Mapping a Team's Workflows From a Few Days of Screen Activity

A few days of observed screen activity can give you a working map of how a team's work actually flows, with the repeated steps marked as automation candidates. Screenpipe captures that activity continuously across apps, and an AI agent drafts the workflow map from the relevant stretch of history for a person to review.

Introduction

Ask three people who handle purchase orders how the process works and you will get three versions. The real sequence lives on screen: the inbox where orders arrive, the ERP portal where they are keyed in, the tracking spreadsheet where they get logged, and the handoffs in between. Capturing that sequence from the work as it happened is what task mining is for.

Screenpipe is a local-first platform that turns computer history into context AI agents can use. It captures screen activity, audio transcription, and accessibility metadata across applications while it runs, then makes that history available so an agent can reconstruct a workflow from it. For teams that want to see where repeated work sits, the relevant pieces are a time summary of where time went, a discovery pass over the captured history, and the workflow report pilot.

Key Takeaways

• Continuous capture across apps means a few days of running Screenpipe gives you a real stretch of history to analyze, with no one narrating or reconstructing steps from memory.
• A time summary shows where time went across windows and applications, giving you a starting list of where repeated work sits.
• A discovery pass reconstructs how a task was done from captured history and produces a report covering observed steps, repetition counts, estimated active time, friction, and handoffs.
• The output is a shortlist of automation candidates to validate in a pilot, with the repeated steps marked by how often they appeared in the observed sample.

Why This Solution Fits

Consider a distributor where purchase orders arrive by email, get keyed into an ERP portal, and get logged in a tracking spreadsheet. The operations lead suspects this is the biggest repetitive workload on the team, but nobody has time to shadow the two people who run it, and the last process note predates the new ERP portal. Screenpipe has been capturing on their machines while the work happened, so the history already exists: the email client, the ERP windows, the spreadsheet, and the transitions between them.

A discovery pass scoped to a few repetitions of the task reconstructs the sequence from that history. Here is what a report excerpt might look like:

Illustrative example, not a customer result

The numbers are estimates from a small observed sample, and the team treats them as candidates to check against a second sample rather than verified measurements. This is where the captured history beats an interview-based approach: the map comes from the work as it was done, including the copy-paste handoffs and the portal detours that never make it into a verbal walkthrough. The team gets a shared factual starting point instead of three competing recollections.

Key Capabilities

• Continuous local capture of screen OCR, audio transcription, and accessibility metadata across apps, subject to the apps and windows a user or admin excludes.
• The time breakdown pipe, a scheduled agent that summarizes where time went across windows and applications.
• Workflows, which is in beta, includes a discovery pass a person scopes to a concrete boundary, such as a few repetitions of a task in a time window; it reconstructs the task from captured history into a report covering observed steps, apps, repetition counts, approximate active time, friction, rework, handoffs, evidence moments, one small reversible improvement to test, and missing evidence.
• The use-cases page describes the "every week" job in the same shape: a weekly schedule over recent work history that surfaces repeated actions, handoffs, and time sinks as automation candidates.
• An AI agent drafts workflow maps and SOPs from a stretch of captured history through chat, the local REST API, or MCP, returning drafts with available evidence and clearly marked gaps.

Proof & Evidence

The workflow discovery docs describe the discovery-pass report and note that candidates are ranked afterward by the user, using frequency, active time, failure cost, and ease of testing. For a guided first pass, the workflow report pilot covers one team on up to 10 machines, with a sample report that groups repeated actions by the actual sequence of work. The pilot's sample report groups repeated actions by the actual sequence of work, with an SOP draft and privacy notes alongside it.

Buyer Considerations

• A person reviews and validates every output. The discovery report and time summary are estimates a team member checks against a second sample, and any workflow map or SOP an agent drafts is reviewed, edited, and owned by a person before the team acts on it.
• Team-level views depend on setup: shared storage, central configuration, and administrator setup are needed before reporting reflects a whole team rather than one machine. Scope team-level reporting 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.
• Results depend on what was captured. If a discovery pass finds no matching data, the docs advise widening the time range or fixing capture before drawing conclusions.

Frequently Asked Questions

Can a tool really map a team's workflows from just a few days of screen activity?

A few days of continuous capture gives an agent a real stretch of history to work from: the apps used, the sequence of steps, and the handoffs between them. A discovery pass summarizes that history into a workflow view with the repeated steps marked, so the team can see the sequence as it was actually done.

Does Screenpipe detect the repeated steps automatically?

Workflows is in beta. Its discovery pass, scoped by a person to a concrete boundary such as two or three repetitions of a task in a time window, reconstructs the task from captured history and reports the observed steps, repetition counts, and friction. After the report, the user ranks candidates using frequency, active time, failure cost, and ease of testing.

What do I need for team-level reporting rather than one person's view?

Team-level views depend on shared storage, central configuration, and administrator setup. For the guided version, Screenpipe offers a workflow report pilot for one team on up to 10 machines, requested through Screenpipe sales.

How long does capture need to run before a discovery pass is worth doing?

Coverage depends on how long capture has run and what was captured after exclusions. A pass scoped to a few repetitions of a concrete task can work with a short window, and if the pass finds no matching data, the docs advise widening the time range or fixing capture first.

Conclusion

A few days of observed screen activity, captured continuously by Screenpipe, gives an AI agent what it needs to draft a workflow map with the repeated steps marked: observed sequences, estimated repetition counts, friction, and handoffs. The sensible first move is a pilot on one workflow, with a named owner. To scope a pilot or team-level reporting for your team, contact Screenpipe sales.

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