Key Highlights
AI triage that learns your taxonomy and it recommends the right Category, Technician Group, Knowledge Base article and Response Template the moment a request is created. Smart Suggestions at a Glance Motadata ServiceOps Smart Suggestions trains an AI classifier on your own request history, not generic guesses, to route tickets accurately from the first request inside an ITIL 4-aligned platform.
Everything a technician needs to act, before investigation begins.
Category recommended at creation, from patterns in your own request history.
Technician Group best suited to the request type, surfaced automatically for first-time-right routing.
Knowledge Base article matched to the issue and offered at triage, to speed resolution.
Response Template aligned to request content, in hand, compressing time-to-first-response.
Learn your taxonomy and routing from requests already in your system.
Trained on your Request Management module's actual request history, not everyone else's tickets.
Each category needs at least 50 requests to qualify.
Categories below that threshold are excluded from training.
Training balances eligible categories, so no single one dominates and skews the model.
Only requests created after training are evaluated, keeping the model honest.
Present suggestions only when the model is sure enough, and record each change.
A configurable Accuracy Level governs the suggestions.
They appear only when confidence clears your threshold.
Retraining refreshes the model as taxonomy, categories, and team structure evolve.
Field suggestions and resolution suggestions are trained independently, for scoped control.
Each train-and-update action is recorded in the Configuration Audit log.
Scope AI triage to where classification volume and routing error concentrate.
Applies to the Category and Technician Group fields, plus Knowledge Base and Response Template resolution.
Configured under Admin > Automation > Smart Suggestions, separate from the Text Intelligence AI section.
Focused on the Request module, where intake volume makes manual classification slowest.
A trained classifier, distinct from the embeddings-based Similarity matching in other records.
Replace experience-dependent triage with classification that holds steady for all technicians.
A new hire's first ticket is classified as accurately as a ten-year veteran would.
Routing stays consistent throughout shifts, sites, and skill levels, not individual judgment.
Reassignments and queue bouncing fall away when the right team is recommended at creation.
Similarity has already surfaced related and duplicate records, so triage decisions arrive pre-informed.
Intelligence
Manual classification stands in front of each resolution. A technician reads the description, decides the category, picks the team, and hopes both are right. Triage quality tracks experience, so a veteran categorizes on instinct while a new hire second-guesses any choice, creating inconsistency that compounds as the desk scales.
Smart Suggestions removes the guess. The classifier is trained on your own history, so a new technician sees the recommendation a veteran would have made. Retraining runs automatically as taxonomy and team structure evolve.
How It Works
Enable Field Suggestion under Admin > Automation, selecting target fields and resolution options.
Set an Accuracy Level threshold to define minimum confidence required before suggestions display.
Train Model on request history, pooling categories that clear the 50-request training minimum.
Predict Category and Technician Group for new requests based on subject and description text.
Show matching Knowledge Base articles and Response Templates based on content alignment.
Evaluate post-training requests automatically. Retrain as taxonomy and team structure evolve.
Each new ticket classified at creation, by a model that learned how your team has always routed work.
Role-Based Value
Triage accuracy no longer depends on which technician opens the ticket.
Triage accuracy no longer depends on which technician opens the ticket.
Eliminate the reassignments and queue bouncing that pile up when classification is left to judgment.
Eliminate the reassignments and queue bouncing that pile up when classification is left to judgment.
Open a request and find the right Category, Technician Group, Knowledge Base article, and Response Template already surfaced.
Open a request and find the right Category, Technician Group, Knowledge Base article, and Response Template already surfaced.
Govern AI triage with controls you set and evidence you can show.
Govern AI triage with controls you set and evidence you can show.
From Visibility to Control
The right Category and Technician Group get recommended at creation, so tickets route correctly the first time.
New technicians triage with the accuracy of seasoned staff, because the model encodes how your team has always classified work.
The supporting Knowledge Base article and Response Template are in hand at intake, compressing time-to-first-response.
The highest-friction moment, deciding where a ticket belongs, is resolved before investigation begins.
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