Blog post
2 mins
Ground AI agents in rich context
Build based on verified work instead of outdated SOPs or assumptions.
Close context and governance gaps
Use process intelligence to close decision-trace gaps at the source.
Prove business impact before scaling
Quantify the opportunity and initial impact before deploying agents at scale.
Build a complete view of today’s work to power successful AI agents
Capture all the undocumented decisions, prioritize your AI investments
- Scan work and classify every activity by nature of work.
- Detect process variants and see the gap between SOPs and reality.
- Create an AI transformation roadmap, prioritized by impact.
Turn decision traces, exception logic, and hidden knowledge into your Context Graph of Work
Capture decision traces so agents don't have to escalate each exception.
Define exception handling rules in agentic operating procedures.
Train agents on constantly updated playbooks instead of static rulebooks.
Give agents complete operational context, so they’re measured and governed from day one
- Maintain a fully traceable, auditable decision trail.
- Establish new baselines for agentic processes over time.
- Feed every outcome back to agents so they continuously improve.
Frequently asked questions
A knowledge graph models entities and their relationships, and a context graph is a type of knowledge graph focused on mapping information relevant to specific AI agents and their workflows. Skan AI creates the Context Graph of Work for each of its customers by incorporating the process flows, decision traces, and exception logic extracted from direct work observation within that organization.
No, using Skan AI actually solves the “cold-start” problem many organizations are running into with AI. Observing and analyzing real work first allows companies to identify the best automation opportunities and gather the right context before deploying a single agent. This sets up organizations with complex processes and regulations for success with AI transformation and investment.
The Skan AI platform gives your transformation office an observed, dollar-quantified baseline to prioritize against. Instead of relying on incomplete work visibility and operational context from SME interviews, system logs, or documentation, your transformation team can ground its efforts with complete insight into how work gets done today.
