Complaint detection
Determine whether an interaction contains a complaint and return a confidence score for downstream review logic.
Complaint Management AI
Turn unstructured interactions into governed complaint cases.
DataKnobs Complaint Management AI detects complaint signals, summarizes conversations, highlights evidence, classifies issues, suggests ownership, and surfaces recurring themes across email, chat, forms, tickets, and call transcripts.
Capabilities
Detection is only the first step.
The product is designed to make complaint data operationally useful while preserving the evidence and review paths needed for high-volume or regulated workflows.
Evidence extraction
Highlight the statements that support the complaint decision, issue category, severity, or escalation signal.
Structured summary
Capture who is affected, what happened, requested remedy, prior attempts, product/service, dates, and key entities.
Taxonomy classification
Map cases into customer-defined product, issue, root-cause, regulatory, operational, or ownership taxonomies.
Routing & human review
Use confidence, severity, policy, and workload rules to route cases automatically or require review.
Theme intelligence
Cluster complaints, monitor emerging drivers, compare products or locations, and surface anomalies for investigation.
Workflow
From raw interaction to action-ready case.
1
Ingest
Email, CRM, tickets, chat, forms, call transcripts, and metadata.
2
Normalize
Thread stitching, language handling, optional redaction, and source metadata.
3
Understand
Complaint signal, summary, evidence, entities, taxonomy, and severity.
4
Route
Owner, queue, SLA, human review, escalation, and next-step workflow.
5
Learn
Use adjudication and outcome feedback to monitor errors and improve knobs.
KONTROLS + KNOBS
Make complaint automation governable.
The operational system should expose the settings that change false negatives, review workload, routing quality, and risk—not bury them inside a prompt.
Controls
- PII handling and access
- Source lineage and evidence
- Human review for sensitive cases
- Audit logs and version history
Knobs
- Complaint threshold
- Taxonomy and severity rules
- Routing confidence
- Sampling and escalation policies
Evaluation
- Detection precision/recall
- Category and evidence quality
- Routing accuracy
- Review burden and workflow outcomes
Output contract
Structured enough for systems. Explainable enough for people.
Case-level output
- Complaint flag + confidence
- Concise summary
- Evidence spans
- Issue/product taxonomy
- Entities, severity, owner, next step
- Model/prompt/policy version metadata
Portfolio-level intelligence
- Volume and rate by product/channel
- Theme and root-cause trends
- Escalation and severity mix
- Review disagreement and error patterns
- Emerging anomalies requiring investigation
FAQ
Complaint AI questions.
What does Complaint Management AI do?
It converts emails, chats, forms, tickets, and call transcripts into structured complaint cases with summaries, evidence, categories, severity, routing suggestions, and trend signals.
How should low-confidence cases be handled?
Low-confidence, high-severity, or policy-sensitive cases should be routed to human review using thresholds defined by the organization.
How is complaint quality evaluated?
Teams should evaluate complaint detection, category accuracy, evidence precision, routing quality, false-negative risk, review workload, and downstream operational outcomes.
Test the workflow on representative complaint data.
A useful pilot should measure detection quality, evidence, taxonomy, routing, human-review load, and the downstream operational outcome—not only a single model accuracy score.
Discuss a Pilot