Watch this session to see how AWS and Snowplow work together to give AI agents real-time awareness of what a customer is doing, not just what they type into a chat box.
Len Gomez (Partner Solutions Architect, AWS) and Yali Sassoon (CTO and Co-founder, Snowplow) cover:
- An overview of the AWS generative AI stack, from Trainium and Inferentia chips through Amazon SageMaker and Amazon Bedrock
- How Snowplow streams behavioral data from web, mobile, and other digital touchpoints into your AWS environment in real time
- Three ways companies are putting this data to work with AI agents: identifying bot and agent traffic, giving customer-facing agents real-time context, and managing customer lifecycle and retention
- A demo of a travel site where a Snowplow Signals profile updates as a visitor browses, and an Amazon Bedrock agent uses that profile to personalize its recommendations and step in proactively when a visitor seems undecided
- How the pieces connect: a Snowplow real-time pipeline, a profile API, and a Bedrock agent, plus how the same profile store brings in historical data from your warehouse or lakehouse
See in action:
Amazon Bedrock
AWS's managed service for building, deploying, and scaling AI agents, with built-in security, observability, and tool orchestration.
Snowplow Signals
Snowplow's real-time customer intelligence layer, which computes attributes and triggers interventions from behavioral data so agents can act on what a customer is doing as it happens.
Interested in learning more? Try Snowplow for free for 14 days: https://snowplow.io/get-started/snowplow-free-trial