This page compares the two approaches on architecture, prerequisite setup and the path from observed task data to operational analysis.
The key difference is how much task-mining setup and processing is required before the resulting evidence is available for downstream analysis.
Why it matters to the business. For an operations team trying to establish an evidence-based baseline before making a broader platform decision, endpoint installation, project-specific collection configuration and event-log processing are additional prerequisite steps. Stereologic’s validated architecture removes the per-employee recorder requirement and carries approved recognition outputs into downstream case reconstruction.
| Dimension | Stereologic | Apromore |
|---|---|---|
| Employee endpoint | No-install, application-agnostic recording. No recorder software installed on observed employee desktops in the validated architecture. | Agents are PCs that have installed Apromore’s digital agent before they join a task-mining project. |
| Before collection begins | Observation can start without first building source-system log integration, and without process- or application-specific setup. | A data collection configuration file is expected to be developed for a given task-mining project. |
| Business data | AI recognises business data and its values as work is observed and generates a data dictionary. A process engineer reviews and approves it before downstream use. | The data processing rules in the configuration file determine which tagged data items are captured. |
| From capture to analysis | Approved recognition outputs are associated to the client’s case identifier and assembled into complete case traces and end-to-end variants. | Raw agent data may lack case ID, activity and end timestamp, so it cannot be used as an event log directly; it must first be processed into event-log form. |
| Case reconstruction | Cases are reconstructed from discovered data, including fragmented work without explicit start and stop triggers, across systems and teams. | Apromore groups the event logs based on the identifier set in the configuration. |
| Process maps | Process maps are built from discovered cases. Every step is linked to its application, screen, action, screenshot and business data. | Process Discoverer displays the 250 most frequent nodes and the 1,000 most frequent arcs; above that, the map is abstracted for display. |
| Exports and documentation | SOP and Process Definition Documents with screenshots and a data dictionary; BI-ready datasets; Visio, XPDL, BPMN and Blue Prism-compatible formats. | A Process Definition Document can be generated directly from a task-mining project. |
| AI and assistants | AI recognition runs inside the approved deployment environment. Where AI is used, you choose whether American or international models are applied, or omit the AI layer entirely. | Copilot is disabled by default; once enabled the default limit is 20 prompts per day, with a maximum of 10 filters per prompt, and it cannot answer on attributes with more than 50 distinct values. |
| Commercial packaging | A Process Mining module is included and integrated with Deep Task Mining™. | Task mining and process mining are documented as separate capabilities within the platform. |
Salesforce completed its acquisition of Apromore on 3 November 2025. Any comparison that describes Apromore as an independent vendor is out of date.
Yes. Apromore has a Digital Agent that is installed on participating PCs and records interface, window, focus and copy-paste data according to project configuration. The collected raw data is then processed into event-log form for analysis in Process Discoverer.
Apromore task mining requires the Digital Agent installed on participating PCs plus project-specific data-collection configuration, and its raw task data must be processed into event-log form before analysis. Stereologic’s validated architecture requires no recorder on observed employee desktops and assembles approved recognition outputs into case traces directly.
Apromore combines process and task mining with simulation, digital twins, root-cause analysis and compliance/control capabilities. Stereologic differs in the prerequisite path to operational discovery: its validated architecture does not require recorder software on each observed employee desktop, and its approved recognition outputs feed downstream case reconstruction without first requiring the same task-data-to-event-log processing path.
Stereologic fits particularly well when the immediate objective is to establish detailed operational evidence with fewer task-mining prerequisites, then connect observed work to business context, cases, timing and operational value before deciding what to optimize or automate.
If that is the question in front of you, the 30-Day Value Leakage Audit is the defined way to establish that evidence before a broader platform decision.
Competitor evidence reviewed 4 September 2026. We re-check these sources periodically; vendor documentation changes.
How this page is writtenAll product names and trademarks are the property of their respective owners. Reference to them here is for identification and comparison only and does not imply any affiliation or endorsement.
Bring the operational scope you are trying to answer and the constraints you have to work inside — endpoint policy, integration backlog, timeline. We will tell you plainly whether this is a fit, and where it is not.
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