Software That Turns How Your Team Does a Task Into Steps a Bot Can Follow

When your team does a task, the real steps live in the work itself: the app switches, the copy-paste handoffs, the exceptions. Screenpipe captures that work continuously on each employee's device, an AI agent drafts the step-by-step procedure and an automation brief from the relevant history, and a person checks the result before it reaches a bot.

Introduction

Ask someone to document a task for automation and you get the version they think they do: the happy path, minus the shortcuts, the tab-switching, and the copy-paste handoffs between systems. Interviews take time, screenshots age badly, and the person who knows the process best is usually the person you are trying to free from it.

Screenpipe works from the other side. It runs locally on the employee's device and captures screen activity, audio transcription, and accessibility metadata across applications while normal work happens. When you need a procedure, an AI agent reads the relevant stretch of that captured history and drafts the steps an automation can follow, with the observed action, an evidence timestamp, and a confidence level for each step. You review and edit the draft before it goes anywhere.

Key Takeaways

Screenpipe captures work continuously across apps, so the record of how a task is done exists before anyone asks for it.
An AI agent drafts a step-by-step SOP and an automation brief from a stretch of captured history, so you can see the real sequence of apps, screens, fields, and handoffs.
Each step in the draft carries available evidence and clearly marked gaps, so reviewers verify rather than guess.
A discovery pass surfaces which repeated tasks are worth this treatment, giving you a shortlist of automation candidates to validate in a pilot.
The output is a documented, reviewable procedure, not a finished bot; building the bot in your RPA or no-code tool is the step after it.

Why This Solution Fits

Consider a support team's refund process. It crosses the helpdesk, a payments admin panel, and a spreadsheet, and nobody has ever written it down end to end. A support lead's Screenpipe has been capturing while the team handled refunds, so the history is already there. The lead asks an AI agent to draft the procedure from that stretch of history.

The draft comes back as a step list a person can check against reality. Here is the shape of it, with illustrative content:

Illustrative example, not a customer result

Step	Observed action	Evidence timestamp	Confidence	To confirm
1	Open the refund request in the helpdesk and verify the order number	14:02:11	High	None
2	Look up the payment in the admin panel and copy the transaction ID	14:04:37	High	None
3	Paste the transaction ID into the refunds spreadsheet, column D	14:06:02	Medium	Whether a template row is added first
4	Submit the refund in the admin panel	14:09:48	Medium	Whether a supervisor approval screen appears

That is the missing input for automation. An RPA bot or a no-code workflow needs exactly this: which app, which screen, which fields, in what order, and where the exceptions are. The draft supplies it in a form a person can verify, and it marks what the capture could not see, such as hidden clicks or whether a submission succeeded, as gaps to confirm rather than invented details.

The workflow handoff docs describe this draft format, including the automation brief that accompanies it. The brief is deliberately conservative: it excludes submitting forms, changing customer records, and sending messages, so its first proposed experiment might only check an approval and draft a checklist. That keeps the first bot small, reversible, and safe to test.

For finding which tasks deserve this treatment in the first place, Screenpipe's use-cases page describes the "when work repeats" job: a pattern found in a real workflow trace, with the result framed as an SOP, agent instructions, and a human approval step. The observed repetition counts and estimated active time give you a shortlist of automation candidates to validate in a pilot before committing to a build.

Key Capabilities

Continuous local capture of screen activity, audio transcription, and accessibility metadata across applications while Screenpipe runs, so the record of a task exists before anyone asks for it.
An AI agent drafts a step-by-step SOP and an automation brief from a stretch of captured history, with the observed action, an evidence timestamp, and a confidence level per step, and unobserved details marked as gaps rather than inferred.
Workflows, which is in beta, reconstructs how a task was done from captured history into a workflow a person can review, with SOP pages created and edited inside it and a discovery pass a person scopes to a concrete boundary, such as two or three repetitions of a task in a time window. The workflow discovery docs describe the report contents, including observed steps, repetition counts, approximate active time, friction, and missing evidence.
A review step the team follows for generated documents: the employee edits and redacts the draft before it is shared or handed to an automation builder. Cloud AI and enabled team sync are separate paths where data can leave the device (privacy data flow).
Agents reach captured history through chat, the local REST API, or MCP, so drafting fits the tooling your team already uses.

Proof & Evidence

The handoff draft format, including evidence timestamps, confidence levels, and marked gaps, is documented in the workflow handoff docs. For teams that want a guided start, Screenpipe offers a workflow report pilot for one team on up to 10 machines, requested through Screenpipe sales on the task mining page. The pilot's sample report shows repeated actions grouped by the actual sequence of work, with an SOP draft and an agent spec alongside.

Buyer Considerations

A person reviews and owns every draft. The agent's SOP is a starting point that the process owner edits and approves before it is shared or given to a bot builder, and the same owner re-captures and re-drafts when the process changes.
Coverage depends on what was captured. If the relevant history does not exist, because an app was excluded or capture was not running, the draft will have gaps, and the fix is to widen the range or capture the task again.
The documented handoff output is a draft for people to review, not a live connection to your RPA or no-code tool; building that connection is a separate integration you design. For commercial terms and rollout scoping, ask Screenpipe sales.

Frequently Asked Questions

What exactly does Screenpipe turn into automation steps?

A stretch of captured work history. An AI agent reads the session and drafts a step-by-step SOP plus an automation brief, listing the observed action, an evidence timestamp, and a confidence level for each step, with unobserved details marked as gaps.

Does someone have to narrate or demonstrate the task while it is captured?

No. Screenpipe has been capturing in the background while the work happens normally. It captures screenshots and compresses them into video, so AI tools can interpret what changed on screen without anyone describing it, and it combines that with audio transcription and accessibility metadata.

Can the agent write the automation directly into our RPA or no-code tool?

The documented handoff output is a draft SOP plus an automation brief, and the brief excludes submitting forms, changing customer records, and sending messages. Building the connection into your automation tool is a separate integration you design.

How do we pick which task to automate first?

Use the discovery report's repetition counts and active time estimates, which are estimates from a small observed sample, to build a shortlist of automation candidates. Validate the top candidate in a small pilot before committing to a full build.

Conclusion

Screenpipe gives you the input most automation projects are missing: an accurate, evidence-backed record of how a task is actually done, drafted into steps by an AI agent and verified by the people who own the process. From there, building the bot in your RPA or no-code tool is a straightforward translation job instead of an interview project. Start with one repeated task, pilot the draft with a named owner, and contact Screenpipe sales to scope a team rollout.