Turn play sessions into issues your team can fix.
Playtest.ai runs structured game tests with real players and AI agents, then turns every session into deduplicated, evidence-backed QA.
Thirty playtests shouldn’t
create thirty reports.
Playtesting generates a lot of useful evidence. It also generates a lot of noise.
One tester says the game crashed. Another says they got a fatal error. Another says closing the game broke something. Those may all describe the same issue.
Playtest.ai turns scattered sessions, notes, videos, logs, and feedback into the few recurring problems the development team actually needs to investigate.
Know what happened.
Know how often.
Know what to fix.
Instead of handing your team raw playtest output, we give them an issue.
From question
to investigation.
Set the question
Give us a playable build and tell us what you want to learn.
- Can players reliably finish onboarding?
- What breaks in character customization?
- Why are players getting stuck here?
- Try to reproduce this crash.
Run the test
We use the right method for the test: structured human playtests, automated testing, AI agents, or a combination.
Find the patterns
Playtest.ai analyses sessions for repeated failures, crashes, friction, unexpected states, hardware correlations, and recurring player behaviour.
Investigate
Related observations are grouped into issues and returned with the evidence needed to reproduce, prioritize, and fix them.
Many observations.
One issue to investigate.
Playtest.ai converts independent reports into an anonymized, evidence-backed issue with prevalence, environment, pattern, and a recommended next step.
It’s figuring out
what matters.
After a playtest, someone still has to read everything, determine which observations refer to the same problem, and decide what deserves attention.
Playtest.ai is building that layer.
From sessions
to signal.
Structured playtesting
Run focused sessions against a feature, flow, build, or hypothesis.
Issue clustering
Turn multiple descriptions of the same underlying failure into one issue.
Ten players reporting the same crash should not create ten tickets.Find the pattern
Compare issues across build, operating system, hardware, player behaviour, location, and session outcome.
Preserve the context
Connect every issue back to the sessions that produced it.
gameplay recordingfeedbacktimestampshardwarebuild informationlogsinputsgame stateVerify what repeats
A strange event once is interesting. A strange event five times is a bug worth investigating.
Scale the repetitive work
As Playtest.ai’s autonomous testing systems improve, agents can take on more repetitive exploration, regression, and reproduction work.
Humans notice.
Machines remember.
player experience · confusion · expectations · subjective judgment · genuinely unexpected behaviour
repetition · reruns · systematic variation · correlation · reproduction · high-volume coverage
Humans help discover what matters. Automation helps determine how often it happens and whether it can happen again.
Start with the problems that
waste your team’s time.
Stability
- crashes
- freezes
- disconnects
- long-session failures
Gameplay
- soft locks
- broken interactions
- unexpected states
- failed mechanics
Progression
- missing triggers
- quest blockers
- impossible sequences
- save / load problems
UX & Onboarding
- confusing flows
- unclear controls
- unexpected behaviour
- authentication friction
Performance
- frame-rate degradation
- hardware-specific issues
- loading problems
- performance anomalies
Systems
- inventory
- customization
- abilities
- persistence
- interactions
Give your QA team signal,
not another inbox.
Your best QA people should be deciding: Is this serious? Why is this happening? What should we fix first? They should not spend their time reading thirty versions of the same bug report.
Playtest.ai helps surface the recurring problems first.
“It crashed when I quit.”
“Fatal error on close.”
“Exit broke the game.”
1 RECURRING ISSUEWhat players say matters.
What repeatedly happens matters more.
Traditional playtesting is excellent at understanding player sentiment and experience. Traditional QA is excellent at investigating defects.
Playtest.ai connects the two. We use what happens during real play sessions to surface recurring, evidence-backed issues developers can investigate.
The player doesn’t always
need to be human.
Once a failure can be observed, measured, and reproduced, much of the repetitive work around it can be automated.
Playtest.ai is building agents that can increasingly take on more of that work.
Humans remain where judgment matters. Agents take on more of the grind.
Building systems that understand interactive worlds.
Games are complex software systems where every action can change what happens next. Understanding them requires more than looking at screenshots.
what state the game was in→what the player did→what happened afterward
Every Playtest.ai session helps us build better infrastructure for testing, reproducing, and reasoning about those interactions.
Give us the part of your game that keeps causing problems.
Start small.
We’ll run the test and show you what comes back.
No long integration project required for the first evaluation.ONE BUILDA playable PC build
ONE SYSTEMThe feature, flow, or area you want tested
ONE QUESTIONSomething your team genuinely wants answered
“Why are players failing onboarding?”“Can you reproduce this crash?”“What breaks if players misuse inventory?”“Where are players getting stuck?”Things teams
ask us.
No. Humans are excellent at judgment, player experience, and discovering genuinely unexpected problems. We want software to handle more of the repetitive collection, analysis, rerunning, and reproduction work around them.