Browser tools to speed up data scientist workflows
Use PixieBrix to customize and automate any tool you already use, right in your browser.
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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 data definitions, schema details, and prior experiment results from the team wiki into a sidebar while working in notebooks or dashboards
Copy model results and summary statistics into stakeholder presentations via a browser sidebar without switching tabs or manual reformatting
Create experiment tasks and data request tickets from any page with pre-filled context from the current notebook or dashboard
Surface dataset documentation and data lineage from the catalog in an overlay while querying or reviewing results in another tool
AI Copilot in the sidebar can summarize model outputs, draft experiment writeups, or explain statistical results from the current page
Build reusable reporting and experiment tracking workflows your team can share without coding custom integrations or waiting for platform engineering
Integrate with 3000+ apps
Frustrations that cost your team hours every week
  • Switching between notebook environments, BI dashboards, documentation wikis, and project trackers to assemble context for an analysis across multiple browser tabs
  • Manually copying model results, summary statistics, and chart insights from notebook outputs into stakeholder presentations and reporting documents
  • No quick way to create a data request ticket or experiment task from a stakeholder message without switching to the project tracker and re-entering details
  • Pulling data definitions, feature documentation, and prior experiment results from the team wiki while working in a notebook or dashboard requires constant tab switching
  • Cross-referencing model performance metrics across different dashboard views and experiment logs requires flipping between tabs and copying values
  • No way to surface dataset documentation, schema details, or data lineage from the catalog while querying or reviewing results in another tool
  • Copying experiment parameters, results, and recommendations from notebook outputs into the project tracker or stakeholder updates involves repetitive manual entry

Chat with AI to create your first custom workflow

Find the schema and field definitions for this dataset in my documentation wiki and display them in the sidebar
Extract the key metrics and summary statistics from this page and format them for my stakeholder presentation
Based on this analysis, create an experiment ticket with the hypothesis, data requirements, and timeline pre-filled
Look up prior experiments related to this dataset in my wiki and display the results summary in the sidebar
Summarize the model performance metrics on this page and draft a plain-language interpretation for stakeholders
Based on the results on this page, draft an experiment writeup with methodology, findings, and recommendations

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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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