CONVERSATION INTELLIGENCE
Understand what makes conversations work. Help your team improve.
Quantivus ContentAgent evaluates call transcripts and chats against your own quality criteria. Each question receives an assessment with reasoning, conversation evidence and confidence. Your team can review the findings and turn them into specific feedback.
- Your questions
- Quality defined by your standards
- Evidence
- Conversation excerpts behind each score
- Coaching
- Reviewed feedback for the team
HOW IT CREATES VALUE
Your conversations. Your quality standards.
A score alone cannot explain a good conversation. Connect your standards with what was actually said and review the evidence before giving feedback.
Specific feedback with conversation evidence, ready for informed coaching.
Read the process
- Conversations: Provide call transcripts or chat histories.
- Your criteria: Choose the appropriate question catalog.
- Assessment: Evaluate conversations against consistent questions.
- Review evidence: Check excerpts, explanations and confidence.
- Focused coaching: Turn validated findings into coaching and service improvements.
Quality assurance based on your operating model
Every service organization defines quality differently. The platform accepts structured question catalogs so evaluation reflects your scripts, obligations, service standards and coaching priorities.
- Custom and reusable question catalogs
- Weighted criteria and standard questionnaires
- Structured JSON input for dialogues and transcripts
- Consistent application across large review volumes
- Separation between source dialogue and evaluation rules
- Traceable updates to the assessment setup
Example: improve a service team without listening to every call
A customer-service leader can define a questionnaire for greeting, identification, mandatory information, resolution and next steps. ContentAgent evaluates incoming transcripts against those criteria, while supervisors review low-confidence results and evidence before coaching a team member.
- Apply one approved questionnaire to calls and chats
- Spot recurring gaps, such as missing confirmations or unclear handovers
- Prepare evidence-based coaching conversations instead of generic feedback
- Use aggregated themes to improve scripts and training
Explainable results for human review
An AI score is useful only when a reviewer can understand it. ContentAgent returns detailed findings with confidence, rationale and evidence so supervisors can validate the assessment before acting on it.
- Question-level answers and overall assessment
- Confidence values that signal review priority
- Reasons linked to the relevant conversation context
- Evidence excerpts for faster verification
- Sentiment and improvement signals where configured
- Human review remains part of the quality process
A practical analysis workflow
Teams provide the transcript or chat, select the applicable questionnaire and run the analysis. Results can then feed coaching, calibration and recurring quality reporting.
- Import dialogue or transcription data
- Select language, customer context and question catalog
- Run model-assisted evaluation
- Review evidence and low-confidence findings
- Compare recurring themes and coaching needs
- Archive or export the validated result
Fit analysis into your existing workflow
Transcripts, questionnaires and model configuration come together in a repeatable process. Supported input paths and models are agreed for your operation. The team uses consistent criteria and receives results that people can review.
- Model capability detection
- File-upload or direct-content processing
- Fallback strategy for API limitations
- Token-limit and model-name normalization
- REST endpoints for model validation
- Configuration remains an operator responsibility
Privacy and controlled operations
Conversation data can contain personal, sensitive or commercially confidential information. Deployment therefore needs data-minimization, retention, access-control and model-provider decisions aligned with the organization’s privacy program.
- Limit inputs to information needed for the selected questions
- Define retention and deletion for source files and results
- Control tenant, user and reviewer access
- Assess model-provider and transfer requirements
- Avoid fully automated employment or customer decisions
- Document the purpose and human oversight
Who benefits
ContentAgent is designed for teams that need greater consistency than sample-based manual call listening can deliver, while retaining explainability and human judgment.
- Contact-center and customer-service quality teams
- Sales enablement and conversation coaching
- Complaint and regulated-service review
- Outsourcing and vendor-quality monitoring
- Multilingual operations with common standards
- Leaders looking for evidence behind quality trends
Conversation intelligence for contact-center quality assurance
If you are looking for call analysis, chat quality monitoring or contact-center quality assurance software, start with the decisions your team needs to make. ContentAgent evaluates transcripts and chat histories against your own questions and provides evidence for review and coaching.
- Evaluate service quality against consistent criteria
- Review evidence and confidence before acting on an AI assessment
- Use conversation analysis alongside human coaching and existing support systems
FAQ
Questions, answered clearly
Scope, deployment and commercial terms are confirmed for your use case.
Does ContentAgent replace human quality reviewers?
No. It structures and accelerates evaluation. Human reviewers should validate findings, especially low-confidence or high-impact results.
Can we use our own questionnaire?
Yes. Configurable JSON-based question catalogs are a core part of the documented product workflow.
Can it analyze calls and chats?
It works with structured phone-call transcripts and chat dialogues. Audio transcription and speaker features depend on the configured deployment.
Which AI model is required?
The implementation supports configurable model integrations and capability-aware routing. The suitable provider and model depend on privacy, quality and operating requirements.
How do I choose the right scope?
Start with one use case, the people involved and the evidence you need. In a demo, agree on integrations, access permissions and the operational scope before rollout.
NEXT STEP
Define quality once. Evaluate conversations with shared evidence.
Bring a representative questionnaire and sample workflow. We will map the analysis, review and privacy controls needed for your operation.