Untangling 30-call AI interactions consumed developers. Now, support teams use system traces to resolve context issues without engineers.
A communication platform serving understaffed small businesses launched an AI agent to automate scheduling and sales interactions across phone, text, email, and social media.
As interactions grew complex, requiring 20 to 30 LLM calls per conversation, the agent struggled with natural interactions and often produced awkward...
AI lead management and communication platform for local businesses.
Framework and developer platform for building LLM-powered applications.
Podium's Application monitoring is part of this use case:
Related implementations across industries and use cases
Tracking spend for 300M AI agent runs was a black box. Real-time tracing now lets finance pinpoint costs and update pricing within hours.
Manually tuning prompts in secure environments was slow and inaccurate. Now, automated feedback loops let engineers refine AI instantly.
Engineers lacked visibility into a slow AI agent. Rebuilt low-level tracing exposed critical bottlenecks and accelerated LLM responses.
Role-play felt awkward and inauthentic. AI agents now simulate realistic calls, coaching reps on tone and objections at scale.
Language barriers and complex menus slowed fintech adoption. Now, an AI voice assistant handles money transfers conversationally.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Accountants manually scoured mailboxes to assemble 15 subsidiary workbooks. Now, staff-built AI agents pull invoice data for instant review.
A 200% yearly data expansion bottlenecked global operations. Now, AI accelerates coding, drafts recipe cards, and resolves inquiries.
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.
Untangling 30-call AI interactions consumed developers. Now, support teams use system traces to resolve context issues without engineers.
A communication platform serving understaffed small businesses launched an AI agent to automate scheduling and sales interactions across phone, text, email, and social media.
As interactions grew complex, requiring 20 to 30 LLM calls per conversation, the agent struggled with natural interactions and often produced awkward...
AI lead management and communication platform for local businesses.
Framework and developer platform for building LLM-powered applications.
Podium's Application monitoring is part of this use case:
Related implementations across industries and use cases
Tracking spend for 300M AI agent runs was a black box. Real-time tracing now lets finance pinpoint costs and update pricing within hours.
Manually tuning prompts in secure environments was slow and inaccurate. Now, automated feedback loops let engineers refine AI instantly.
Engineers lacked visibility into a slow AI agent. Rebuilt low-level tracing exposed critical bottlenecks and accelerated LLM responses.
Role-play felt awkward and inauthentic. AI agents now simulate realistic calls, coaching reps on tone and objections at scale.
Language barriers and complex menus slowed fintech adoption. Now, an AI voice assistant handles money transfers conversationally.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Accountants manually scoured mailboxes to assemble 15 subsidiary workbooks. Now, staff-built AI agents pull invoice data for instant review.
A 200% yearly data expansion bottlenecked global operations. Now, AI accelerates coding, drafts recipe cards, and resolves inquiries.
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.