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Launch HN: Roark (YC W25) – Taking the pain out of voice AI testing

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Re: Launch HN: Roark (YC W25) – Taking the pain out of voice AI testing

#22

Why is it called Roark?

Great question! Roark is named after Ted Roark from Chuck — both of us (Daniel and I) watched Chuck a lot growing up, and the name just stuck with us.

No deep meaning, just something we liked and thought sounded cool!

Re: Launch HN: Roark (YC W25) – Taking the pain out of voice AI testing

#23
post #12

I noticed this on your website regarding transcription -- "More accurate than Deepgram, supporting 50+ languages with a word error rate of just 8.6%." Can you explain how this helps me? At the end of the day you are not my transcriber, wouldn't I want to test using transcriptions produced by the transcriber that I'm actually using in production?

We help capture discrepancies between your transcription model and errors in order to effectively calculate a Word Error Rate (WER) as part of your evaluation process. Post-call transcription tends to be more accurate, and we’ve seen teams manually do this by hiring humans to label a dataset and test against it for WER calculations.

By providing a more accurate baseline, Roark helps teams quantify how well their production transcriptions match reality and flag cases where the model is introducing errors that could impact downstream agent performance. That way, you’re not just testing if your agent responds correctly, but whether it’s getting the right inputs in the first place.

Re: Launch HN: Roark (YC W25) – Taking the pain out of voice AI testing

#24
post #14

As someone who's building a personal work assistant for voice - I see the merit in automating test case generation and validation. All products in this space by YC teams are targeted at scaled voice agent startups or teams. - Roark ( https://roark.ai/ ) - Hammin ( https://hamming.ai/ ) - Coval ( https://www.coval.dev/ ) - Vocera ( https://www.vocera.ai/ ) How do you differentiate - who is this for? Voice agent devs p…

Wow, YC has so much conviction in this space that they invested in four of the same company. If anyone ever doubts if YC will invest in direct competitors, this should be your answer. Roark: Best of luck. Talk to customers, talk to customers, talk to customers!

“Spaces” are much less important for early stage companies in newish spaces.

There was a time when Facebook, Twitter, GitHub and LinkedIn were all considered social media companies- competing to become the place I talked to dev friends.

In a way they all succeeded, and in another way they all failed.

Re: Launch HN: Roark (YC W25) – Taking the pain out of voice AI testing

#25

This seems useful for issues early in the convo but what if the AI responses diverge from the recorded convo prior to the issue being hit?

That’s a great point! While we do our best to simulate an identical case, if the agent responds differently, our focus is on whether the key evaluator or goal for that replay set passes or fails. We use that as the source of truth and flag the exact moment where the conversation diverges from the expected flow.

Re: Launch HN: Roark (YC W25) – Taking the pain out of voice AI testing

#29

As someone who's building a personal work assistant for voice - I see the merit in automating test case generation and validation. All products in this space by YC teams are targeted at scaled voice agent startups or teams. - Roark ( https://roark.ai/ ) - Hammin ( https://hamming.ai/ ) - Coval ( https://www.coval.dev/ ) - Vocera ( https://www.vocera.ai/ ) How do you differentiate - who is this for? Voice agent devs p…

Great question! There’s been a lot of movement in this space, but most existing solutions focus on simulation-based testing—generating synthetic test cases or scripted evaluations. Roark takes a different approach: we replay real production calls against updated AI logic, preserving actual user inputs, tone, and timing. This helps teams catch failures that scripted tests miss—especially in high-stakes industries like…

> Roark takes a different approach: we replay real production calls against updated AI logic

How does that work? As soon as the AI responds with something different, the rest of the customer call is mismatched.

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