Case Study: Handshake scales 15+ LLM use cases in six months with evals from day one on Arize
Key results
The challenge
Handshake, the largest early-career network, set out to build and ship a growing portfolio of LLM-powered product features quickly. Doing so without a consistent evaluation and observability practice risked quality regressions and fragmented tooling as the number of use cases scaled.
The solution
Handshake built an opinionated orchestration layer giving product and engineering a consistent path to production, and integrated Arize AX for tracing, evaluations, and observability. Teams defined success criteria and golden datasets and ran offline testing from the outset, treating evals as part of each use case from day one.
“Arize AX is where all of the traces go - it's been fantastic to collect everything in one place for evals and iteration.”
KGKyle GallatinTechnical Lead Manager of ML Infrastructure, Handshake
The results, in context
Handshake deployed 15+ LLM use cases in under six months, with a typical path from idea to live measured in one to two weeks. Centralizing traces in Arize AX supported evaluation and iteration across the growing set of features.