Modern automated testing environments generate vast amounts of test results, making failure analysis increasingly complex as both the number of tests and failures grow. This presentation introduces an AI-driven approach to failure aggregation, leveraging text embeddings and semantic similarity to efficiently group and analyze unique failures. The workflow integrates open-source, pre-trained models for text embedding (such as Sentence Transformers) and vector similarity search using PostgreSQL with pgvector, enabling scalable and low-barrier adoption.