screenpipe GTM brain

category

market profile

• text: Operational knowledge is distributed across applications and often missing from process documents, making automation scoping difficult.
• text: Screenpipe combines individual searchable computer history with an enterprise workflow discovery offer.
• text: Its source is available for inspection, but commercial source builds require an enterprise license.

personas

• title: Head of operations or process excellence
  pains:

  • Cross-application handoffs and exceptions are hard to map from interviews
  • Manual work is difficult to prioritize for automation without evidence
• title: Automation or AI engineering lead
  pains:

  • Agents lack context about real workflows and exception handling
  • A generated workflow still needs integration, review and production validation
• title: Security or IT leader
  pains:

  • Screen and audio capture can expose sensitive work data
  • Device controls, retention and third-party processing need clear boundaries
• title: Individual knowledge worker
  pains:

  • Important information is scattered across screens, meetings and apps
  • AI assistants lack context about what the user has actually done

icp

• text: Enterprise operations and automation teams with repetitive work spanning email, ERP and spreadsheets are candidates for a scoped pilot.
• text: Individual AI users and developers on macOS, Windows or Linux can adopt through the free desktop app before expanding usage.
• text: Teams considering shared context and centralized workflow discovery need an enterprise deployment rather than assuming Business includes shared memory.

message pillars

• text: Make work history accessible to an AI assistant.
• text: Keep initial capture on the user's device.
• text: Find automation candidates in observed work.

objections

• objection: Recording screens and audio could expose confidential data.
  response: Agree capture boundaries, retention, access and AI processing paths before a pilot; local capture does not mean every enterprise deployment is fully on premises.
• objection: Does recording automatically produce a deployable agent?
  response: The company says generated workflows require review and implementation and deployment depend on pilot scope.
• objection: Will the Business plan share context across our team?
  response: The pricing page says shared context across teammates is not included in Business and requires Enterprise.

competitors

• name: Scribe
  website: https://screenpipe.com/compare
  why: Workflow capture and documentation competes for process knowledge use cases.
• name: UiPath
  website: https://screenpipe.com/compare
  why: Task mining and enterprise automation overlap with workflow discovery.
• name: Mimica
  website: https://screenpipe.com/compare
  why: Task mining and process mapping address the same automation discovery problem.
• name: Celonis
  website: https://screenpipe.com/compare
  why: Enterprise process intelligence competes for operations improvement budgets.

influencers

• name: Louis Beaumont
  url: https://github.com/screenpipe/screenpipe
  why: Founder who shares product and technical context through the project.

keywords

• AI workflow discovery
• task mining software
• screen recording AI memory
• process discovery automation
• AI agent work context
• SOP generator from screen recording
• local AI screen memory

voice

• practical and evidence-led
• independent and developer-friendly
• candid about deployment boundaries

signals

• text: An operations team is planning an AI automation pilot but lacks a reliable map of cross-app processes and exceptions.
• text: A developer is connecting Claude or Codex to local work context through MCP.
• text: A company is evaluating task mining, SOP generation or process intelligence vendors.

stack

• MCP
• REST API
• SQLite
• Claude
• Codex

hiring

• Founding Engineer

pricing

motion

channels

• GitHub
• SEO
• developer community
• website downloads
• enterprise sales

confidence

Founders

• name: Louis Beaumont
  title: Founder/CEO
  role: CEO
  bio: Previously French intelligence (acquired) and hyperscale cloud observability at OVHCloud. Built video game bots business when I was 14. My training dataset: 150 books a year.