Teams couldn't manually review hundreds of daily AI hotel calls. Audio models now evaluate raw recordings, routing exceptions to humans.
An all-in-one travel and expense management platform with an AI voice agent making hundreds of calls daily to hotels worldwide to confirm bookings and deliver payment card details on behalf of travelers.
As call volume scaled to hundreds per day, the development and ops teams couldn't manually listen to each conversation to assess quality or...
“When we started to build this AI agent very quickly, what started happening was that hundreds of calls started happening on behalf of our travelers.”
All-in-one travel and expense management platform that combines corporate travel booking, expense tracking, and corporate card services.
AI observability and evaluation platform that helps developers build, test, and monitor LLM-powered applications.
Navan's Voice call evaluation is part of this use case:
Related implementations across industries and use cases
Prompt iterations rubber-banded: each engineer's fix overcorrected the last. Evals are now a merge requirement—no eval, no commit.
Scattered spreadsheets couldn't catch AI hallucinations. Now, automated LLM judges evaluate every prompt change to block regressions.
Reviewers struggled to predict how code ripples through the system. AI now flags cross-service risks that cause outages.
Mining 5 coachable minutes from a 45-min call kept coaching rare. AI summaries in 20 seconds lifted scorecards from 23 to 218 in a quarter.
Student insights were trapped in unreviewed audio. AI securely evaluates every call to power instant feedback and proactive coaching.
Surging calls caused long holds and overtime. A 24/7 AI voice agent handles routine payroll, freeing 700 HR partners for advisory work.
Keyword bots bottlenecked 100 agents supporting millions. Now, AI resolves FAQs, freeing staff to mine chat logs for product feedback.
Querying Wikidata required specialized syntax, locking out most AI systems. Vector search now lets LLMs navigate 100M+ entities in plain language.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.
Teams couldn't manually review hundreds of daily AI hotel calls. Audio models now evaluate raw recordings, routing exceptions to humans.
An all-in-one travel and expense management platform with an AI voice agent making hundreds of calls daily to hotels worldwide to confirm bookings and deliver payment card details on behalf of travelers.
As call volume scaled to hundreds per day, the development and ops teams couldn't manually listen to each conversation to assess quality or...
“When we started to build this AI agent very quickly, what started happening was that hundreds of calls started happening on behalf of our travelers.”
All-in-one travel and expense management platform that combines corporate travel booking, expense tracking, and corporate card services.
AI observability and evaluation platform that helps developers build, test, and monitor LLM-powered applications.
Navan's Voice call evaluation is part of this use case:
Related implementations across industries and use cases
Prompt iterations rubber-banded: each engineer's fix overcorrected the last. Evals are now a merge requirement—no eval, no commit.
Scattered spreadsheets couldn't catch AI hallucinations. Now, automated LLM judges evaluate every prompt change to block regressions.
Reviewers struggled to predict how code ripples through the system. AI now flags cross-service risks that cause outages.
Mining 5 coachable minutes from a 45-min call kept coaching rare. AI summaries in 20 seconds lifted scorecards from 23 to 218 in a quarter.
Student insights were trapped in unreviewed audio. AI securely evaluates every call to power instant feedback and proactive coaching.
Surging calls caused long holds and overtime. A 24/7 AI voice agent handles routine payroll, freeing 700 HR partners for advisory work.
Keyword bots bottlenecked 100 agents supporting millions. Now, AI resolves FAQs, freeing staff to mine chat logs for product feedback.
Querying Wikidata required specialized syntax, locking out most AI systems. Vector search now lets LLMs navigate 100M+ entities in plain language.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.