In an interview with TimesTech, Mandar Kulkarni, VP of Findability Sciences, unveiled their GenAI-powered BI BPC. The product revolutionizes data interaction by enabling users to query and analyze operational data using plain English. This innovation allows executives and business users to extract actionable insights without technical expertise, leading to faster decision-making and significant cost savings across multiple industries.
Read the full interview here:
TimesTech: What is the core functionality of the product?
Mandar: Findability Sciences GenAI powered BI BPC provides business intelligence through a natural language interface. Users can query operational data using plain English, with the system automatically generating database queries and visualizations in response. This allows business users and executives to access and analyse data without relying on technical teams.
TimesTech: Who is the primary target audience for this product?
Mandar: Executives, managers, and business users in various industries who need timely and actionable insights from operational data, particularly those lacking technical skills in data analysis and preferring an intuitive, natural language interface.
TimesTech: Who are the customers? Which industries and what specific problem does the product solve for the customers?
Mandar: Customers span multiple industries, including finance, healthcare, retail, and manufacturing. The product solves the problem of data accessibility, allowing users to easily extract insights from complex datasets without technical expertise. It’s particularly useful in industries where timely decision-making is critical, such as financial services for risk management or retail for inventory optimization.
TimesTech: What are the key features and benefits of the product? Can you substantiate benefits in $ terms?
Mandar:
- Features:
- Natural Language Query Processing
- Automated Data Visualization
- Real-Time Data Analysis
- No data access to LLM
- Benefits (with $ substantiation):
- Reduced Dependency on Technical Staff: Up to 45% savings on labour costs
- Faster Decision-Making: Potential revenue increase up to 25%
- Labor Cost Reduction: Mid-sized companies can save $200,000 to $800,000 annually
- Revenue Growth: Potential annual revenue increases of up to $1 million
TimesTech: Are there any unique technologies or innovations used in the product?
Mandar: The product is compatible with cutting-edge Generative AI models (GPT, Claude, Llama, Mistral) and GenAI platforms like WatsonX.ai, Bedrock, Azure, Vertex AI, etc. for natural language processing and understanding. This enables automatic translation of natural language queries into structured database queries.
TimesTech: What is the current development stage of the product?
Mandar: The product is live with 4 international customers.
TimesTech: What are the plans for future updates or features?
Mandar: Enhancements in natural language understanding, integration with additional data sources, advanced analytics features, more sophisticated data visualization, predictive analytics capabilities, machine learning-driven insights, user access with constraints, and integration with internal communication channels like Slack and Teams.
TimesTech: Who are the main competitors, and how does our product compare? Give a price comparison.
Mandar: The product differentiates itself with a more intuitive natural language interface and advanced AI for query generation. Exact pricing details are not provided, but it’s positioned to offer competitive pricing, potentially lower than high-end competitors, while providing unique value through ease of use.
TimesTech: What are the key pain points identified by customers that this product addresses?
Mandar: Key pain points addressed include:
- Difficulty in accessing and understanding complex data
- Reliance on technical teams for data analysis
- Delays in obtaining actionable insights
The product addresses these by simplifying data interaction, reducing dependency on specialized skills, and providing quick access to data-driven insights.
