Customer support isn't just a cost center—it’s the heartbeat of your brand. Based on the principles of the book Support Experience, this podcast dives into the strategies that transform standard service into a competitive advantage.
Voice of the Customer is the lifeblood of every technology business. But most companies lose touch with it as they scale, leading to poor customer experiences and high churn.
Some companies, however, have taken a different path. They not only stay in touch with the Voice of the Customer... they amplify it with artificial intelligence and smart automation. Their secret? Building a world-class Support Experience.
Support Experience transforms customer support from a reactive cost center to a proactive profit center. It empowers your people to deliver exceptional support at scale. It turns customer conversations into tangible product improvements, fueling the long-term health of your business.
Krishna Raj Raja shares the blueprint for building a thriving business in the age of AI while making customer support more human than ever, with examples from iconic companies like Apple, Adobe, Google, Salesforce, Snowflake, VMware, and more. This podcast is for CEOs, Chief Customer Officers, Customer Support Leaders, Product Managers, and anyone looking to leverage AI for better customer experiences.
Why "Just Use Claude" is a Strategic Mistake for Customer Support Automation
Reaching for a frontier model like Anthropic's Claude by default feels safe—but it is frequently the wrong choice technologically and financially. Drawing on lessons from the open-source database revolution and the Big Data era, we explain why the technology itself is never going to be the moat. Discover why successful AI strategies require a multi-model orchestration layer instead of a single-engine bet
30 Jun 2026
How Browser Wars Predict LLM Wars And the AI Endgame
Are Large Language Models (LLMs) the ultimate product of the AI revolution, or just the plumbing? In this episode, we explore the striking structural parallels between the 1990s browser wars and today's race for AI dominance. Just as Netscape's web browser transformed from a billion-dollar standalone product into a free, ubiquitous utility, raw artificial intelligence is rapidly commoditizing in front of our eyes.
We break down why the massive innovations produced by frontier AI labs are destined to become open standards—essentially the "HTML and JavaScript of the AI era". We also dive deep into the "Chrome logic" of why tech giants are giving away highly capable open-weight models for free.
Key Takeaways for Customer Support & AI Builders: This episode is tailored specifically for folks building AI tools for customer support, CRM, and service workflows. You will learn:
Why the moat is imaginary: Why you shouldn't build your business around a specific model's capabilities, as breakthroughs quickly become baseline expectations.
Where the value actually pools: How to ensure your AI tool survives the commoditization of the model layer by focusing on proprietary data, solving hard domain-specific problems, and building compounding customer relationships.
The ambient future: How LLMs will eventually disappear into the background of support queues and operating systems, acting as the invisible engine while your service becomes the destination.
Don't fall in love with the engine. Tune in to find out how to position your AI customer support tools on the right side of the glass, building the durable services that will actually win the AI era.
2 Jun 2026
Why AI Bills Rise as Prices Fall: Defeating the Token Tax
In this episode, we explore a strange paradox showing up in enterprise budgets this year: if AI token prices are falling rapidly, why do enterprise AI bills keep climbing?
Even though the cost to run inference on frontier models is projected to drop by more than 90% by 2030, total spend continues to rise. We break down exactly why this happens, focusing on how the shift to agentic workflows means a single task can consume 5 to 30 times more tokens than a standard chatbot. We also expose the hidden "context tax" of traditional RAG pipelines, which forces companies to pay over and over to haul raw, unstructured text—like massive ticket threads and heavy CRM payloads—past the token meter.
Drawing on insights from Krishna Raj Raja, Founder and CEO of SupportLogic, we explain why this isn't a pricing problem you can negotiate away, but an architecture problem you have to design out. Tune in to learn how enterprises can shift from paying for volume to "paying for meaning".
Key Takeaways in this Episode:
The Problem with Hauling: Why the expensive part of enterprise AI isn't the reasoning, but the constant hauling of raw, redundant data on every single API call.
Signal Extraction: How sending a distilled, structured signal instead of a four-thousand-token raw thread drastically cuts input costs.
CRM-Less Architecture: Why keeping the intelligence layer independent from heavy CRM payloads stops the cycle of paying to drag your CRM into every prompt.
Precision-Guided RAG & Pre-computation: How retrieving specific passages and computing insights once to serve multiple assistants (like Claude, ChatGPT, and Gemini) prevents paying the extraction cost multiple times.
Discover how designing an AI system that listens for meaning instead of swallowing raw volume will not only make your customer support more human, but dramatically shrink your bill.
Reach and audience
Public platform figures. Ratings count people who left a rating, not total listeners.
Apple Podcasts (US)
5.0 / 5
1 ratings
Host of Support Experience?
Claim your podcast to manage its listing and keep your show details accurate.
Pod Engine is an independent podcast discovery and analytics service and is not affiliated with or endorsed by this podcast. Artwork and show content belong to their owners. Full legal notice.
Explore this show Podcast research with Pod Engine
