Voker is the Agent Analytics Platform for AI product teams. It gives you the usage behavior and agent performance insights you need to monitor and optimize your production agents at scale. Install the lightweight, provider agnostic SDK and Voker handles the rest: automatic intent, correction and resolution detection on your user to agent interactions, conversation reconstructions, queryable timelines, agent performance tracking so you can build the best agents possible.
I’m Tyler - CoFounder of Voker, and I’m so tired of being disappointed by AI hype claims. I bet you are too.
I studied physics in college, and worked in data science, ML, and analytics until founding Voker. I’m a skeptical person by nature (I think it's the scientist in me) and my gut reaction to any technology hype is to be cautiously optimistic until I see things proven out in data.
I felt this way about LLMs when they first hit mainstream. I knew they had real potential applications, but was also worried about the lofty marketing buzz they were getting.
AI as an industry has written checks that individual builders are left to cash. Promising full automation, PhD-level intelligence, and perfect results. As someone who's skeptical of that narrative, I still believe agents can genuinely deliver, but only if teams are rigorous about measuring performance in production. Every website or product has Amplitude or PostHog for click and pageview analytics; a standard way to understand who's using it and how. Agents have no equivalent, so we built Voker.
We are the Agent Analytics Platform where you can:
- Monitor your agents - Measure their performance - See what users are asking - Know for certain agents are delivering for your users - Optimize based on real data
You install our SDK, and Voker collects your agent conversation data, automatically detecting:
- User intents (Book me a hotel in Vegas for next Saturday with a poolside view) - Corrections (No, that room doesn’t have a poolside view!! TRY AGAIN) - Agent resolutions (Tool Result: Room Booked... Success!)
These automated annotations are the foundation for building a holistic view of agent performance and user behavior in one analytics platform.
We asked 100+ AI founders, product managers, and agent engineers how they monitor their agents in production and the answer was resounding: by combing through individual traces (with the occasional evals sprinkled in). They all reported that they depend on customer complaints to tell them when agents are messing up. We feel strongly that there is a third leg of the agent monitoring stool missing - Agent Analytics.
You shouldn’t have to wait for users to complain to learn that a recent prompt change is breaking your hotel booking agent, or that the AI finance advisor you built is calling the wrong tool to look up realtime stock prices.
Turns out the antidote to AI hype is simple: measure your agents diligently, then iterate until you get it right.
Your users deserve better AI experiences (we all do)!
“The Agent Analytics Platform for AI Product Teams”
Voker launched on Product Hunt on May 19th, 2026 and earned 142 upvotes and 38 comments, placing #10 on the daily leaderboard. Voker is the Agent Analytics Platform for AI product teams. It gives you the usage behavior and agent performance insights you need to monitor and optimize your production agents at scale. Install the lightweight, provider agnostic SDK and Voker handles the rest: automatic intent, correction and resolution detection on your user to agent interactions, conversation reconstructions, queryable timelines, agent performance tracking so you can build the best agents possible.
On the analytics side, Voker competes within Analytics, Developer Tools and Artificial Intelligence — topics that collectively have 1.2M followers on Product Hunt. The dashboard above tracks how Voker performed against the three products that launched closest to it on the same day.
Who hunted Voker?
Voker was hunted by Garry Tan. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Voker including community comment highlights and product details, visit the product overview.
Garry Tan
I’m Tyler - CoFounder of Voker, and I’m so tired of being disappointed by AI hype claims. I bet you are too.
I studied physics in college, and worked in data science, ML, and analytics until founding Voker. I’m a skeptical person by nature (I think it's the scientist in me) and my gut reaction to any technology hype is to be cautiously optimistic until I see things proven out in data.
I felt this way about LLMs when they first hit mainstream. I knew they had real potential applications, but was also worried about the lofty marketing buzz they were getting.
AI as an industry has written checks that individual builders are left to cash. Promising full automation, PhD-level intelligence, and perfect results. As someone who's skeptical of that narrative, I still believe agents can genuinely deliver, but only if teams are rigorous about measuring performance in production. Every website or product has Amplitude or PostHog for click and pageview analytics; a standard way to understand who's using it and how. Agents have no equivalent, so we built Voker.
We are the Agent Analytics Platform where you can:
- Monitor your agents
- Measure their performance
- See what users are asking
- Know for certain agents are delivering for your users
- Optimize based on real data
You install our SDK, and Voker collects your agent conversation data, automatically detecting:
- User intents (Book me a hotel in Vegas for next Saturday with a poolside view)
- Corrections (No, that room doesn’t have a poolside view!! TRY AGAIN)
- Agent resolutions (Tool Result: Room Booked... Success!)
These automated annotations are the foundation for building a holistic view of agent performance and user behavior in one analytics platform.
We asked 100+ AI founders, product managers, and agent engineers how they monitor their agents in production and the answer was resounding: by combing through individual traces (with the occasional evals sprinkled in). They all reported that they depend on customer complaints to tell them when agents are messing up. We feel strongly that there is a third leg of the agent monitoring stool missing - Agent Analytics.
You shouldn’t have to wait for users to complain to learn that a recent prompt change is breaking your hotel booking agent, or that the AI finance advisor you built is calling the wrong tool to look up realtime stock prices.
Turns out the antidote to AI hype is simple: measure your agents diligently, then iterate until you get it right.
Your users deserve better AI experiences (we all do)!
Install the Voker SDK on our free tier (up to 2,000 events/mo), and start building better agents today:
https://voker.ai/