Enterprise Forecasting provides BI via NL interface: Findability Sciences

Findability Sciences stands at the forefront of AI product and solution providers, with keen focus on facilitating traditional enterprises in harnessing the power of data and cutting-edge AI technologies. It ‍works with over 50 global clients across the United States, India, Middle East, and Japan. 

Core expertise lies in delivering advanced Enterprise Forecasting and Business Process Co-Pilots, driven by the proprietary Findability platform. This platform integrates AI technologies, such as discriminative and GenAI, within a stringent governance framework.  ‍

Mandar Kulkarni, VP, tells us more. Excerpts from an interview: 
 
DQ: What is the primary function of this product?

Mandar Kulkarni: The Enterprise Forecasting product provides business intelligence through a natural language interface. It allows users to query operational data using plain English, automatically generating desired output in tabular form and visualizations, enabling faster, data-driven decision-making.
 
DQ: How does this product stand out from our current offerings in BPC for BFSI?

Mandar Kulkarni: This product leverages Generative AI to make data interaction more accessible and intuitive. It does not require user data to be sent to LLM, ensuring enhanced data privacy and a user-friendly experience, making it ideal for business executives, business analysts, etc.
 
DQ: Who is the main target audience, and what specific problems does the product address for them?

Mandar Kulkarni: The target audience includes executives, managers, and business users across industries, including finance, healthcare, retail, and manufacturing.

Problems addressed are the difficulty in accessing and understanding complex datasets, over-reliance on technical teams for data analysis, delays in obtaining actionable insights. 

The product enables users to access, analyse, and visualize data effortlessly, empowering quicker and more informed decision-making.
 
DQ: What are the key features and benefits of the product, and can these benefits be quantified in monetary terms?

Mandar Kulkarni: Key features include natural language query processing, automated data visualization, real-time data analysis, and no data access to LLM (ensuring privacy).

Benefits, such as monetary quantification are labour cost reduction, saving up to 45% of technical staff costs, translating to $200,000 to $800,000 annually for mid-sized companies. Faster decision-makingsSpeeds up decision cycles, potentially boosting revenue by 25% annually. Hence, revenue growth enables incremental revenue increases of up to $1 million/year by leveraging timely insights.
 
DQ: Does the product incorporate any unique technologies or innovations?

Mandar Kulkarni: Yes, it incorporates advanced Generative AI models (e.g., GPT, Claude, Llama, Mistral) and platforms (e.g., WatsonX.ai, Bedrock, Azure, Vertex AI). These innovations enable seamless natural language translation into structured database queries, ensuring ease of use and wide compatibility with modern AI ecosystems.
 
DQ: At what stage is the product currently in its development, and what are the plans for future enhancements?

Mandar Kulkarni: The current stage is that the product is live and deployed with four international customers, demonstrating its market readiness and functionality.

Future enhancements include broader data source integration, advanced analytics and predictive capabilities, machine learning-driven insights, integration with tools like Slack and Teams for collaborative use, user-specific access controls for enhanced governance, and user feedback mechanism.
 
DQ: How does the product compare to competitors, particularly in terms of pricing, and what customer pain points does it specifically address?

Mandar Kulkarni: In comparison with competitors, the Findability Enterprise Forecasting and Business Process Co-Pilots offer a more intuitive natural language interface than competitors like Tableau or Power BI with AI. It incorporates advanced Generative AI for superior query generation. We have priced this competitively, offering better value through ease of use and productivity gains. 

Pain points addressed include difficulty in accessing and interpreting data, dependence on technical teams for analysis, delays in obtaining actionable insights.