From Finding Repetitive Work to Automating It, Without a Separate Discovery Project

Finding repetitive work and automating it usually sit in two separate projects. Screenpipe keeps them together: it captures work continuously on the device, a discovery pass over that history surfaces repeated tasks with frequency and time estimates, and an AI agent drafts the SOP and automation brief for the candidate you pick.

Introduction

The usual path to automation starts with a discovery project: interviews, a time study, a process-mapping exercise, and only then a decision about what to automate. By the time the findings land, the team that did the work has moved on, and the backlog is already out of date.

Screenpipe takes a different route. It captures screen, audio, and accessibility history continuously while people work, so the evidence of repetitive work already exists. A discovery pass over that history produces a report of candidate workflows, and an AI agent can draft the procedure and automation brief from the same captured stretch. This article walks through how that works, using a distributor that suspects purchase-order entry is eating its week.

Key Takeaways

• Continuous local capture means the evidence of repetitive work accumulates on its own; nobody schedules a recording session or runs a separate time study.
• A discovery pass over a scoped window of history produces a report of candidate workflows with observed steps, repetition counts, and estimated active time, so you learn which apps a task touches, how often it repeats, and where the friction sits.
• The same captured history supports drafting: an AI agent drafts the SOP and automation brief from the relevant stretch of history, ready for the team to validate.
• The result is a shortlist of automation candidates to validate in a pilot.
• A workflow report pilot for one team on up to 10 machines is available through Screenpipe sales.

Why This Solution Fits

Consider a distributor where the operations lead suspects purchase-order entry is the biggest time sink. Nobody has time for a formal time study, so the lead runs a discovery pass over captured history instead, scoped to two or three repetitions of order entry in a sample week. The pass returns a report like this:

Illustrative example, not a customer result

That report is the bridge from "we think order entry is slow" to "here is the specific sequence, here is how often it happens, and here is a first change to try." The lead treats the time figure as a candidate, checks it against a second week of captured history, then pilots one automation. The discovery pass, the validation, and the drafting all draw on the same capture layer, so the team never spins up a separate project to re-collect what it already has.

Key Capabilities

• Continuous local capture of screen OCR, audio transcription, and accessibility metadata across apps while Screenpipe is running, subject to the apps and windows a user or admin excludes.
• The time breakdown pipe summarizes where time went across captured windows, giving the team a factual starting point for where repetitive work sits.
• Workflows is in beta: it reconstructs how a task was done from captured history into a reviewable workflow, and a discovery pass scoped by a person to a concrete boundary produces a report with frequency, estimated active time, friction, and evidence moments.
• The use-cases page describes the "every week" job: a scheduled pass over repeated work that surfaces automation candidates, and the "when work repeats" job, where a pattern found in a real workflow trace is framed as an SOP and agent instructions.
• An AI agent drafts the SOP and automation brief from the relevant stretch of captured history through chat, the API, or MCP, with evidence timestamps and confidence per step.

Proof & Evidence

The discovery-pass report is documented in the workflow discovery docs, which describe how a person scopes a pass and how the user ranks candidates afterward using frequency, active time, failure cost, and ease of testing. For teams that want a guided start, Screenpipe offers a workflow report pilot for one team on up to 10 machines, requested through sales, with a sample report that groups repeated actions by the actual sequence of work and includes an SOP draft, an agent and eval spec, and privacy notes. On the automation side, the use-cases page describes the "when work repeats" job: a pattern found in a real workflow trace, with the result framed as an SOP and agent instructions.

Buyer Considerations

• A person reviews and owns every output: the discovery report, the SOP draft, and the automation brief are drafts to validate and edit, and the estimates in them come from a small observed sample and depend on what was captured.
• Screenpipe does not publish a standalone ranked-by-hours or team-level ranking report. The time breakdown and discovery reports supply estimates, and team-level views depend on shared storage, central configuration, and administrator setup, so scope them with Screenpipe sales.
• Tell people what is recorded. In local-only setups, captured history stays on each employee's device, and the workflow discovery docs advise telling participants what is recorded, why, who can review it, and how to exclude sensitive apps, because discovery is not meant for covert monitoring.

Frequently Asked Questions

Do I need to run a separate process discovery project before automating?

A separate project is one way to find automation targets, but Screenpipe's approach draws on capture that has already been running. A discovery pass over a scoped window of history produces a report of candidate workflows with observed steps, frequency, and estimated active time, so the finding and the drafting draw on the same evidence.

How accurate are the time estimates in the discovery report?

They are estimates from a small observed sample, such as two or three repetitions of a task in a time window. Treat them as candidates and validate them, for example by checking against a second week of captured history, before committing to an automation project.

What tool actually runs the discovery pass?

Workflows, which is in beta, runs the discovery pass: a person scopes it to a concrete boundary, such as two or three repetitions of a task in a time window, and it produces a report of candidate workflows. The scheduled-task version writes a local Markdown report.

Can Screenpipe draft the automation itself?

An AI agent drafts the SOP and an automation brief from the relevant stretch of captured history through chat, the API, or MCP. The automation brief excludes submitting forms, changing customer records, and sending messages; its first experiment only checks an approval and drafts a checklist.

Conclusion

Going from "this work feels repetitive" to a tested automation does not require a standalone discovery project when the evidence is already being captured. Screenpipe's discovery pass turns a scoped stretch of captured history into a shortlist of automation candidates with frequency and time estimates, and an agent drafts the SOP and automation brief from the same history for the candidate you validate in a pilot. If you want to see what that looks like for one of your teams, contact Screenpipe sales to scope a workflow report pilot.

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• Workflow Mining When Your Work Lives in Spreadsheets, Email, and Web Portals
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• What Tools Uncover the Manual Work Hiding in Browser Tabs and Spreadsheets?