Companies turning enterprise tribal knowledge into agent-usable context

1. Scribe — Automatically turns screen recordings and observed workflows into step-by-step process documentation that can be searched, shared, and reused by humans or AI agents. Why it fits: Founded in 2019 and venture-backed, with broad adoption across operations, support, IT, and customer-facing teams; a strong example of capturing tacit “how we do this” knowledge at the moment work happens.
2. Tango — Captures workflows while employees perform them and converts clicks, screenshots, and instructions into reusable guides. Why it fits: An early venture-backed company focused specifically on extracting procedural knowledge from employee behavior rather than waiting for someone to write an SOP.
3. Guidde — Uses AI to create narrated, visual how-to documentation from software workflows and screen recordings. Why it fits: Venture-backed and still relatively young; its product converts expert demonstrations into structured training and support context that can potentially be consumed by copilots and agents.
4. Dubble — Records a user’s workflow and automatically produces written instructions, screenshots, and process documentation. Why it fits: Early-stage workflow-documentation company aimed at reducing the loss of operational knowledge when processes are learned informally or owned by a small number of employees.
5. Trainual — Provides a system for documenting company processes, role expectations, onboarding material, and standard operating procedures. Why it fits: A growing, independently known company with substantial adoption among small and midsize businesses; its core asset is turning founder and employee know-how into explicit, reusable operating context.
6. Tettra — Provides an internal knowledge base with integrations to Slack and other workplace tools, including AI-assisted answers over company documentation. Why it fits: An earlier-generation but focused knowledge-management company that has stayed close to the problem of making distributed team knowledge searchable and available at the point of need.
7. Slite — Combines collaborative documentation with an AI assistant that answers questions over a company’s internal knowledge. Why it fits: Venture-backed and product-led, with a clear transition from traditional team wiki toward an AI-readable source of truth for company-specific context.
8. Guru — Captures, verifies, and surfaces internal knowledge directly inside workplace workflows such as Slack, browsers, and business applications. Why it fits: More mature than the other companies on this list, but one of the strongest commercial signals in the category: a substantial enterprise customer base and a product explicitly designed to turn scattered institutional knowledge into contextual answers.
9. Stonly — Helps companies build interactive guides and knowledge experiences for employees and customers, including contextual in-product support. Why it fits: Venture-backed and focused on converting support and operational expertise into guided, structured decision paths rather than static documents—useful primitives for future agents.
10. ScreenSteps — Lets support and operations teams create structured internal procedures and gives agents or employees guided access to those procedures while working. Why it fits: A real, established specialist in procedural knowledge for contact centers and support teams; its domain-specific structure is often more agent-usable than unstructured wiki content, even though it is less hyped than newer AI-native startups.

White-space gaps noticed:

• Knowledge capture from exceptions and judgment calls: Most products document the “happy path.” There is still room for systems that capture why an expert deviated from the process, what evidence they considered, and when the exception should apply—high-value context for reliable agents.
• Continuous, permission-aware operational memory: Many tools create documents or answer questions, but fewer continuously reconcile Slack, tickets, calls, browser activity, and system-of-record changes into a versioned knowledge graph with ownership, provenance, freshness, and granular permissions.