TransMon AutoQA powered by RAAAI Genie


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TransMon AutoQA generates output through its Machine Learning algorithms on 100% customer interactions as per your internally defined SOP (Standard Operating Procedures) along with evidence from the chat.
Expedite and increase the efficiency of the ML training program through live inputs from Quality Auditors.
Send auto updates on feedback and other critical matter to multiple stakeholders (Quality Auditors, Team Leaders, Agents, Managers) for immediate action on relevant matters.

Use AI to scale your interaction analytics

Empower your business with the insights from every customer interaction by leveraging the power of AI and automation. Our solution helps you make data-driven decisions based on the reality of your customer conversations, using advanced Text Analytics technology. With our Text Analytics tool, you can extract valuable insights from the text-based data of customer conversations, such as sentiment analysis, keyword extraction, and topic modeling. By analyzing this data, you can gain a deeper understanding of your customers' needs, preferences, and pain points, and use this information to optimize your business processes and enhance the customer experience.

Gain complete visibility into your customer conversations.

Analyze interaction data at a 1000X volume

Instantly identify coaching opportunities with real-time insights

Provide agents with faster feedback on their performance.

Maximize agent performance by coaching based on reality

Empower supervisors and managers to provide proactive coaching at scale by leveraging the most robust insights on agent performance, powered by TransMon AutoQA technology for Quality Monitoring. With our solution, coaching workflows are optimized to help change behavior and drive better results.

Gain Valuable Customer Insights by Analyzing Key Moments Across Calls and Chats

With our AI-based Moments, your customer touchpoints are no longer limited to a single channel. Our technology empowers agents to interact with customers across multiple channels, providing you with valuable market feedback and insights into what factors contribute to or detract from your customers' experience.

Understanding Machine Learning Operations (MLOps)

The MLOps process involves a series of critical steps for a successful journey from data to machine learning models. Let's examine them in detail

Ingest Data:
Collect raw data from various sources for further processing.
Validate Data:
Verify data quality, integrity, and consistency.
Clean Data:
Remove inconsistencies, handle missing values, and address quality issues.
Standardize Data:
Transform data into a consistent format for seamless processing.
Curate Data:
Organize and structure data for effective feature engineering and model development.
Extract Features:
Derive insights and patterns through feature engineering.
Select Features:
Identify impactful features, discarding irrelevant ones.
Identify Candidate Models:
Explore suitable ML models for the task.
Write Code:
Implement code for model training and evaluation.
Train Models:
Utilize curated data and features for accurate predictions.
Validate Models:
Evaluate model performance on validation data.
Evaluate Models:
Measure performance using appropriate metrics.
Revisit :
Refine candidate model selection based on evaluation results.
Select Best Model:
Determine the highest-performing model aligned with business objectives.
Package Model:
Prepare the model for deployment with necessary files and dependencies.
Register Model:
Maintain a central repository for tracking deployed models.
Containerize Model:
Use containerization for portability and easy deployment.
Deploy Model:
Release model in a production environment for consumption.
Serve Model:
Expose deployed model through APIs for seamless integration.
Inference Model:
Utilize the model for real-time predictions and data-driven decisions.
Monitor Model:
Implement robust monitoring for performance and behavior tracking.
Retrain or Retire Model:
Regularly evaluate and update or retire the model based on performance.
The above steps provide a simplified view of the end-to-end MLOps process. In actual enterprise scenarios, additional stages of testing and steps may exist to ensure rigorous validation and deployment of models across diverse environments.

Trustworthy automation for your contact center

Transparency builds trust. Our solution offers an objective evaluation lens for your entire business, providing automated recommendations based on evidence-based insights.

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