Skan AI
by Skan AI
A context graph of how work actually gets done, so enterprise AI agents can execute against reality
Skan AI observes how employees actually move work through enterprise applications and turns that into a continuously updated model of business operations. It sells that context as the missing input for AI agents, targeting large regulated enterprises whose documented processes and system logs do not reflect what people really do day to day.
Skan AI is an enterprise process-intelligence platform, founded in 2018 by IIT classmates Avinash Misra and Manish Garg and headquartered in Menlo Park, California, that builds what the company calls a context graph of work. Rather than reading system logs or trusting documented procedures, Skan observes employee desktops across every application a task touches — the CRM, the email client, the mainframe terminal — and distills those observations into a continuously updated model of how a business actually runs, which it then supplies as execution logic to AI agents. The platform ships as four connected products: Blueprint, which scans operations including legacy environments and produces sequenced automation recommendations with ROI justification; Process Intelligence, which turns observed activity into living process maps and surfaces bottlenecks; Engineering Intelligence, which extends the same visibility across software development tooling beyond repository-level analytics; and Agents, work-aware agents deployed with operational context and governed by what Skan calls Agentic Operating Procedures. Capture requires no integration work and processing happens inside the customer's own perimeter, which is how Skan sells into regulated buyers, and the stack is built on NVIDIA AI Enterprise and NVIDIA NIM microservices. On August 12, 2026 the company raised a $63 million Series C co-led by Cathay Innovation and Dell Technologies Capital, taking total funding to roughly $120 million, and disclosed 300%-plus year-over-year revenue growth across two consecutive years, 150% net dollar retention, 25 billion processed work signals, and a customer base spanning one in four Fortune 50 companies, seven of the ten largest US banks and three of the five largest US insurers.
COOs and transformation leaders at large regulated enterprises — banks, insurers, healthcare payers — who have tried to deploy AI agents into back-office operations and found that documented processes and system logs do not describe what staff actually do.
An observed, evidence-backed map of real work that both prioritizes which processes are worth automating and supplies the operational context agents need to execute them.
At a Glance
- Category
- Automation & Workflows
- Pricing
- Subscription, Contact for pricing
- Target Market
- CIOs, COOs, CTOs, Heads of Operations, Transformation Leaders
- Deployment
- Cloud-first, Self-hosted, Hybrid
- Founded
- 2018
- Headquarters
- Menlo Park, United States
- Customers
- 1 in 4 Fortune 50 companies, 7 of the top 10 US banks, 3 of the top 5 US insurers
Key Features
- ✓Zero-integration desktop capture
Observes activity across applications without APIs or connectors, which is what makes legacy and mainframe processes visible at all.
- ✓Context Graph of Work
A continuously updated model of real task flow that agents consume as execution logic rather than as static documentation.
- ✓Blueprint opportunity discovery
Scans operations and returns sequenced automation recommendations with ROI justification, so prioritization is evidence-based rather than anecdotal.
- ✓In-perimeter processing
Observation data is processed inside the customer environment and does not leave the firewall, which is the precondition for banking and insurance deployment.
- ✓Agentic Operating Procedures
Governance layer constraining what deployed agents may do, with human oversight and an auditable record of agent actions.
- ✓Engineering Intelligence
Applies the same observation approach to development toolchains, surfacing workflow drag that repository-level metrics miss.
- ✓Process benchmarking
Measures cost per transaction and throughput continuously, so post-deployment gains are measured against an observed baseline.
Capabilities
Use Cases
- •Anti-money-laundering case triage
At one customer, AI now handles 60% of AML cases after Skan mapped how analysts actually worked through them.
- •Banking transaction cost reduction
A top US bank reported 32% lower cost per transaction, 41% higher throughput and $18 million in annual savings.
- •Automation opportunity assessment
Definiti used Skan to identify more than 14,000 hours a year of automation-ready effort across its operations.
- •Insurance claims process redesign
Observed claims handling reveals rework loops and system switching that documented procedures and logs both omit.
- •Post-deployment agent governance
Agentic Operating Procedures constrain and audit agent behaviour once processes move from human execution to autonomous handling.
Ideal For
Best For
- ✓Identifying which back-office processes are genuinely automation-ready before committing to an agent build
- ✓Operations in legacy or mainframe environments where no API or event log exists to mine
- ✓Regulated industries needing in-perimeter processing because desktop activity data cannot leave the enterprise firewall
- ✓Building a defensible ROI case for AI investment, with observed baselines rather than estimated ones
- ✓Continuous benchmarking of process cost and throughput after agents are deployed
- ✓Extending process visibility into software engineering workflows beyond what repository analytics show
Not Ideal For
- ✗Organizations without the appetite or works-council standing to deploy desktop observation software — this is employee activity capture, and in Europe it required works council approval
- ✗Small and mid-market companies: pricing is enterprise-negotiated with no public list price, and the customer base is Fortune 50 banks and insurers
- ✗Teams whose processes already run through well-instrumented modern SaaS with clean event logs, where conventional process mining on system data is cheaper
- ✗Buyers unwilling to confront workforce impact — the CEO has confirmed the technology has led some customers to cut headcount in specific processes
- ✗Anyone wanting a self-serve or short-cycle evaluation, since deployment is a structured enterprise engagement
Deployment
Market & Ratings
1 in 4 Fortune 50 companies, 7 of the top 10 US banks, 3 of the top 5 US insurers
Market Analysis
Pros
- ✓Sees work that log-based process mining cannot, because it observes the desktop rather than system events
- ✓Deep penetration of the hardest enterprise segment — a quarter of the Fortune 50 and seven of the ten largest US banks
- ✓Strong commercial signal with 150% net dollar retention and 300%-plus revenue growth over two consecutive years
- ✓In-perimeter processing removes the data residency objection that usually stalls this category in banking and insurance
- ✓Backed by strategic investors from its own customer base — Citi, State Farm, Dell and Wipro
Cons
- ✗It is employee activity monitoring, and the surveillance tension is real: European deployments required works council approval and opt-in application scoping
- ✗The CEO has confirmed the platform has led some customers to cut headcount, including an AML operation where AI now handles 60% of cases
- ✗The headline $500 million in customer value is identified opportunity, not uniformly realized savings — a distinction the company's own coverage makes
- ✗No public pricing at all, so buyers cannot size an engagement without entering a sales cycle
- ✗Almost all published metrics are company-supplied; no G2, Capterra or TrustRadius rating was retrievable, so independent user sentiment is thin
- ✗Competes against Celonis, which dominates enterprise process mining with a broader ERP and CRM integration footprint
Pricing
Enterprise
Contact for pricing
- ✓Blueprint
- ✓Process Intelligence
- ✓Engineering Intelligence
- ✓Agents
- ✓In-perimeter deployment
- ✓Enterprise security and compliance
Skan publishes no list pricing anywhere on its site and G2 records none either; every deal is an enterprise-negotiated subscription, which is consistent with a customer base of Fortune 50 banks and insurers. Expect scoping by number of observed users and processes plus a structured deployment engagement — the company markets six weeks or less from first observation to a deployed agent, which implies professional services are part of the cost.
Security & Compliance
Sources
This page was written from 4 sources, 2 on domains other than skan.ai.
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