Prompt iterations rubber-banded: each engineer's fix overcorrected the last. Evals are now a merge requirement—no eval, no commit.
The first agentic B2B support platform, where AI proactively solves customer problems using company backend tools rather than retrieving knowledge base answers, making response consistency the product that customers' customers experience directly.
Early prompt development was vibes-based, creating a rubber-banding failure mode where engineers over-corrected each other's work and introduced...
“We added a test so that every single AI prompt we check into the codebase needs to have a Braintrust playground ID. If it doesn't have a playground ID attached to it, the test fails.”
AI-powered customer support and ticketing platform for B2B teams.
AI observability and evaluation platform that helps developers build, test, and monitor LLM-powered applications.
Pylon's Prompt evaluation is part of this use case:
Related implementations across industries and use cases
Teams couldn't manually review hundreds of daily AI hotel calls. Audio models now evaluate raw recordings, routing exceptions to humans.
Scattered spreadsheets couldn't catch AI hallucinations. Now, automated LLM judges evaluate every prompt change to block regressions.
Debugging scattered across individual notebooks meant week-long fixes. One shared platform now catches errors before customers do.
Scattered spreadsheets couldn't catch AI hallucinations. Now, automated LLM judges evaluate every prompt change to block regressions.
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.
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Scattered AI tools and manual document searches slowed engineers. Now, a unified AI rapidly retrieves specialized technical answers.
Querying Wikidata required specialized syntax, locking out most AI systems. Vector search now lets LLMs navigate 100M+ entities in plain language.
Prompt iterations rubber-banded: each engineer's fix overcorrected the last. Evals are now a merge requirement—no eval, no commit.
The first agentic B2B support platform, where AI proactively solves customer problems using company backend tools rather than retrieving knowledge base answers, making response consistency the product that customers' customers experience directly.
Early prompt development was vibes-based, creating a rubber-banding failure mode where engineers over-corrected each other's work and introduced...
“We added a test so that every single AI prompt we check into the codebase needs to have a Braintrust playground ID. If it doesn't have a playground ID attached to it, the test fails.”
AI-powered customer support and ticketing platform for B2B teams.
AI observability and evaluation platform that helps developers build, test, and monitor LLM-powered applications.
Pylon's Prompt evaluation is part of this use case:
Related implementations across industries and use cases
Teams couldn't manually review hundreds of daily AI hotel calls. Audio models now evaluate raw recordings, routing exceptions to humans.
Scattered spreadsheets couldn't catch AI hallucinations. Now, automated LLM judges evaluate every prompt change to block regressions.
Debugging scattered across individual notebooks meant week-long fixes. One shared platform now catches errors before customers do.
Scattered spreadsheets couldn't catch AI hallucinations. Now, automated LLM judges evaluate every prompt change to block regressions.
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.
Keyword bots bottlenecked 100 agents supporting millions. Now, AI resolves FAQs, freeing staff to mine chat logs for product feedback.
Scattered AI tools and manual document searches slowed engineers. Now, a unified AI rapidly retrieves specialized technical answers.
Querying Wikidata required specialized syntax, locking out most AI systems. Vector search now lets LLMs navigate 100M+ entities in plain language.