Multi-modal AI powers personalized recovery for a global airline
- 8% Revenue at risk from disruption events
- Millions Unstructured documents processed in database
- 160+ AI/ML engagements delivered in 2025
Turning unanalyzed interaction data into a competitive advantage for customer experience
A top global airline partnered with Teradata to build a multi-modal AI solution that transforms customer emails and chat interactions into actionable intelligence. By vectorizing interaction data and joining it to structured transaction and event records, the airline now understands customer intent at scale, identifies complaints with precision, and delivers personalized recovery plans—especially for high-value customers impacted by disruptions.
- Multi-modal AI integrates text, event, and transaction data for a complete customer picture
- Teradata Bring Your Own Model (BYOM) deploys task-specific language models directly in database at massive scale
- Vector store built on Teradata enables both semantic and hybrid K-means search
- Sentiment analysis surfaces complaints and their severity in real time
Vast stores of customer interaction data sat untapped—and disruption kept costing the business
Like many large airlines, this carrier accumulated enormous volumes of customer emails and chat transcripts that were never systematically analyzed. Customer intent, sentiment, and complaint trends remained invisible, making it nearly impossible to respond proactively or personalize resolution. Meanwhile, weather and operational disruptions continued to threaten up to 8% of industry revenues, and recovery efforts lacked the context needed to prioritize effectively.
- Customer emails and chats not routinely analyzed for intent or sentiment
- No unified view linking interaction data to transaction and event history
- Disruption recovery was reactive rather than personalized or context-aware
- High-value customer impacts went unidentified and unaddressed
Teradata's BYOM and vector store capabilities unlock multi-modal intelligence at airline scale
Teradata's BYOM technology enabled the airline to deploy smaller, task-specific language models directly in database, using Teradata as a massively parallel processing harness across millions of documents. The resulting embeddings populate a vector store built on Teradata, supporting both semantic similarity search and hybrid clustering via K-means. This architecture enables the airline to join unstructured interaction data back to structured customer records—creating a unified foundation for personalized, context-aware engagement.
- BYOM deploys task-specific LLMs indatabase for scalable parallel document processing
- Embeddings stored in a Teradata-native vector store for semantic and hybrid retrieval
- K-means clustering groups interactions by customer mission and intent
- Structured joins connect interaction insights to transaction and event data
Customers feel heard—and high-value relationships are protected when it matters most
By understanding customer intent and sentiment at scale, the airline can now respond to disruptions with speed and empathy. Personalized resolution plans are developed for affected customers—with priority given to high-value travelers—transforming what was once damage control into a loyalty-building moment. The result is a measurable shift from reactive firefighting to proactive, intelligent customer care.
- Faster disruption recovery through AI-prioritized, context-aware resolution workflows
- Personalized recovery plans for high-value customers based on interaction and transaction history
- Proactive complaint detection and sentiment analysis enable intervention before loyalty erodes
연결하자
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