| AI knowledge compiler | Converts resolved cases, threads, and docs into draft articles with consistent structure. | Turns day-to-day work into reusable guidance without extra writing. |
| Auto-publish rules | Publishes immediately when sources meet thresholds for recency, agreement, and sensitivity; otherwise queues for approval. | Keeps the library fresh while protecting high-risk topics. |
| Source-linked outputs | Every generated article carries citations back to tickets, files, or records. | Readers can verify facts; editors can audit provenance. |
| Template-aware drafting | Chooses the right template (FAQ, how-to, policy, troubleshooting) and fills required fields. | Produces content people can scan and act on. |
| De-duplication & canonicalization | Detects overlaps, recommends merges, and links to the canonical article. | Prevents fragmenting the knowledge base over time. |
| Terminology normalization | Maps synonyms, acronyms, and internal shorthand to approved terms. | Improves searchability and keeps language consistent. |
| Redaction & scope controls | Masks sensitive fields and limits sources by role, program, or record type. | Protects confidentiality while compiling knowledge. |
| Scheduled refresh & reviews | Sets review dates and auto-refreshes articles when upstream sources change. | Keeps guidance current without manual sweeps. |
| Translation & variants | Generates localized versions with translator workflows and side-by-side diffing. | Makes knowledge accessible for distributed teams. |
| Impact analytics | Measures deflection from tickets, zero-result reduction, and article satisfaction. | Shows measurable ROI and where to improve content next. |
| Inline “promote to article” | One click from a resolved ticket or Q&A thread to create or update a knowledge entry. | Captures answers at the moment they’re proven to work. |
| Human-in-the-loop options | Require approval for certain categories, auto-publish others; keep full change history. | Balances speed with oversight and audit needs. |
