About Knowledge Orchestration
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What is Knowledge Orchestration?
Knowledge Orchestration is an intelligence layer that transforms raw customer conversations and portal search logs into a prioritized, auditable backlog of knowledge work. It continuously surfaces what customers are asking, measures how well your portals are answering those questions, and gives teams a clear, evidence-backed path to close the gaps that matter most. Knowledge Orchestration also analyzes your existing taxonomy and suggests missing topics, so your content structure stays aligned with customer needs.
Knowledge Orchestration works by ingesting transcripts and search logs, clustering questions by intent, selecting a single canonical phrasing for each cluster, and then evaluating your portal against every canonical question to capture confidence scores, source references, and answer snippets. Where answers fall below the confidence threshold or are flagged by a reviewer, Knowledge Orchestration marks a gap and adds it to the queue for resolution.
Access to Knowledge Orchestration is governed by existing Knowledge Base roles and does not introduce new roles or permissions. Knowledge Managers can create jobs, configure settings, and edit canonical questions. Authors can view jobs, canonical questions, answers, gaps, topics, cluster maps, and quadrant maps, and can generate answers and articles, and flag and star canonical questions, but cannot create jobs or modify settings.
Why Knowledge Orchestration?
Content priorities have traditionally been driven by anecdotes rather than demand. Organizations collect large volumes of calls, chats, and emails but lack a systematic way to extract what those interactions are actually asking. At the same time, portals often answer inconsistently, and there is no reliable method to measure answer quality or prove improvement over time.
Knowledge Orchestration addresses these problems directly. It produces a manageable surface area of high-value canonical questions, typically 100 to 200 per domain, ranked by a combination of question volume, business value, and documentation complexity. Every priority is traceable to observed demand and inspectable evidence, so authors spend their time on work that demonstrably closes measured gaps rather than responding to the loudest voice in the room.
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