Unlock Peerless Agent Productivity with AI-powered QC

AI-powered Quality Control (QC) refers to the usage of AI algorithms and technologies to assess, monitor, and enhance the quality of interactions between a contact center agent and customers. It involves the automated analysis of various metrics such as call recordings, agent performance data, and customer feedback to identify areas for improvement and ensure adherence to predefined quality standards and compliance.

AI-powered QC enhances quality control processes by leveraging advanced data analytics, conversation intelligence, GenAI, NLP, and LLM techniques to analyze large volumes of data in real-time. It enables the identification of trends, patterns, and anomalies in agent interactions, allowing for proactive intervention and targeted coaching to improve performance. Moreover, AI-powered QC can automate repetitive tasks such as call scoring and evaluation, freeing up valuable time for quality assurance teams to focus on strategic initiatives and areas requiring human judgment.

VQC can be implemented across a wide range of industries and organizations. Currently, we have demonstrated expertise in helping clients in the telecom, IT, healthcare, collections, BFSI, and e-comm industries.

VQC has a powerful list of game-changing features including

1. Sentiment Analysis: Decoding emotional undertones for tailored CX.
2. Unique Keyword Mapping and Voice of Customer: Creating a dynamic map of evolving customer preferences.
3. Call Summaries: Simplifying complex conversations into actionable insights.
4. AI Speech-to-Text: Transforming spoken words into actionable data.
5. AI-Powered Call Scoring: Objective evaluation of calls for performance assessment.
6. Automated Transcripts: Efficiently document customer-agent conversations.
7. Dialer and CRM Integration: Integrate with the CRM and CIM systems of your choice with scope of further customization. 
8. Agent Performance Management: Automating brand guideline compliance for aligned interactions.

Automated QC is far more accurate than traditional QC methods. Traditionally, only 5-10% of calls were analyzed using human QC FTEs, whose feedback was inaccurate and lacked standardization due to bias and human errors. AI QC covers 100% of calls and analyzes them using sentiment analysis, voice of customer, compliance standards, pitch adherence, and several other agent performances and CX KPIs.

Yes, VQC can integrate with your current QC mechanisms and CRMs.

AI-powered QC has showcased up to 75% cost reduction on QC FTEs, process efficiency, and 100% QC coverage and accuracy. Beyond that, the improved QC insights enable CS teams to improve their service quality and uncover further revenue outcomes.

AI-powered QC handles complex data sets and variability using advanced conversational AI, genAI, machine learning, and NLP algorithms. These algorithms are trained on diverse datasets to recognize patterns, anomalies, and context-specific nuances within interactions. Through continuous learning and adaptation, AI can effectively analyze and interpret variations in data, ensuring accurate evaluation and actionable insights for improving quality control processes in contact centers.

Main challenges of implementing AI-powered QC:

1. Data quality and availability: Access to sufficient and high-quality data is crucial for training accurate AI models.
2. Model interpretability: Understanding how AI algorithms reach decisions is essential for trust and regulatory compliance.
3. Integration with existing systems: Seamless integration with legacy systems and workflows can be complex and time-consuming.
4. Privacy and security concerns: Safeguarding sensitive customer data and complying with regulations such as GDPR or HIPAA is paramount.
5. Training organization-specific language models (LLM): Customizing AI models to understand industry-specific terminology and nuances requires significant resources and expertise.

Ensuring data privacy and security with AI-powered QC systems:

1. Encryption: Utilize encryption techniques to protect data both in transit and at rest, safeguarding it from unauthorized access.
2. Access controls: Implement robust access controls and authentication mechanisms to restrict data access only to authorized personnel.
3. Anonymization: Remove personally identifiable information (PII) from datasets used for AI training to mitigate the risk of data breaches.
4. Regular audits: Conduct regular audits and assessments to identify and address security vulnerabilities and ensure compliance with relevant regulations.
5. Secure infrastructure: Deploy AI-powered QC systems on secure infrastructure with built-in security features and protocols to prevent unauthorized access and data breaches.