task mining for AI agents
Task mining from real work, not survey interviews.
Screenpipe captures the screen-level workflow around your systems: the spreadsheet, browser tab, meeting, message, ERP screen, and repeated handoff. Use that trace to generate SOPs, automation candidates, and computer-use agent evals.
Nobody wrote this down or sat through an interview. Every step lands in the automation matrix with an execution layer, and the exceptions become the escalation map.
workflow report pilot
One team in. One report out. Then a decision.
The enterprise offer is not another generic demo. Deploy a handful of machines in one team, define the data-flow boundaries, and let capture surface the workflows. The report is something your ops, IT, and AI teams can actually evaluate; the workflow list is its output, not your homework.
sample output
Repeated-action report
Recurring workflows
Top repeated actions across apps, users, and days, grouped by the actual sequence of work.
Automation candidates
Estimated repetition, friction, handoffs, and confidence so the team can pick the first automation.
SOP draft
A step-by-step procedure from observed work, not a workshop or someone trying to remember the process.
Agent/eval spec
Inputs, expected outcome, acceptance criteria, edge cases, and traces for a computer-use agent.
Privacy notes
Data-flow boundaries, redaction assumptions, employee controls, and what was excluded from the report.
point of view
Capture should produce decisions, not surveillance dashboards.
Screenpipe's enterprise lane is workflow intelligence for AI adoption: prove which work repeats, what can be automated, what an agent should attempt, and which data paths the buyer approves.
One department beats a fleet rollout
Start with up to 10 machines in one team and capture real work. The workflow map is an output of capture, not homework the buyer does up front.
The useful data lives between systems
ERP, CRM, and ticketing logs miss the spreadsheet, tab, message, meeting, and judgment step. That is where the automation target usually hides.
Agents need traces, not vibes
A usable computer-use agent spec needs real inputs, expected outcomes, edge cases, failure modes, and a way to grade the result.
Privacy is part of the deliverable
A workflow report should say what was captured, excluded, redacted, retained, exported, and shared before the team expands deployment.
where this fits
Process mining finds system events. Screenpipe finds the work between them.
ERP, CRM, and ticketing systems show part of the workflow. The repeated work usually lives between systems: Excel to ERP, vendor bill matching, meeting follow-up, CRM updates, approvals, and weekly reporting.
That is the useful lane for Screenpipe: capture the messy human version of a workflow before asking an AI agent or automation vendor to replace it.
Turn traces into agent evalstask mining vs process mining
Process mining explains the system log. Task mining explains the human run.
The strongest workflow-discovery programs use both. Process mining finds structured variants in systems of record. Task mining fills the messy gaps that become SOPs, automation specs, and realistic agent evals.
deployment modes
Local-first does not mean one data path.
Screenpipe can run as a local-only personal assistant, a scoped team deployment, or an embedded capture engine. The important question for buyers is not a slogan; it is which data flow they approve.
Local-only
- What stays local
- Screen capture, accessibility text, OCR output, audio files, transcripts, and the local database.
- What may leave the device
- Nothing is required to leave the device for core capture and search.
- Buyer decision
- Best for self-serve use, regulated pilots, and proving value before any cloud path is enabled.
Local + optional cloud AI
- What stays local
- The raw capture store remains on the endpoint unless the user or organization enables export or sync.
- What may leave the device
- Selected prompts, summaries, or context snippets may be sent to the chosen AI provider or confidential route.
- Buyer decision
- Buyer chooses model, provider, retention posture, redaction, and whether local models are required.
Team / enterprise
- What stays local
- Endpoint capture and local history can stay on managed devices under admin policy.
- What may leave the device
- Team reports, sync, admin workflows, exports, connectors, and agent outputs depend on deployment scope.
- Buyer decision
- Buyer defines consent, retention, employee controls, report contents, and admin visibility.
SDK / OEM
- What stays local
- The embedding app defines the storage path, model path, and user-facing privacy controls.
- What may leave the device
- Data movement depends on the partner architecture and the contractually agreed processing path.
- Buyer decision
- Partner owns data-flow design, disclosures, user consent, and downstream model/provider choices.