Learn how DataStax transformed customer feedback into a hybrid search solution that powers Fortune 500 companies through their partnership with AWS.

Topics Include:

• AWS and DataStax discuss how quality data powers AI workloads and applications.
• DataStax built on Apache Cassandra powers Starbucks, Netflix, and Uber at scale.
• Their TIL app collects outside-in customer feedback to drive product development decisions.
• Hybrid search and BM25 kept trending in customer requests for several months.
• Customers wanted to go beyond pure vector search, not specifically BM25 itself.
• Research showed hybrid search improves accuracy up to 40% over single methods.
• ML-based re-rankers substantially outperform score-based ones despite added latency and cost.
• DataStax repositioned their product as a knowledge layer above the data layer.
• Developer-first design prioritizes simple interfaces and eliminates manual data modeling headaches.
• Hybrid search API uses simple dollar-sign parameters and integrates with Langflow automatically.
• AWS PrivateLink ensures security while Graviton processors boost efficiency and tenant density.
• Graviton reduced total platform operating costs by 20-30% with higher throughput.

Participants:

• Alejandro Cantarero – Field CTO, AI, DataStax
• Ruskin Dantra - Senior ISV Solution Architect, AWS, Amazon Web Services

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

Qlik's Field CTO for Generative AI Ryan Welsh reveals why 95% of enterprise AI projects fail and shares the three proven strategies the successful 5% use to deliver real business value from their AI investments.

Topics Include:

• Qlik's Field CTO reveals why 95% of AI projects fail despite massive investments
• MIT research shows shocking failure rates, but 5% are achieving real business value
• First major pitfall: Bad data foundations doom even the most sophisticated AI models
• Second problem: Companies use generative AI when predictive models would work better
• Third issue: Unnecessary complexity - AI projects disconnected from business outcomes
• Success secret #1: Ground AI in trusted enterprise data and user context
• Some LLMs struggle at specific tasks like claims processing despite passing medical exams
• Success secret #2: Let AI learn from users while keeping data governance intact
• Success secret #3: Embed AI directly into existing workflows like Salesforce
• Agentic AI shifts from reactive Q&A to proactive systems that execute across platforms
• Case study: Lintek reduced churn 10% and saved millions using these principles
• Your AI choices today will lock in your trajectory for years to come

Participants:

• Ryan Welsh – Field CTO – Generative AI, Qlik

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

Rapid7's Vice President of Data and AI Laura Ellis shares how they built an AI-first cybersecurity platform by investing in AI platform AND data infrastructure simultaneously.

Topics Include:

• Rapid7 processes massive cybersecurity data across exposure management, threat detection, and managed SOC.
• 84% of security analysts want to quit due to data overload burnout.
• Challenge: investing in AI platform AND data infrastructure simultaneously, not sequentially.
• Built security data lake with AWS, unified IDs, and standardized schemas across products.
• Used traditional machine learning for 10 years before generative AI emerged.
• Generative AI raised questions about business impact; agentic AI enables full automation.
• Chose AWS for scale, model marketplace flexibility, and true partnership on capacity.
• Co-development incubator with SOC team proved critical: equal responsibility, full-time collaboration.
• Launched alert triage automation, SOC assistant chatbot, and incident report generation tools.
• Built AI platform with guardrails after pen testers generated cookie recipes costing money.
• One agentic feature initially cost-estimated at $140 million before optimization and guidance.
• Future: more AI features, granular customer configuration, and bring-your-own-model capabilities.

Participants:

• Laura Ellis – Vice President, Data & AI, Software Engineering, Rapid7

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