Comparison

Stereologic vs Skan.ai

Skan.ai and Stereologic both observe real operational work, but they take different approaches to endpoint deployment and how operational context is created and carried downstream.

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Skan.ai in one sentence

Enterprise-scale observation of real work, with a strong Context Graph and a context-for-AI position.

How Skan approaches the problem

Continuous observation of real work across applications, handoffs, exceptions and workarounds.
A privacy-first architecture in which privacy controls are performed locally, and sensitive or raw screen information is not transmitted while abstracted metadata supports platform analysis.
Enterprise scale, with a Context Graph the company describes as built on billions of events across hundreds of processes and over a thousand enterprise applications.
Turning observed work into end-to-end flows, exception paths and operational context.

The structural difference

Where Stereologic is stronger than Skan.

The clearest architectural difference is the footprint required to begin observation, followed by how recognized business context is carried into downstream analysis.

Endpoint footprintStereologic records with no-install, application-agnostic capture rather than a per-employee recorder installation. Skan’s own privacy documentation describes its Virtual Assistant installing directly on employees’ computers, and its current pages describe a lightweight desktop sensor or agent. This is the clearest structural difference between the two.
Business data in contextStereologic uses Compound AI to generate a business-data dictionary, with a process engineer reviewing, correcting and approving that recognition before it is used downstream. Skan describes rich operational context through its Context Graph.

Why it matters to the business. Where desktop software requires extended security, VDI or change-control approval, removing that rollout can materially shorten the path to an operational baseline.

Side by side

Stereologic and Skan, dimension by dimension.

Architecture and operating-model comparison. Competitor entries are drawn from that vendor’s current public documentation.
Dimension	Stereologic	Skan
Employee endpoint	No recorder software installed on observed employee desktops in the validated architecture.	Virtual Assistant installs on employees’ computers; described as a lightweight desktop sensor or agent.
To begin discovery	Observation can start without any system integration.	No API integrations or system changes are required for observation.
Privacy and data control	Privacy is supported on-premises, in the customer cloud, or in a dedicated hosted environment with regional controls. PII and other sensitive data removal is automated.	Privacy controls performed locally on the end point; sensitive and raw screen data is not transmitted, and abstracted metadata is used by the platform.
Breadth and depth of observed work	Cross-application and inside-application observation including screens, data, users’ actions, process and task outcomes, alternate scenarios, handoffs, interruptions and legacy or remote scenarios.	Observes applications and handoffs and captures exceptions and workarounds across real work.
Business data in context	AI recognizes and generates a reviewed and approved business-data dictionary.	Rich operational context, screen-level data extraction and case and context reconstruction.
Results	An AI-enabled operating model — scalable and insight-driven.	Context Graph.

Common questions

Stereologic and Skan

Does Skan.ai require software on employee desktops?

Skan’s own data privacy documentation describes its Virtual Assistant installing directly on employees’ computers, and its current product pages describe a lightweight desktop sensor or agent. Stereologic’s validated architecture does not require a recorder installed on each observed employee desktop, which is the clearest structural difference between the two.

Where do the approaches differ most?

Skan uses a desktop observation component and Context Graph to support large-scale continuous operational observation. Stereologic differs most in avoiding recorder installation on each observed employee desktop and in its deterministic recognition of user system interactions for downstream case evidence.

What is the main difference between Stereologic and Skan.ai?

Endpoint footprint. Both observe real work rather than only system records. Skan deploys observation software to employee computers; Stereologic’s validated architecture uses remote and browser-connected recording instead. Stereologic additionally generates a reviewed, reliable business-data dictionary. Skan provides a Context Graph. Stereologic provides an AI-enabled operating model.

Fit

Where Stereologic fits best

Stereologic fits particularly well where employee-endpoint software is a deployment constraint and the operational analysis needs observed work connected to reviewed business data, case context and timing evidence.

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 written

Written from their documentation, not from our sales deck.

Every statement about another vendor is taken from that vendor’s own current public documentation, and describes architecture rather than quality.
There are no scores, no rankings and no “winner”. Different architectures suit different environments, and the right answer depends on yours.
Competitor capabilities are stated where the evidence supports them. The comparison focuses on architectural and operating-model differences and why those differences matter operationally.
Where event logs already exist and are usable, Stereologic can layer Process Mining on top to extend coverage into the system-recorded layer. They are an addition, not a precondition — discovery does not wait on them.
We do not publish comparisons of pricing, funding or customer counts. They change, and they are not architecture.
If you believe anything here is out of date or unfair, tell us and we will correct it. Contact us.

All 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.

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