14,000+
hours/year of automation-ready effort
Before, it was turn on AI and hope. Now we know exactly which processes to automate, what we'll save, and how to prove we got there.
Most AI agents are trained on generic data or manually written playbooks. The result is an agent that knows the happy path and nothing else.
Skan AI Agents are built on reality, not assumptions
The ideal path, identified from real work
Skan AI observes how your best teams handle decisions, exceptions and complex workflows. From that evidence, it identifies the ideal path for an agent to execute and builds the agent around it.
You control the agent
Define where humans review, approve or take over. Set policies, guardrails and system access, all through configuration that stays under your control.
Built and tested for mission-critical scale
Skan AI agents execute repeatable work deterministically, with governed reasoning applied only where judgement is required. Durable execution keeps workflows resilient as they scale.
From real work to working agents
A lightweight sensor captures real human work across every app. The shortcuts, the exceptions, the judgement that never makes it into an SOP.
Deploy agents for
From observation to automation
Turn observed work into
the ideal path for an
agent to execute
Deploy agents with controls you define
Monitor every agent action, escalation and decision path
Every action traceable to evidence
Real-time monitoring
Compliance controls built in
Built and tested for workflows the business cannot afford to drop
Deterministic-first execution
Governed reasoning, only where it's needed
Durable, fault-tolerant workflows
Agents, purpose-built for regulated industries
Frequently asked questions
Successful enterprise AI agents are built on a deep understanding of real operational workflows, business rules, decision paths, and exception handling. Agents perform best when they are grounded in how work actually happens rather than relying solely on documentation or predefined process maps.
An Agent Operating Procedure is a structured model of how an AI agent should perform a workflow, including the steps, decisions, rules, and exception paths required to complete work safely and effectively. It serves as the operational blueprint for agent execution.
AI governance typically includes human oversight, approval workflows, access controls, auditability, policy enforcement, and operational guardrails. Effective governance ensures agents operate safely while remaining accountable to business and compliance requirements.
Human-in-the-loop AI is an approach where people remain involved at key decision points, approvals, or exception scenarios. Rather than fully replacing human expertise, it combines automation with oversight to improve accuracy, trust, and operational control.