Our Core Approach
- LLMs Trained on Diverse Data: We use large language models to recognize patterns far beyond simple keyword counts or sentiment scoring.
- Data-Science + Human-Research Foundations: Concepts from social science and qualitative research guide how we detect themes, outliers, and sentiment.
- Fast & Scalable Pipeline: Our infrastructure can process anything from a dozen responses to large datasets in seconds, with consistent outputs.
What We Detect
- Recurring Themes: Common patterns and frequently mentioned topics across responses.
- Emerging Concerns: New issues or signals that may not dominate yet but are gaining traction.
- Unique Outliers: Standout responses that highlight critical, unusual, or high-priority insights.
- Contextual Sentiment: Positive, negative, and mixed emotions, interpreted within the context of the specific question or dataset.
Why You Can Trust the Method
- Explainable Outputs: Reports include breakdowns, percentages, and per-response evaluations to show exactly what was found.
- Scientifically Informed: Criteria are inspired by social science and measurement theory, adapted for robust, at-scale analysis.
- Consistent & Automated: No human reviewers influence results; the same input yields the same analysis every time.
- Decision-Support First: AI augments your judgment—it doesn’t replace it. Use insights as decision support alongside human expertise.
- Mind Metrology Ecosystem: Text Response Hub is part of the broader Mind Metrology ecosystem, building on years of experience in metrology, social science, and data engineering.