Acxiom uses LangSmith to scale AI-driven audience segmentation with LLM observability
Acxiom needed to build a scalable generative AI application for dynamic audience segmentation from natural language input, but their initial lightweight logging solution could not scale as the user base grew, and complex multi-step LLM reasoning produced hallucinated or incorrect results.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Natural language audience request
A user submits a natural language request to identify an audience segment from Acxiom's data catalog.
By adopting LangSmith, Acxiom achieved streamlined debugging of complex LLM chains, more accurate and dynamic audience segment creation, scalable growth without reengineering the observability layer, and optimized token usage through better cost visibility.
What failed first
Acxiom's initial prompt input/output logging system was too lightweight for scale, their RAG-based agentic architecture suffered from cascading debugging failures, and hallucinations arose from LLM reasoning gaps.