D.A.V.E. is an agentic platform that automates the analysis of diverse feedback data sources, including surveys, audio, and transcripts. It employs blended intelligence, combining LLMs with statistical methods to generate auditable, decision-ready insights quickly. This process allows organizations to move beyond manual data cleaning and focus on actionable recommendations for customer satisfaction, market research, and engagement.
Funding
Funding not disclosed
Founders
Product
Problem
Analyzing and reporting on survey data is often a time-consuming and inefficient process, requiring significant manual effort to clean data, perform quantitative and qualitative analysis, and generate actionable insights. Traditional tools often lack the ability to provide comprehensive analysis, requiring users to employ multiple platforms and limiting the speed and effectiveness of decision-making.
Solution
GoLLM's H.A.A.L. is a generative AI platform designed to automate the entire survey data analysis and reporting workflow, transforming raw data into actionable insights. H.A.A.L. ingests data from various sources, automatically cleans and normalizes the data, and performs both quantitative and qualitative analysis using natural language processing. The platform generates comprehensive reports with interactive visualizations, thematic and sentiment analysis, historical comparisons, and forward-looking recommendations. Users can interact with the AI through a chat interface to refine the analysis, add business context, and customize reports for different audiences.
Target Audience
H.A.A.L. is designed for businesses of all sizes, local government authorities, national organizations, and market research firms seeking to streamline survey data analysis and improve decision-making.
Features
- Automated data ingestion from various sources, including CSV, Excel, JSON, Google Forms, and SurveyMonkey
- Advanced data cleaning algorithms to handle missing values, outliers, and inconsistencies
- Quantitative analysis, including statistical summaries, frequency distributions, and cross-tabulations
- Qualitative analysis using NLP to identify key themes and sentiments in open-ended responses
- Context-aware sentiment analysis that incorporates context-specific understanding for improved accuracy
- Historical comparison to highlight trends and changes over time
- Recommendation system that uses external data sources to provide context-aware recommendations
- Interactive chat interface for refining analysis and customizing reports