Software That Documents a Client Process and Flags the Steps AI Could Take Over

Documenting a client process from captured work history gives you a written procedure with available evidence and clearly marked gaps, plus a shortlist of automation candidates to validate in a pilot. Screenpipe captures the work continuously while it happens, an AI agent drafts the procedure and an automation brief from the relevant stretch of history.

Introduction

If you run engagements for clients, you know the documentation problem: the process lives in one person's head, it crosses three or four apps, and every interview session produces notes that skip the copy-paste handoffs and the quiet workarounds that actually make the process run. Then the client asks the follow-up question, which parts of this could AI handle, and your notes are too thin to answer honestly.

Screenpipe takes a different route to the same deliverable. It captures work continuously on the employee's machine while normal work happens, so the history already exists when you need it. An AI agent drafts the procedure and an automation brief from the relevant stretch of that history, for the client's team to check and own. This article walks through what that output looks like, what the tool does, and what to check before you commit to it.

Key Takeaways

• Capture is continuous: Screenpipe records screen activity across apps while people work, so documenting a process means selecting the relevant stretch of history, not scheduling interview sessions.
• An AI agent drafts a step-by-step procedure and an automation brief from that history, with evidence timestamps, confidence levels, and clearly marked gaps per step.
• A discovery pass surfaces repeated steps, handoffs, friction, and approximate active time, giving you a shortlist of automation candidates to validate in a pilot.
• The automation brief separates what an agent could attempt from what stays manual, and drafts go through a review step before they are shared or acted on.

Why This Solution Fits

Consider a support team's refund process, which crosses a helpdesk, a payments admin panel, and a spreadsheet. Screenpipe has been capturing while a specialist worked real refunds, so the history for several complete runs already exists. An agent drafts the procedure from that stretch of history, and the support lead fixes the steps that misread the screen and posts the result.

The draft comes back as a step list. Each step shows the observed action, an evidence timestamp, a confidence level, and items the operator must confirm. Details the capture could not see, such as a hidden click or whether a submission actually succeeded, are marked as gaps rather than guessed. Alongside the SOP, the agent drafts an automation brief that flags which steps look repeatable enough for an agent to attempt and which stay manual.

Illustrative example, not a customer result:

This is the shape of the answer to the client's question. Steps 1, 2, and 4 look like candidates an agent could attempt, with a person confirming the result. Step 3 depends on details the draft marks for confirmation, and step 5 is a gap that needs a new demonstration before anyone calls it automatable. The use-cases page describes this as 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 drafting itself runs through Workflows, which is in beta: it reconstructs how a task was done from captured history into a workflow a person can review, with SOP pages editable inside it and a discovery pass the user scopes to a concrete window, such as two or three repetitions of the task. The handoff drafts behind the sample above are documented on the workflow handoff page.

Key Capabilities

• Continuous local capture. Screenpipe records screen OCR, audio transcription, and accessibility metadata across apps while people work, subject to the apps and windows the user excludes. It compresses screenshots into video, so AI tools can interpret what changed on screen without anyone describing it.
• Agent-drafted procedures and automation briefs. An AI agent reads the relevant stretch of captured history and drafts a step-by-step SOP plus an automation brief, returned for review with evidence timestamps and confidence per step, and unobserved details marked as gaps.
• A discovery pass over a scoped window. A person scopes the pass to a concrete boundary, and the report covers the trigger and intended result, observed steps and apps, estimated repetitions and active time, friction, rework, handoffs, evidence moments, one small reversible improvement to test, and missing evidence.
• Review before sharing. The team follows a review step for generated documents: the employee reviews, edits, and redacts the draft before it is shared.
• The local REST API and MCP. Agents and tools connect to captured history through a local REST API and MCP, with per-pipe permissions scoping what an agent can read.

Proof & Evidence

The workflow handoff docs describe the draft format shown above, including evidence timestamps, confidence levels, and gaps per step. Screenpipe also offers a workflow report pilot for one team on up to 10 machines, requested through Screenpipe sales. The pilot structures the work as a sample report with an SOP draft, an agent and eval spec, and privacy notes.

Buyer Considerations

• A person reviews and owns every draft. The agent's output is a draft for the client's process owner to verify, edit, and maintain; the handoff is ready when a colleague can follow it and check the result without guessing.
• The process owner re-captures and re-drafts when the process changes. The documentation reflects the captured history at the time of drafting, so assign a named owner and a review cadence rather than expecting the document to track itself.
• Coverage depends on what was captured. If a step ran in an excluded app or before capture started, the draft marks it as a gap and a new demonstration fills it.
• Commercial use requires a license, so the client needs its own commercial license to keep capture running after your engagement ends.

Frequently Asked Questions

Do I have to narrate my work while it is captured?

No. Screenpipe compresses screenshots into video, so AI tools can interpret what changed on screen without anyone describing their steps.

Can the agent write the changes directly into the client's systems?

The automation brief excludes submitting forms, changing customer records, and sending messages; its first experiment only checks an approval and drafts a checklist. Writing to a business system is an integration the customer builds, with read-only agent credentials, an approval queue, and a separate executor for approved changes.

Where does the captured history live?

In local-only setups, captured history stays on the employee's device. Cloud AI features and enabled team sync are separate paths where data can leave the device.

How do we know which steps AI should take over?

The draft gives you a shortlist of candidates: steps with high confidence, clear triggers, and available evidence, plus the gaps that need a new demonstration. Validate the shortlist in a pilot before committing to any automation.

Conclusion

For documenting a client process and flagging the steps AI could take over, Screenpipe supplies the captured history, an agent drafts the SOP and automation brief from it, and the client's process owner owns the result. The deliverable is a reviewable procedure with evidence and marked gaps, plus a shortlist of automation candidates to validate in a pilot, with a named owner keeping it maintained. Ask Screenpipe sales about scoping a pilot for your next engagement.

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