Skan AI's 'Context Graph' Aims to Ground Enterprise AI in Reality

MENLO PARK, Calif. – May 20, 2026 – As enterprises race to deploy artificial intelligence, a fundamental challenge persists: how to make AI agents reliable enough to handle complex, real-world business operations. Menlo Park-based Skan AI is gaining significant industry attention for its proposed solution, securing a trifecta of recognitions from technology research firm Gartner this month.

The company, which calls itself the "enterprise context graph company," received an Honorable Mention in the May 2026 Gartner® Magic Quadrant™ for Process Intelligence Platforms. It was also named a Representative Vendor in the 2026 Gartner Market Guide for Task Mining Tools and featured in the firm's recent Innovation Insight report on AI Agent Mining. This multi-faceted acknowledgment highlights Skan AI's unique approach to a foundational problem: understanding how work actually gets done.

"By being recognized in both the Magic Quadrant for Process Intelligence Platforms and the Market Guide for Task Mining Tools, it's clear to us that we are filling a capacity gap no one else is addressing," said Avinash Misra, Skan AI's co-founder and CEO, in a statement. "We believe this recognition verifies Skan AI's unique end-to-end approach to a foundational technology: continuous, screen-level observation of how complex, business-critical work actually runs across teams and systems."

Beyond the Buzz: Decoding Gartner's Recognition

For technology buyers, a nod from Gartner is a significant market signal. While Skan AI was not placed as a Leader in the highly competitive Process Intelligence Magic Quadrant—a quadrant dominated by established players like Celonis and SAP Signavio—its "Honorable Mention" is noteworthy. It suggests the company is an emerging force with a relevant offering that warrants close attention from enterprises evaluating their automation strategies.

Simultaneously, being named a "Representative Vendor" in the Market Guide for Task Mining Tools places Skan AI squarely within a market projected to surge from $2 billion in 2025 to $10 billion by 2033. Market Guides are often used for emerging or fragmented markets, and this designation validates the company's solution as a key example of the technology available. The inclusion in an Innovation Insight report on AI Agent Mining further cements its position as a pioneer in what many see as the next frontier of enterprise automation.

Taken together, the recognitions paint a picture of a company bridging the gap between two established categories—process and task mining—while pioneering a new one focused on providing the essential groundwork for AI agents.

The 'Context Graph': Mapping the Unseen Work of an Enterprise

At the heart of Skan AI's platform is the "enterprise context graph." This technology moves beyond the limitations of traditional process mining, which primarily analyzes event logs from backend systems. While useful, log-based approaches often miss what analysts call "dark data"—the vast amount of work that happens between applications, in spreadsheets, emails, and through human decision-making.

Skan AI tackles this by deploying a lightweight sensor that performs continuous, screen-level observation of employee workflows across every application, including legacy mainframes and virtual desktops. This "observation-first" approach requires no direct system integrations, a significant advantage that simplifies deployment and reduces IT overhead. It silently captures every click, keystroke, and screen change, creating what the company describes as a "digital twin of operations."

This vast stream of work telemetry is then used to build the context graph—a living, dynamic map of how people, processes, applications, and business rules interact. Unlike a static process map, the graph understands nuance: why a certain exception was made, the sequence of approvals required for a non-standard task, and the informal workarounds teams develop to be efficient. It captures the decision lineage, providing a rich, historical record of operational reality.

From Observation to Automation: Powering Reliable AI Agents

This detailed understanding of work is not merely for analysis; it's the raw material for building and deploying smarter, more reliable AI. This is where Skan AI's vision of "AI for AI enablement" comes into focus. The company's platform is designed to solve the "cold-start problem" that plagues many AI agent initiatives. Instead of being trained on static manuals or incomplete logs, Skan's AI agents learn from the context graph, which is grounded in thousands of hours of real human expertise.

The platform uses a hybrid approach called neurosymbolic AI, which combines the pattern-recognition capabilities of neural networks with the logical, rule-based precision of symbolic AI. This allows the system to generate auditable "Agentic Operating Procedures." The result is an AI that can not only perform tasks but also understand the context and reasoning behind them, making it more transparent and trustworthy, a critical requirement in highly regulated industries like banking and healthcare.

By capturing deep decision traces and exception-handling patterns, Skan AI aims to create agents that are less prone to the "hallucinations" or errors common in purely generative AI models. These context-aware agents can execute complex workflows, adapt to changes, and handle novel situations by referencing how similar issues were resolved by human experts in the past.

The Bottom Line: Translating Context into Corporate Savings

The ultimate test for any enterprise technology is its impact on the bottom line. Skan AI reports that its approach is delivering substantial returns for some of the world's largest organizations. The company is trusted by 7 of the top 10 U.S. banks and one in four Fortune 50 organizations.

According to its press release, customers have realized more than $130 million in savings for a global healthcare organization, $110 million for a top U.S. banking institution, and $100 million for a leading insurance carrier. While these specific figures are company-reported, they align with published case studies showing multi-million dollar savings from identifying process inefficiencies, reducing non-value-added activities, and optimizing complex workflows like insurance claims processing.

These savings are achieved through a combination of process transformation, risk mitigation, and by providing a clear roadmap for agentic AI enablement. By focusing on the true economic impact of process friction, rather than just task frequency, the platform helps businesses prioritize automation efforts where they will deliver the most value. As organizations move from ambition to production with their AI strategies, this ability to ground autonomous systems in the observable reality of daily work may prove to be the critical foundation for success.