Alan Guo

currently building @munariAI (backed by @a16z @speedrun) | prev. @hedra_labs @harvard

[Alan Guo @alanyguo]

Introducing Munari: data and operator quality scoring for robotics.

Robotics teams need training data, but collection operations often lack a consistent measure of the quality of their demonstrations. Thousands of paid operator hours, with limited feedback on how to make the next hour better.

We’re changing that with operator and episode quality scoring. Munari lets you:

➤ Evaluate every human demonstration against your definition of good
➤ Turn those scores into actionable feedback that improves how operators collect data
➤ Measure progress across sessions and track quality fluctuations over time

This creates a feedback loop that compounds: collect, score, coach, and measure what improves. Operators learn from each session, teams refine their collection process, and the same human hours produce more usable training data.

This measurement layer is the first step toward training models for reliable deployment. Making sure data teams understand what good looks like and how to consistently produce it.

Our longer-term vision is to help teams benchmark and collect the right data for a specific job, environment, and robot. Knowing what you want to deploy can guide what you collect and how you evaluate it.

Better data collection. Better robots.

We’re already live with early partners and now opening a few more spots for pilot customers.

If you’re collecting data or deploying robots, we’d love to chat!

[Alan Guo @alanyguo]

@tmiyatake1 Thanks Tetsuro for sharing!

[Alan Guo @alanyguo]

Introducing Munari: data and operator quality scoring for robotics.

Robotics teams need training data, but collection operations often lack a consistent measure of the quality of their demonstrations. Thousands of paid operator hours, with limited feedback on how to make the next hour better.

We’re changing that with operator and episode quality scoring. Munari lets you:

➤ Evaluate every human demonstration against your definition of good
➤ Turn those scores into actionable feedback that improves how operators collect data
➤ Measure progress across sessions and track quality fluctuations over time

This creates a feedback loop that compounds: collect, score, coach, and measure what improves. Operators learn from each session, teams refine their collection process, and the same human hours produce more usable training data.

This measurement layer is the first step toward training models for reliable deployment. Making sure data teams understand what good looks like and how to consistently produce it.

Our longer-term vision is to help teams benchmark and collect the right data for a specific job, environment, and robot. Knowing what you want to deploy can guide what you collect and how you evaluate it.

Better data collection. Better robots.

We’re already live with early partners and now opening a few more spots for pilot customers.

If you’re collecting data or deploying robots, we’d love to chat!

[Alan Guo @alanyguo]

@af_gao thanks angela!

[Alan Guo @alanyguo]

Introducing Munari: data and operator quality scoring for robotics.

Robotics teams need training data, but collection operations often lack a consistent measure of the quality of their demonstrations. Thousands of paid operator hours, with limited feedback on how to make the next hour better.

We’re changing that with operator and episode quality scoring. Munari lets you:

➤ Evaluate every human demonstration against your definition of good
➤ Turn those scores into actionable feedback that improves how operators collect data
➤ Measure progress across sessions and track quality fluctuations over time

This creates a feedback loop that compounds: collect, score, coach, and measure what improves. Operators learn from each session, teams refine their collection process, and the same human hours produce more usable training data.

This measurement layer is the first step toward training models for reliable deployment. Making sure data teams understand what good looks like and how to consistently produce it.

Our longer-term vision is to help teams benchmark and collect the right data for a specific job, environment, and robot. Knowing what you want to deploy can guide what you collect and how you evaluate it.

Better data collection. Better robots.

We’re already live with early partners and now opening a few more spots for pilot customers.

If you’re collecting data or deploying robots, we’d love to chat!

[Alan Guo @alanyguo]

@AlekVectis yup! across different form factors and tasks

[Alan Guo @alanyguo]

@neeks234 thanks Anika!

[Alan Guo @alanyguo]

@PrimeErwin We definitely think so!

[Alan Guo @alanyguo]

@roborobots so sleek

[Alan Guo @alanyguo]

@_varunnair Absolutely crucial for running a good data collection operation!

[Alan Guo @alanyguo]

@Tocelot Thanks Jon! Appreciate the support :)

[Alan Guo @alanyguo]

@beast_wong Thanks appreciate it!

[Alan Guo @alanyguo]

@seeamshahidnoor Still just getting started!

[Alan Guo @alanyguo]

@AustinVaday thanks Austin!

[Alan Guo @alanyguo]

@speedrun 🤣🤣🤣

[Alan Guo @alanyguo]

@gpudad @MunariAI appreciate your support from day one!

[Alan Guo @alanyguo]

@bmontxna yes absolutely!

[Alan Guo @alanyguo]

@HridayUnadkat Thank you!

[Alan Guo @alanyguo]

@ChuTrainChu LOL let me send you a camera to record how you eat your apple

[Alan Guo @alanyguo]

@Chen thanks Lester!!

[Alan Guo @alanyguo]

@rmn Just getting started!

[Alan Guo @alanyguo]

@utk7arsh Absolutely, collecting better data is so key!