Learns from real work
Capture how your team completes tasks across apps, giving agents a workflow grounded in the job.
Screenpipe for enterprise
Screenpipe captures your company's computer work, maps workflows, and builds AI agents so you can scale with tokens instead of adding headcount.
Built for people and teams who want their time back
Meet Screenpipe
Start with work captured on the device. Review the process, document what matters, and give your agents the context to help.
A person resolves mismatches and approves every payment.
AI interviews capture your team’s decisions, exceptions and checks.
Viewing Step 1 of 7: Open the invoice
Question 1 of 3
Screenpipe captures your steps, then tests an AI agent repeating them in a separate workspace. You review the results before production.
You handle an invoice. AI learns the workflow, then repeats it.
Any harness. Any model, including open source.
5–8 hours
saved in a typical week, by one customer’s estimate.
“I get a daily summary from my Hermes agent using screenpipe information. It also adds calendar blocks for Mission Critical tasks. What a life changer.”
William Lucas
Allied Auto Online
500+
customers using Screenpipe
From finding past context to building their own AI workflows.
“Instead of trying to remember where something happened, I can search for the original evidence in seconds. I've stopped rebuilding context from memory and started building AI workflows grounded in what actually happened.”
Lian Munday
FundingFunnel
Capture how your team completes tasks across apps, giving agents a workflow grounded in the job.
AI interviews add the decisions, exceptions and checks the recording cannot explain.
Check the source screenshots and correct the workflow before handing it to an agent.
Use reviewed workflows with your existing agents through exports, APIs and MCP.
Optional training on the company workflows and data you approve, with scope agreed upfront.
Screenpipe complements company-brain products, but maintaining a company knowledge base is not its primary use case. Its focus is capturing work history, understanding workflows, and giving your AI context from work across your apps. You can use that context to inform the shared knowledge you maintain in your company brain.
With personal capture, employees can review outputs before sharing them through an approved channel. Organization-managed storage and policies can allow broader access. Agree on what employees control, what administrators can access, and who can change those settings before inviting the team.
Set capture exclusions before recording and review the history for sensitive material. Agree on retention and deletion for the device and any configured archive or shared destination. Deleting local history does not automatically retract copies already sent to an AI provider, integration, or colleague.
Capture and AI-assisted analysis can reduce manual documentation, but generated workflows still need review. Team-level reports, implementation help, and integrations are scoped separately. Your pilot proposal should distinguish self-service functionality, assisted delivery, and planned features. A workflow recommendation or SOP draft is not a tested, deployed automation.
Agree on a baseline for time per task, task volume, and rework. After a workflow change, measure the same work at the same quality. Compare the value of time recovered with software, setup, and review costs. Time freed up is capacity, not automatically a reduction in payroll. Projected savings stay estimates until the change is tested.
Enterprise pricing depends on the deployment and agreed delivery scope. We scope the seats, duration, support, implementation work, and expansion terms in your proposal, with the total cost agreed before you start. Smaller self-service options are available on the pricing page. A pilot proposal should make clear which outputs and services are included.