Automate AI ops workflows in the browser
Use PixieBrix to customize and automate any tool you already use, right in your browser.
3000+ Integrations
AI Automation
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"PixieBrix has solved one of our hardest operational problems - streamlining communication & product updates across support teams. Tracking and keeping everyone in the loop has yielded better agent performance, customer satisfaction & taken a huge burden off management."

Thatcher Foster

VP, Client Solutions

Top reasons to automate in the browser
Pull model monitoring metrics, deployment status, and alert details into one sidebar without switching between dashboards and consoles
Create incident tickets from any monitoring page or cloud console with pre-filled error context, model metadata, and severity in one click
Surface deployment history and model version info from your cloud platform directly inside your ticketing tool for faster triage
Auto-generate runbook updates and status reports by pulling metrics from dashboards and experiment trackers
AI Copilot in the sidebar can summarize alerts, draft incident reports, or extract key performance metrics from the current page
Build reusable workflows your team can share for common ops tasks without writing scripts or waiting for platform engineering
Integrate with 3000+ apps
Frustrations that cost your team hours every week
  • Constant tab switching between model monitoring dashboards, ticketing systems, and cloud consoles to investigate incidents and log findings
  • Manually copying error logs, model performance metrics, and incident details from monitoring tools into Jira or Slack for escalation
  • No quick way to create an incident ticket from a Datadog alert or cloud console error page with pre-filled context
  • Cross-referencing model metadata in experiment trackers with deployment status in cloud dashboards requires opening multiple tools side by side
  • Repetitive documentation updates — copying metric snapshots from dashboards into SOPs, runbooks, and training guides each review cycle
  • No way to surface relevant model deployment context from a cloud platform when triaging a ticket in the project tracker
  • Manually transferring data validation findings from notebooks and dashboards into QA tracking spreadsheets

Chat with AI to create your first custom workflow

Summarize the alert details on this monitoring page and draft an incident report for the on-call channel
Extract the key metrics from this dashboard and create a ticket in my project tracker with severity and model info
Pull the latest deployment steps from this cloud console page and update the corresponding section in my runbook
Extract the data validation results from this page and add them as rows to my QA tracking spreadsheet
Summarize the deployment status and model version info from this page for a status update in my team channel
Capture the key performance metrics from this monitoring dashboard and format them for my weekly ops report

Watch PixieBrix in action

Frequently Asked Questions

PixieBrix is designed for teams that want to move faster without heavy engineering effort. It is commonly used by support teams, operations teams, product teams, and technical teams who need to connect tools, reduce manual work, and ensure the right information reaches the right people at the right time.

PixieBrix is a browser-based automation platform that lets you customize how the tools you already use work together. It allows teams to add context, automate workflows, and create guided experiences across apps like support tools, internal dashboards, and SaaS products without building or maintaining custom integrations.

PixieBrix works by layering automation directly into the browser. It can read data from the page you are viewing, connect to APIs, and trigger actions like sending messages, filling forms, or enriching data in real time. This lets teams automate workflows exactly where work is already happening.

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