMs to classify customer intents.
Building AI maturity at Pandora with Sonia Ingram
In this episode, Kane is joined by Sonia Ingram, Global Director of AI at Pandora, to explore what it really takes to develop AI maturity in a global business.
AI beyond the hype: what actually works for enterprise with Andrei Papancea, NLX
In this episode, we chat with Andrei Papancea, CEO and Co-Founder of NLX, to cut through the noise and get real about where AI in the enterprise is delivering value, and where it’s not.
10 differences between small language models (SLM) and large language models (LLMs) for enterprise AI
With all this talk about large language models, you’d be forgiven for thinking that they’re going to solve the world’s problems. All we need is more data and more computing power!