Today, I'm thrilled to disclose Voker’s $2.2M in pre-seed funding from @ycombinator and @FundersClub. I’ve watched product teams ship AI agents with zero visibility into whether they're actually working. Logs don't tell you if your agent is helping users. You can't review 10,000 conversations manually. And metrics don't appear on their own. That's why we built Voker.

May 19, 2026 · 4:05 PM UTC

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Voker is the agent analytics platform for AI product teams. It’s simple: just install our llm stack-agnostic SDK and Voker tracks your agent performance and usage data, alerts you when things drift, and helps every stakeholder understand how your agent is really performing. We're at the beginning of understanding how to build AI products that actually work at scale. @voker_ai is how teams get there.
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Huge thanks to our investors, employees, partners, and customers. If you're building with AI agents and want more signal, less noise, try Voker for free up to 2000 events/mo: voker.ai and check us out on Product Hunt today! producthunt.com/products/vok…
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"You can't review 10,000 conversations manually" is the key line There's a missing layer between logs and metrics: reviewable evidence Raw traces are too much dashboards are too late What teams need is a way to turn a few conversations into cases: what broke, why it broke, and whether the fix belongs in prompt, retrieval, policy, or product
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Or whether you dont need to fix at all! Maybe its not a common pattern, just an edge case that isnt even worth an eval
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This is exactly the gap. Agent teams do not just need logs. They need a way to tell whether the agent made the right operating decision in context.
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Context is key! Monitor and optimize your agents or fall behind!
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