- 00:00: Discussion on transforming customer support with AI and language models - 00:16: Introduction to MyAsk AI founders and their focus on customer support - 01:04: Empowering customer support using AI chatbots and gathering feedback - 02:40: Insights into founders' background and transition to entrepreneurship - 04:44: Entrepreneurial journey from travel startup to AI-focused company - 04:52: Transition from tech interest to starting own business - 05:14: Challenges faced with Pluto travel startup and its closure - 06:01: Evolution towards AI with MK AI development - 09:28: AI Chat GPT customization and usability - 09:30: Focus on reducing support ticket volume - 09:42: Tailored content for users and customer support - 09:53: Differentiation from generic models for accuracy and customization - 14:12: AI Support Features Overview - 14:16: 75% resolution by AI, 25% to human - 14:32: Focus on quality answers through data - 15:10: Insights from conversations for better focus - 18:56: Discussion on key tools for rapid business development - 19:02: Usage of Bubble for no-code development in front and back end - 19:17: Importance of fast development cycles utilizing tools like Bubble and Carbon - 20:02: Integration of Portkey for AI model requests and fallback options - 23:41: Discussion on embedding models and chunking strategies - 24:02: Migration to new open AI embedding models - 24:24: Challenges in migrating embedding models - 24:46: Performance improvement with new OPI models - 28:25: Key points on data processing challenges and AI advancements - 28:37: Suggests focusing on customer use cases to streamline data handling - 29:05: Advises identifying poor-quality data sources to enhance outcomes - 29:29: Emphasizes the need for novel strategies in tackling data processing issues - 33:09: Challenges in deploying AI technologies and navigating distribution channels - 33:34: Differentiating services in a crowded market - 33:42: Demonstrating credibility and business stability - 34:25: Struggle to find repeatable distribution channels for AI products - 37:53: Discussion on managing system prompts and user feedback in LLMS production - 37:57: Challenges in ensuring obedience to system prompts - 38:07: Importance of patience and testing in LLMS production - 39:00: Handling user feedback signals and AI-human handover for improvement - 42:37: Discussion on handling unstructured data and updating content - 42:50: Scraping processes used for web content and PDFs - 42:58: Handling tabular data and structuring it - 44:20: Updating content in Pinecone index for customers
- 00:00: Discussion on transforming customer support with AI and language models
- 00:16: Introduction to MyAsk AI founders and their focus on customer support
- 01:04: Empowering customer support using AI chatbots and gathering feedback
- 02:40: Insights into founders' background and transition to entrepreneurship
- 04:44: Entrepreneurial journey from travel startup to AI-focused company
- 04:52: Transition from tech interest to starting own business
- 05:14: Challenges faced with Pluto travel startup and its closure
- 06:01: Evolution towards AI with MK AI development
- 09:28: AI Chat GPT customization and usability
- 09:30: Focus on reducing support ticket volume
- 09:42: Tailored content for users and customer support
- 09:53: Differentiation from generic models for accuracy and customization
- 14:12: AI Support Features Overview
- 14:16: 75% resolution by AI, 25% to human
- 14:32: Focus on quality answers through data
- 15:10: Insights from conversations for better focus
- 18:56: Discussion on key tools for rapid business development
- 19:02: Usage of Bubble for no-code development in front and back end
- 19:17: Importance of fast development cycles utilizing tools like Bubble and Carbon
- 20:02: Integration of Portkey for AI model requests and fallback options
- 23:41: Discussion on embedding models and chunking strategies
- 24:02: Migration to new open AI embedding models
- 24:24: Challenges in migrating embedding models
- 24:46: Performance improvement with new OPI models
- 28:25: Key points on data processing challenges and AI advancements
- 28:37: Suggests focusing on customer use cases to streamline data handling
- 29:05: Advises identifying poor-quality data sources to enhance outcomes
- 29:29: Emphasizes the need for novel strategies in tackling data processing issues
- 33:09: Challenges in deploying AI technologies and navigating distribution channels
- 33:34: Differentiating services in a crowded market
- 33:42: Demonstrating credibility and business stability
- 34:25: Struggle to find repeatable distribution channels for AI products
- 37:53: Discussion on managing system prompts and user feedback in LLMS production
- 37:57: Challenges in ensuring obedience to system prompts
- 38:07: Importance of patience and testing in LLMS production
- 39:00: Handling user feedback signals and AI-human handover for improvement
- 42:37: Discussion on handling unstructured data and updating content
- 42:50: Scraping processes used for web content and PDFs
- 42:58: Handling tabular data and structuring it
- 44:20: Updating content in Pinecone index for customers