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Jun 15, 2026
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readme file #137

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ionelpanaitescu Jun 15, 2026
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2 changes: 1 addition & 1 deletion Live-AI-Hub/README.md
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Expand Up @@ -15,7 +15,7 @@ The initial demo assets focus on two example experiences:

- **Retail Hub / Fashion Advisor Team Agent** - demonstrates a multi-agent retail experience where specialized agents work together to understand the customer, gather customer profile and analytics data, retrieve weather context, search back-office FAQs and terms, research fashion articles and product catalog information, generate grounded fashion recommendations, and support add-to-cart actions.

- **Ticket AI Hub** - demonstrates an intelligent service and ticketing hub that connects to existing ticketing systems, customer channels, customer data, and enterprise knowledge bases. It is designed to improve customer satisfaction, increase resolution accuracy, reduce response time, support intelligent automation, provide knowledge-grounded answers, improve quality and accuracy, support smart escalation, and deliver operational insights and analytics.
- **Ticket AI Hub** - (code assets coming soon) - demonstrates an intelligent service and ticketing hub that connects to existing ticketing systems, customer channels, customer data, and enterprise knowledge bases. It is designed to improve customer satisfaction, increase resolution accuracy, reduce response time, support intelligent automation, provide knowledge-grounded answers, improve quality and accuracy, support smart escalation, and deliver operational insights and analytics.


More assets and reusable components will be added as the Live AI Hub evolves.
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4 changes: 3 additions & 1 deletion Live-AI-Hub/reusable-assets/README.md
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Expand Up @@ -4,4 +4,6 @@
## <a name="Reusable Assets"></a>Overview


- Fashion-Retail-Hub - Deploy Fashion-Retail-Hub demo showcases how a fashion retailer can evolve into an AI-driven business by connecting live enterprise data across multiple platforms and turning it into personalized, customer-facing experiences. The solution brings together operational, analytical, and external data to power intelligent conversations, better recommendations, and more effective digital engagement.
- **Retail Hub / Fashion Advisor Team Agent** - demonstrates a multi-agent retail experience where specialized agents work together to understand the customer, gather customer profile and analytics data, retrieve weather context, search back-office FAQs and terms, research fashion articles and product catalog information, generate grounded fashion recommendations, and support add-to-cart actions.

- **Ticket AI Hub** - (code assets coming soon) - demonstrates an intelligent service and ticketing hub that connects to existing ticketing systems, customer channels, customer data, and enterprise knowledge bases. It is designed to improve customer satisfaction, increase resolution accuracy, reduce response time, support intelligent automation, provide knowledge-grounded answers, improve quality and accuracy, support smart escalation, and deliver operational insights and analytics.
2 changes: 1 addition & 1 deletion cloud-foundation/solutions/README.md
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Expand Up @@ -59,6 +59,6 @@ Other assets have been moved to the `Live-ai-hub` folder.

The folder follows a pattern that brings AI to your data where it already lives, enabling the use of live, federated enterprise information across SaaS applications, databases, data lakes, catalogs, and content repositories. This allows users and agents to talk to data and reason across data with the right governance in place.

This approach matters because most organizations are not blocked by AI models themselves. They are blocked by disconnected data, stale copies, and the complexity of moving and reshaping information. When AI runs on live data in place, it can provide more timely answers, better context, and higher trust, without requiring a long data platform transformation first.
This approach matters because many organizations have disconnected data, stale copies, and fight the complexity of moving and reshaping information. When AI runs on live data in place, it can provide more timely answers, better context, and higher trust, without requiring a data platform transformation first.

The benefits are tangible: faster time-to-value through quick wins that can scale, better outcomes because AI is grounded in current enterprise context, lower cost and risk by reducing replication and ETL sprawl, and production readiness with the controls enterprises need, including security, resiliency, observability, and scalability.