Weights & Biases on how Neuralift builds trust in AI-powered segmentation
Weights & Biases published a customer story on how Neuralift uses W&B Weave to evaluate, monitor, and de-hallucinate the LLM layer underneath our segmentation platform.
Weights & Biases just published a customer story on how Neuralift uses W&B Weave to evaluate and monitor the LLM layer underneath our segmentation platform — the bit that turns deep-learning clusters into the natural-language summaries, personas, and play recommendations marketers actually use.
▶ Weights & Biases customer story · 04:32
The piece is built around our co-founder and CDO Mike Maloney, who walks through how the team got to a point where every LLM call inside the platform is logged, traced, and benchmarked — and why that matters when you’re shipping AI-generated marketing strategy to enterprise customers.
W&B Weave is featured heavily in how we aim to continuously improve and build a high-quality applied AI product. — Mike Maloney, Co-Founder and CDO
A few specifics from the case study:
- Every LLM input, output, and trace is captured automatically — so the team can answer questions about model performance, token usage, and latency without instrumenting from scratch.
- Contradiction rates and hallucinations across segment summaries are now caught and quantified, instead of being spotted by sharp-eyed customers.
- Hundreds of segmentation models are tracked through W&B Models alongside the Weave traces, so improvements compound rather than overwriting each other.
Trust in the AI layer of marketing tooling is a real thing customers ask about — usually somewhere between data security and procurement. We’ve leaned on W&B Weave specifically because that question deserves a real answer rather than a marketing one.