The Client
The client analyzes customer experience data from large-scale qualitative surveys. They collect extensive survey data and needed a reliable way to classify survey responses under predefined topics and map them to relevant teams, stakeholders, or decision-makers.
Project Requirements
The client manages more than 700 qualitative survey responses each week, often scaling up to 100,000. Their in-house process for extracting, cleaning, and tagging data was partly manual—making it slow, inconsistent, and prone to errors. As a result, insights were often delayed. To overcome these challenges, they sought an AI-driven automation solution that could streamline response classification, minimize manual effort, and ensure high accuracy across 24+ predefined categories. The solution also needed to be scalable to support increasing survey volumes over time.
Project Challenges
Manual Data Processing Bottleneck
The team was manually reading and categorizing hundreds of survey responses each week, creating significant time delays in insight delivery.
Inconsistent Response Classification
Different analysts were categorizing similar responses differently, leading to inconsistent data quality and stakeholder mapping.
Resource Intensive Operations
Manual processing consumed valuable analyst time that could be better spent on strategic analysis and insight generation.
Scalability Limitations
Growing survey volumes threatened to overwhelm the manual process, requiring either additional headcount or accepting longer processing delays.
Our Solution
SunTec India designed and deployed an AI-driven survey response coding model, built on NLP and ML frameworks, with a Human-in-the-Loop (HITL) validation process for continuous improvement.
We gathered past survey responses and built a structured training dataset via data annotation across 24+ topic categories. To ensure consistency, our team applied text preprocessing steps—such as cleaning, standardizing, and normalizing language—so the model could be trained effectively.
We developed a specialized Natural Language Processing model that functions like a smart assistant, trained to read and understand survey responses. The model learned to recognize patterns in language and classify responses across more than 24 predefined topic categories.
We designed the solution to integrate directly with the client's existing processes, eliminating the need for staff retraining. The team continues to work with Excel files as before, but now the tedious classification work is handled automatically in the background.
Unlike simple keyword matching, our system understands context and meaning. When a survey response mentions multiple topics (such as both "product quality" and "customer service"), the system correctly identifies and tags both categories, ensuring that no feedback is missed or incorrectly classified.
The system gets smarter over time. As the client provides feedback on classifications or adds new types of responses, the model learns and improves its accuracy. This means the solution becomes more valuable the longer it's used.
The NLP solution is built to handle growth seamlessly. Whether the client processes 700 responses per week or 10,000 per month, the system maintains the same speed and accuracy without requiring additional resources or manual intervention.
We implemented a quality control system that automatically flags uncertain classifications for human review. Subject matter experts from our team validate edge cases and ambiguous responses, with their feedback continuously improving the model's performance. This hybrid approach combines the efficiency of AI with human expertise to achieve optimal results.
Our Workflow
Tech Stack
Python
Core programming language used for building the AI model and data pipelines.
NLP Libraries
TensorFlow, Keras, and spaCy for intent classification and natural language understanding.
Data Processing
Pandas and NumPy for efficient handling of unstructured survey responses.
70% Reduction in Data Processing Time
60% Cost Reduction in Survey Processing Operations
95–98% Classification Accuracy Maintained
Improved Decision-Making Speed for Stakeholders
They really transformed our survey workflow by delivering high accuracy and remarkable efficiency, turning a once tedious manual task into a seamless, scalable process.
Emily Parker - Research Operations Manager
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