Skan AI
by Skan AI, Inc. · Process Mining
Zero-integration process and task mining platform that observes work to ground AI agents.
Updated August 2026 · By the ERP Research Editorial Team · Independent and vendor-neutral.
- Deployment
- Cloud
- Company size
- Enterprise
- Pricing
- Quote-based
- Founded
- 2018
Overview
Skan AI is a process and task mining platform, marketed as building a "Context Graph of Work," that observes how enterprise employees actually complete their jobs across desktop, web, mainframe, legacy and office applications. Rather than extracting event logs from ERP or system-of-record APIs the way traditional process mining tools do, Skan deploys a lightweight desktop agent that captures clicks, application switches and handoffs directly at the UI layer, so it can profile work in systems that have no accessible event log, including legacy and mainframe screens.
The captured signals feed three linked products: Process Intelligence, which turns raw activity into process maps, variant analysis, exception paths and time-allocation metrics; Blueprint, which classifies observed work into a "Nature of Work" framework and produces a phased roadmap for where to automate, augment or keep humans in the loop; and Agents, which builds governed AI agents from "Agent Operating Procedures" derived from the observed work, including its exceptions and judgment calls, running on a deterministic workflow engine with human-in-the-loop approval points. A related Engineering Intelligence product applies the same observation model to software delivery teams.
Skan is aimed at large, regulated enterprises, particularly financial services, insurance and healthcare, where it reports customers among the top US banks and insurers. The company states it has processed 25 billion events, mapped 500+ processes, and delivered over $500M in customer value across 100+ enterprise deployments.
Features & capabilities
Process Intelligence & Task Mining
Observes how work actually happens across desktop, web, mainframe and legacy applications without requiring system integrations.
- End-to-end process mapping with variant analysis
- Exception path and rework loop identification
- Time allocation tracking by process, task and application
- Workload distribution and idle capacity analysis
- Context-switching overhead measurement
- Real-time compliance exception detection with root cause analysis
- Cross-team performance benchmarking
- Billions of events processed into process maps and activity classifications
Blueprint (AI Operating Model)
Turns observed work into a phased roadmap for where to apply AI automation or augmentation, and where to keep processes human-driven.
- Nature of Work framework classifying activities into Execution, Comprehension, Creation, Decision and Communication
- Configurable AI posture: Conservative, Moderate or Aggressive automation stance
- Four-phase implementation roadmap from quick wins to enterprise-wide transformation
- Baseline metrics and before/after ROI quantification
- Contextual AI analysis distinguishing identical actions across different tools
AI Agents
Deploys governed AI agents built from Agent Operating Procedures derived from observed human work rather than hand-written SOPs.
- Agent Operating Procedures generated from observed work patterns, including exceptions and edge cases
- Deterministic, fault-tolerant workflow execution engine
- Human-in-the-loop approval and escalation configuration
- Policy, guardrail and system-access controls set through configuration rather than code
- Full action-level audit trail tying every agent action back to the work it was modeled on
- Governed reasoning applied selectively, only where judgment is required
Engineering Intelligence
Applies the same observation-based approach to software delivery teams, tracking developer workflow across coding, collaboration and delivery tools.
- Unified dashboard combining delivery data, workflow activity and collaboration patterns
- AI coding assistant impact tracking (e.g. Cursor) on developer throughput
- Focus time and context-switching measurement
- Root-cause analysis of delivery bottlenecks across distributed teams
- Works with Jira, Git/GitHub, Cursor and other developer tools
- High/low-performing team comparison
Zero-Integration Capture & Governance
Captures work signals directly from the interface layer across essentially any application, avoiding system-integration projects.
- Lightweight desktop agent capturing every click, app and handoff
- Coverage across mainframe, legacy desktop, VDI, web apps, Excel, email and chat
- In-environment processing that keeps each customer in a private cloud environment
- States it never captures content, passwords or keystrokes
- SOC 2 Type II, ISO 27001, GDPR, CCPA, HIPAA-ready and ISO 42001 controls
Common use cases
- Mapping how a back-office or shared-services process actually runs before deciding what to automate
- Building an AI adoption roadmap that sequences quick wins ahead of enterprise-wide agent rollout
- Deploying governed AI agents for loan underwriting, KYC/AML or claims processing built from observed procedures rather than static SOPs
- Identifying automation-ready hours and rework loops in insurance claims or policy servicing
- Benchmarking contact center or shared-services team performance and idle capacity
- Tracking developer productivity and AI-coding-assistant impact across distributed engineering teams
- Running continuous control testing to flag regulatory or compliance breaks before auditors find them
Strengths & considerations
Strengths
- Zero-integration desktop-level observation across mainframe, legacy, VDI, web and office apps, rather than API- or log-based process mining
- Builds AI agents directly from Agent Operating Procedures derived from real observed work, including exceptions and judgment calls
- Nature of Work framework and configurable AI posture (Conservative/Moderate/Aggressive) aimed at regulated industries
- In-environment processing model keeps each customer in a private cloud environment rather than pooled multi-tenant storage
- Deep concentration in regulated, high-compliance sectors, citing customers among 7 in 10 top US banks and 3 in 5 top US insurers
Pricing
Enterprise, sales-led pricing. The public pricing page only offers a custom quote via demo request; no price points or tiers are published. Get an independent shortlist with pricing guidance below.
Technical & security
- Hosting
- SaaS, with each customer in its own private cloud environment
- Compliance
- SOC 2 Type II, ISO 27001, GDPR, CCPA, HIPAA-ready, ISO 42001
About the vendor
- Founded
- 2018
- Ownership
- Private, venture-backed (Series C, 2026: $63M co-led by Cathay Innovation and Dell Technologies Capital; investors also include Citi Ventures, Bloomberg Beta, Zetta Venture Partners, GSR Ventures, Liberty Global, State Farm Ventures and Wipro Ventures)
- Notable customers
- Unum, Definiti, Mitie
Alternatives to Skan AI in Process Mining
Skan AI — frequently asked questions
Does Skan AI require integrating with my ERP or other enterprise systems?
No. Skan AI captures work at the desktop/UI level through a lightweight agent, observing mainframe, legacy desktop, VDI, web and office applications without requiring API integrations or an IT project.
What does Skan AI's Blueprint product do?
Blueprint analyzes observed work to classify activities (Execution, Comprehension, Creation, Decision, Communication) and produces a phased AI adoption roadmap with a configurable automation posture (Conservative, Moderate or Aggressive), plus before/after ROI baselines.
How does Skan AI build its AI agents?
Agents are built from Agent Operating Procedures generated from observed human work, including exceptions and judgment calls, and run on a deterministic workflow engine with human-in-the-loop approval points and a full action-level audit trail.
How is Skan AI priced?
Skan AI does not publish pricing. It is sold on an enterprise, quote-based model through a sales-led demo process.
What compliance certifications does Skan AI hold?
Skan AI states SOC 2 Type II, ISO 27001, GDPR, CCPA, HIPAA-ready and ISO 42001 (AI management) controls, and hosts each customer in a private cloud environment.
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