Fix. Learn. Prevent.
PlayerZero brings AI to a new era of software
development beyond the code editor.
PlayerZero brings AI to a new era of software development
beyond the code editor.
Agentic Debugging
AI agents that automate the path from problem to resolution. Debug any problem to the line of code and make sure it never happens again.
Debug quickly
Agents map each customer ticket to the exact code, commit, and line that introduced it.
Propose fixes and safeguards
A candidate patch appears in your PR or IDE, and the system records a check that blocks the same condition in future builds.
Accumulate working knowledge
Every resolved issue becomes a pattern the agent can match next time, reducing re-work and review load.
Codebase & Runtime Awareness
Navigate, debug, and build across millions of lines of code and hundreds of repos.
Multi-repo context
A single index spans every repo and service, so queries jump cleanly across boundaries and surface the owning team, file, and commit.
Intent-aware ranking
Agents weigh signals from commit graphs, call-graphs, and live telemetry to pull back the few lines that matter for the task at hand.
RL-driven retrieval
Reinforcement-learning retrieval systems follow dependencies, code history and runtime traces to reveal how complex or legacy systems really behave.
Agentic Debugging
AI agents that automate the path from problem to resolution. Debug any problem to the line of code and make sure it never happens again.
Debug quickly
Agents map each customer ticket to the exact code, commit, and line that introduced it.
Propose fixes and safeguards
A candidate patch appears in your PR or IDE, and the system records a check that blocks the same condition in future builds.
Accumulate working knowledge
Every resolved issue becomes a pattern the agent can match next time, reducing re-work and review load.
Codebase & Runtime Awareness
Navigate, debug, and build across millions of lines of code and hundreds of repos.
Multi-repo context
A single index spans every repo and service, so queries jump cleanly across boundaries and surface the owning team, file, and commit.
Intent-aware ranking
Agents weigh signals from commit graphs, call-graphs, and live telemetry to pull back the few lines that matter for the task at hand.
RL-driven retrieval
Reinforcement-learning retrieval systems follow dependencies, code history and runtime traces to reveal how complex or legacy systems really behave.
Agentic Debugging
AI agents that automate the path from problem to resolution. Debug any problem to the line of code and make sure it never happens again.
Debug quickly
Agents map each customer ticket to the exact code, commit, and line that introduced it.
Propose fixes and safeguards
A candidate patch appears in your PR or IDE, and the system records a check that blocks the same condition in future builds.
Accumulate working knowledge
Every resolved issue becomes a pattern the agent can match next time, reducing re-work and review load.
Codebase & Runtime Awareness
Navigate, debug, and build across millions of lines of code and hundreds of repos.
Multi-repo context
A single index spans every repo and service, so queries jump cleanly across boundaries and surface the owning team, file, and commit.
Intent-aware ranking
Agents weigh signals from commit graphs, call-graphs, and live telemetry to pull back the few lines that matter for the task at hand.
RL-driven retrieval
Reinforcement-learning retrieval systems follow dependencies, code history and runtime traces to reveal how complex or legacy systems really behave.
Agentic Debugging
AI agents that automate the path from problem to resolution. Debug any problem to the line of code and make sure it never happens again.
Debug quickly
Agents map each customer ticket to the exact code, commit, and line that introduced it.
Propose fixes and safeguards
A candidate patch appears in your PR or IDE, and the system records a check that blocks the same condition in future builds.
Accumulate working knowledge
Every resolved issue becomes a pattern the agent can match next time, reducing re-work and review load.
Codebase & Runtime Awareness
Navigate, debug, and build across millions of lines of code and hundreds of repos.
Multi-repo context
A single index spans every repo and service, so queries jump cleanly across boundaries and surface the owning team, file, and commit.
Intent-aware ranking
Agents weigh signals from commit graphs, call-graphs, and live telemetry to pull back the few lines that matter for the task at hand.
RL-driven retrieval
Reinforcement-learning retrieval systems follow dependencies, code history and runtime traces to reveal how complex or legacy systems really behave.
Why join us:
We believe that as a growing portion of code bases are generated by language models, engineering and support teams will have a greater demand to manage quality for customers — we want to empower that future.
We’re proud to be used by engineering teams at the world's most innovative companies, including Healthline, Dropbox, Okta, and more.
We believe that as a growing portion of code bases are generated by language models, engineering and support teams will have a greater demand to manage quality for customers — we want to empower that future.
We’re proud to be used by engineering teams at the world's most innovative companies, including Healthline, Dropbox, Okta, and more.
Our backers include
Our backers include
and
and
To learn more about our team, visit our company page.
To learn more about our team, visit our company page.
We're production ready and compliant –
We're production ready and compliant –
If you'd like to use PlayerZero, <speak with us>.
If you'd like to use PlayerZero, <speak with us>.
