Engineers relied on manual QA and user emails to catch chatbot errors. Automated tracing now flags poor thread-level interactions.
A user assistance platform provides an embedded widget that serves millions of end users across thousands of software support teams by capturing user intent and delivering personalized in-product help.
The engineering team needed to build an accurate retrieval system capable of adapting to the diverse content providers and knowledge bases of its...
“I was surprised how straightforward it was to set up the traces just with the decorators in LangSmith. It was super easy to get started.”
AI-powered user assistance platform for product and customer support teams.
Framework and developer platform for building LLM-powered applications.
CommandBar's Performance monitoring is part of this use case:
Related implementations across industries and use cases
Untangling 30-call AI interactions consumed developers. Now, support teams use system traces to resolve context issues without engineers.
Manually tuning prompts in secure environments was slow and inaccurate. Now, automated feedback loops let engineers refine AI instantly.
Debugging scattered across individual notebooks meant week-long fixes. One shared platform now catches errors before customers do.
Manually tuning prompts in secure environments was slow and inaccurate. Now, automated feedback loops let engineers refine AI instantly.
Tracking spend for 300M AI agent runs was a black box. Real-time tracing now lets finance pinpoint costs and update pricing within hours.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Surging calls caused long holds and overtime. A 24/7 AI voice agent handles routine payroll, freeing 700 HR partners for advisory work.
A 200% yearly data expansion bottlenecked global operations. Now, AI accelerates coding, drafts recipe cards, and resolves inquiries.
Legacy keyword search failed on typos and vague queries. Now, semantic AI interprets natural language and images to find exact items.
Engineers relied on manual QA and user emails to catch chatbot errors. Automated tracing now flags poor thread-level interactions.
A user assistance platform provides an embedded widget that serves millions of end users across thousands of software support teams by capturing user intent and delivering personalized in-product help.
The engineering team needed to build an accurate retrieval system capable of adapting to the diverse content providers and knowledge bases of its...
“I was surprised how straightforward it was to set up the traces just with the decorators in LangSmith. It was super easy to get started.”
AI-powered user assistance platform for product and customer support teams.
Framework and developer platform for building LLM-powered applications.
CommandBar's Performance monitoring is part of this use case:
Related implementations across industries and use cases
Untangling 30-call AI interactions consumed developers. Now, support teams use system traces to resolve context issues without engineers.
Manually tuning prompts in secure environments was slow and inaccurate. Now, automated feedback loops let engineers refine AI instantly.
Debugging scattered across individual notebooks meant week-long fixes. One shared platform now catches errors before customers do.
Manually tuning prompts in secure environments was slow and inaccurate. Now, automated feedback loops let engineers refine AI instantly.
Tracking spend for 300M AI agent runs was a black box. Real-time tracing now lets finance pinpoint costs and update pricing within hours.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Surging calls caused long holds and overtime. A 24/7 AI voice agent handles routine payroll, freeing 700 HR partners for advisory work.
A 200% yearly data expansion bottlenecked global operations. Now, AI accelerates coding, drafts recipe cards, and resolves inquiries.
Legacy keyword search failed on typos and vague queries. Now, semantic AI interprets natural language and images to find exact items.