Register here for AWS re:Invent 2024, Dec 2-6, Las Vegas, NV

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Richard Borstein of RingCentral and Richard Sonnenblick of Planview discuss how AI-driven innovations enhance customer and employee experiences, and unlock organizational growth through cutting-edge tools and strategies.

Topics Include:

• Importance of integrated communication tools for businesses.
• Challenges caused by disconnected communication platforms.
• Role of data in enhancing business operations.
• How RingCentral addresses communication and data integration issues.
• Benefits of real-time conversational intelligence in organizations.
• Leveraging AI to transform communication into actionable insights.
• Unlocking customer and employee voices through AI.
• How AI identifies patterns in customer interactions.
• Overview of RingSense for Sales AI tool.
• Real-world success story with RingSense for Sales.
• Streamlining customer interactions using AI-powered analysis.
• Enhancing employee productivity with AI-driven tools.
• AI solutions for faster, accurate information searches.
• Overview of RingCentral's Ring CX contact center solution.
• Improving customer satisfaction through AI-powered call analysis.
• Case study: Success with Ring CX at Worldwide Express.
• Features and benefits of RingCentral Events platform.
• Integrating event tech with existing customer workflows.
• Personalizing events with branding and engagement tools.
• PlanView’s use of AWS to drive innovation.
• Solving governance challenges with PlanView’s solutions.
• How generative AI accelerates productivity and decision-making.
• Making every user a power user with AI.
• Practical examples of generative AI in project management.
• Unlocking growth with next-gen AI-driven business tools.

Participants:

• Richard Bornstein - Chief Business Development Officer, RingCentral
• Richard Sonnenblick Ph.D – Chief Data Scientist, Planview

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Register here for AWS re:Invent 2024, Dec 2-6, Las Vegas, NV

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Benjamin Flast, Director, Product Management at MongoDB discusses vector search capabilities, integration with AWS Bedrock, and its transformative role in enabling scalable, efficient, and AI-powered solutions.

Topics Include:

• Introduction to MongoDB's vector search and AWS Bedrock
• Core concepts of vectors and embeddings explained
• High-dimensional space and vector similarity overview
• Embedding model use in vector creation
• Importance of distance functions in vector relations
• Vector search uses k-nearest neighbor algorithm
• Euclidean, Cosine, and Dot Product similarity functions
• Applications for different similarity functions discussed
• Large language models and vector search explained
• Introduction to retrieval-augmented generation (RAG)
• Combining external data with LLMs in RAG
• MongoDB's document model for flexible data storage
• MongoDB Atlas platform capabilities overview
• Unified interface for MongoDB document model
• Approximate nearest neighbor search for efficiency
• Vector indexing in MongoDB for fast querying
• Search nodes for scalable vector search processing
• MongoDB AI integrations with third-party libraries
• Semantic caching for efficient response retrieval
• MongoDB's private link support on AWS Bedrock
• Future potential of vector search and RAG applications
• Example use case: Metaphor Data's data catalog
• Example use case: Okta's conversational interface
• Example use case: Delivery Hero product recommendations
• Final takeaways on MongoDB Atlas vector search

Participants:

• Benjamin Flast - Director, Product Management, MongoDB

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Register here for AWS re:Invent 2024, Dec 2-6, Las Vegas, NV

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David Gildea of Druva shares their approach to building cost-effective, fast generative AI applications, focusing on cybersecurity, data protection, and the innovative use of LLMs for simplified, natural language threat detection.

Topics Include:

• Introduction by Dave Gildea, VP of Product at Druva.
• Focus on building generative AI applications.
• Emphasis on cost and speed optimization.
• Mention of Amazon's Matt Wood keynote.
• AI experience with kids using "Party Rock."
• Prediction: GenAI as future workplace standard.
• Overview of Druva's data security platform.
• Three key Druva components: protection, response, and compliance.
• Druva's autonomous, rapid, and guaranteed recovery.
• Benefits of Druva’s 100% SaaS platform.
• Handling 7 billion backups annually.
• Managing 450 petabytes across 20 global regions.
• Druva’s high NPS score of 89.
• Introduction to Dru Investigate AI platform.
• Generative AI for cybersecurity and threat analysis.
• Support for backup and security admins.
• Simplified cybersecurity threat detection.
• AI-based natural language query interpretation.
• Historical analogy with Charles Babbage’s steam engine.
• "Fail upwards" model for LLM optimization.
• Using small models first, escalating to larger ones.
• API security and customer data protection.
• Amazon Bedrock and security guardrails.
• Testing LLMs with Amazon’s new prompt evaluation tool.
• Speculation on $100 billion future model costs.
• Session wrap up

Participants:

· David Gildea - VP Product Generative AI, GM of CloudRanger, Druva

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/