Travellers wanted destination suggestions that matched who they are — adventure, business, nature, family or couples — not a generic list. It also had to stay fast and affordable as usage grew.
What we built
An end-to-end AI pipeline: data prepared with AWS Glue and Spark, BERT classifiers that profile traveller personas, and multi-agent orchestration on AWS Bedrock combining graph and document retrieval so every suggestion is grounded in real data.
The outcome
Personalised recommendations in production, with cost and latency cut at scale through prompt caching, model right-sizing and batch processing.
AI approach
Multi-agent orchestrationGraph + vector RAGPersona classificationPrompt caching & model right-sizing
Tech stack
AWS BedrockAWS GlueApache SparkBERT
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Chat & advertising technology company (consulting engagement)
Problem type
Contextual recommendations & ads
Service area
Recommendation Systems
Used by
Chat-platform users and advertisers
The challenge
An existing chat engine needed to recommend relevant content and ads based on what users were actually talking about.
What we built
An add-on that analyses conversation context with sentiment analysis, named-entity recognition and text categorisation, then recommends content such as movies and books and serves suggestive ads.
Context-based content recommendations and advertising driven by chat history.
