Use this package
The config below is ready to use as-is — download or copy it and pass it as theconfig payload in a Velma Triage request. Its behaviors are preset references: the API expands each preset:<identifier> into its full definition at request time, so you don’t need the criteria inline to run the package.
Download customer-retention.json
View / copy full config
View / copy full config
Expand the full criteria
To produce a self-contained config with every behavior’s full criteria inlined — for review, customization, or pinning a snapshot — fetch the live preset catalog and merge it into the downloaded config. The catalog is the source of truth for detection criteria.customer-retention.full.json keeps the same conversation_types and participant_roles and replaces each preset reference with its full behavior definition — drop it into the config payload exactly like the preset version.
Conversation types
The interaction contexts this package expects to see.| Name | What it is |
|---|---|
| Enterprise IT support | Any call to assist an employee in accessing or managing internal IT resources |
| General Customer Support Call | Any call where a customer is contacting a business to assist them with the use of a service or goods provided by the business |
| Finance Account Support | Any call to support the configuration, management, or cancellation of financial accounts |
| Consumer Technical Support | Any call to assist a customer in using a business’s software or website |
| Insurance Policy or Claims Management | Any call to check on the status of, change, cancel, or check on the status of a claim for an insurance policy. |
| Retail Customer Support Call | A call to coordinate the delivery or transfer of goods to a consumer |
Participant roles
The speaker roles the package distinguishes.| Name | What it is |
|---|---|
| Support Specialist | Someone who works with employees or contractors to solve problems with things like IT, logistics, or communication |
| Employee | someone who is employed by company and exists in professional settings |
| Customer Service Representative | A representative from a business who is assisting a customer with an issue they are having |
| Insurance Agent | A customer support representative from an insurance company |
| Customer | The recipient of a service or good |
Behaviors
The 23 signals this package detects. Each maps to a reusablepreset:<identifier> you can drop into the behaviors array of any BatchConfig — the config above already references them.
Full detection criteria are not duplicated here. The live preset catalog is the source of truth — retrieve the exact criteria for any behavior by name from the
list-presets endpoint.| Behavior | What it detects | Preset |
|---|---|---|
| Complaints | Customer expresses dissatisfaction or grievance. We detect this through elevated volume, sharp intonation, frustration markers, accelerated pacing, and emotional intensity. | preset:complaints |
| Vishing | Attempts to elicit sensitive information through deceptive voice interactions. We detect vishing based on abnormal call pacing, probing question patterns, stress-induced pitch shifts, and background noise suggesting call centers or spoofed environments. | preset:vishing |
| Account Impersonation | Fraudulent attempt to access another’s account. We detect this through identity inconsistencies, rehearsed responses, stress-induced vocal shifts, and abnormal verification behavior. | preset:account_impersonation |
| Action Plan Created | Explicit agreement on next steps or follow-up. We detect this through structured enumeration, decisive tone, slowed pacing, confirmation cues, and reduced ambiguity in delivery. | preset:action_plan_created |
| Cancelled Order | Customer cancels a previously placed order. We detect this using finality in tone, decisive pacing, administrative phrasing, and reduced emotional engagement after confirmation. | preset:cancelled_order |
| Service Churn | Customer decides to cancel an ongoing service. We detect this through resignation tone, conclusive phrasing, disengaging cadence, and emotional withdrawal. | preset:service_churn |
| Off-topic Discussion | Conversation largely unrelated to call purpose. We detect this using semantic drift paired with relaxed pacing, reduced task-oriented urgency, and tonal divergence from initial intent. | preset:off_topic_discussion |
| Coercion Manipulation | Social engineering through intimidation or threats. We detect this using dominance-oriented tone, reduced empathy markers, pressure timing, and aggressive pacing. | preset:coercion_manipulation |
| Return Fraud Attempt | Fraudulent product return behavior. We detect this using scripted explanations, defensive tone, timing irregularities, and emotional mismatch with stated circumstances. | preset:return_fraud_attempt |
| Feigned Ignorance | Pretending towards ignorance to garner fraudulent sympathy. We detect this using inconsistent knowledge signals, exaggerated confusion tone, strategic pauses, and implausible vocal uncertainty. | preset:feigned_ignorance |
| Bargaining Manipulation | Social engineering through cajoling and persuasion. We detect this through pressure-based tone, strategic silence, inconsistent emotional signaling, and coercive pacing changes. | preset:bargaining_manipulation |
| Pre-established Professional Relationship | Evidence of prior interaction between speakers. We detect this through shorthand references, reduced formalities, synchronized turn-taking, familiarity in tone, and absence of introductory framing. | preset:pre_established_professional_relationship |
| Rapport Building | Positive alignment forming a professional relationship. We detect this through reciprocal tone matching, affirming backchannels, relaxed pacing, and increasing conversational ease. | preset:rapport_building |
| Customer Gratitude | Customer expresses satisfaction or appreciation. We detect this using positive emotional tone, softened volume, upward inflection, reduced tension markers, and closing politeness cues. | preset:customer_gratitude |
| Inclusive Practices | Respectful language promoting inclusion and equity. We detect this through careful word choice reinforced by respectful tone, measured pacing, non-dismissive intonation, and calm emotional delivery. | preset:inclusive_practices |
| Unaddressed Question | Failure to adequately respond to a posed question. We detect this through avoidance pauses, topic-shifting intonation, increased filler usage, and prosodic signals of deflection. | preset:unaddressed_question |
| Refund or Credit Issued | Confirmation that financial remediation occurred. We detect this using transactional tone, formal cadence, confirmation phrasing, system-interaction pauses, and reduced customer tension. | preset:refund_or_credit_issued |
| Issue Not Resolved | Customer’s problem remains unresolved. We detect this through lingering frustration, repeated issue framing, unresolved tonal tension, and absence of closure cues. | preset:issue_not_resolved |
| Inappropriate Speech | Unprofessional or unsuitable spoken content. We detect this using aggressive tone, boundary-crossing language, emotional volatility, and contextual mismatch with professional norms. | preset:inappropriate_speech |
| Discriminatory Practices | Subtle prejudicial decision-making indicators. We detect this through biased framing, dismissive tone shifts, unequal politeness levels, and coded language delivered with emotional distance. | preset:discriminatory_practices |
| Issue Resolved | Customer’s problem successfully addressed. We detect this using relief markers, positive tonal shift, relaxed pacing, confirmation language, and conversational closure cues. | preset:issue_resolved |
| Threat-based harassment | Targeted threats toward an individual in a professional context. We detect this using aggressive volume, hostile prosody, explicit threat markers, and sustained emotional intensity. | preset:threat_based_harassment |
| Sexual Harassment | Unwanted sexualized speech or advances. We detect this through suggestive intonation, boundary-testing pauses, inappropriate familiarity, and discomfort responses from others. | preset:sexual_harassment |