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Guardian Owl AI 

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The Guardian Owl AI and Findability Sciences alliance is a data-driven, Artificial Intelligence solution that helps organizations by developing sharp marketing strategies for sharper communication. Our big data, cognitive and AI platform makes use of readily available data from your internal database and other consumer interaction sources to create actionable customer insights. The solution provides a 360-degree view of the customers & identifies prospective leads by creating personas of our customers and incorporating data analytics & AI predictions to suggest the most relevant communication technique. Powered by advanced AI and cognitive analytics, CMOs and Brand Managers can now deliver more effective brand communications that are based on sharper, more focussed marketing strategies.

Featured in Fast Company

World Changing Ideas Awards 2021: AI and Data Finalists and Honorable Mentions

Notable Customers and Partners

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Applications of Artificial Intelligence in Accounting

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A.I. in KY: Northern Kentucky University Announces Minor in Artificial Intelligence

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What to Know About Answer from Brave, One of the Internet’s Largest A.I. Search Engines

In case you are unaware, one of the biggest changes to an extant technology that many analysts are expecting A.I. to bring about is the comprehensive upgrading of the search engine. From Google's Search Generative Experience to Microsoft's “the new Bing”, there are a...

Meet Meta AI, the Latest A.I. Assistant

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Findability.ai for business

Powered by Findability.AI

Enterprise AI Solutions

Guardian Owl serves clients through the award winning propriety Findability Sciences platform.  

Findability.ai enables customers to accelerate their Data-to-ai jouney through building their own IP and data science capibilities to drive a 90% forecast Accuracy, a 30% resource savings average, and a. 20-100% in increase in click rates. 

  • 90% Accuracy 90% 90%
  • 20% -100% in CTR 100% 100%
  • 30% Savings in Resources 30% 30%

Persona Modeling

Customize HR operations to improve employee satisfaction and productivity by better understanding the personality characteristics, needs, and values of personnel.

Creative Insurance

Our Touch Up AI tool uses customers’ personality characteristics to determine which creative and interaction medium to use, what web page to display, and what video to share for quantifiable profits prior to creative deployment.

Chatbots/Conversational Computing Platform

Ever feel like cloning yourself? Deploy a lead generation chatbot and lead follow-up chatbot for customer acquisition and after-sales follow up.

Data Analytics Platform

Make use of donor and recruiting data to generate and select multiple models from a data pattern, making use of vast volumes of unstructured data.

 

 

AI Predictions Platform

Predict raw materials and commodities pricing to sustained accuracy levels of over 99%! Project the movement of raw materials and work-in-process inventory from point of origin to consumption.

GO AI Academy Platform

GO AI Academy aims at establishing an industry standard for bench-marking Artificial Intelligence (AI) and Data Science skills, this program comprises an online assessment of theoretical concepts, working knowledge of the latest tools and algorithms, and business problem-solving abilities. 

 

The AI Adoption Challenge

 

Our client, a leading operator of multi-purpose retail chain issues shopping credit card to the members of its loyalty program, resulting in an average monthly overdue debt amounting to nearly USD 50 million. The client wanted to adopt an AI-driven, propensity-based debtor contact strategy that would increase collections without additional manpower costs.

 

View AI Solution Case Study

 

 

The AI Solution

 

Self-learning, multi-modeling, predictive AI technology of Findability Platform was used to assign “Propensity to Pay” for each record in the debt portfolio. The follow-up strategy for collections was developed basis two factors: Propensity-to-pay and ‘age’ of the debt. The debt portfolio was divided into six categories, based on how long overdue the debt was, and propensity modeling was undertaken separately for each category.

 

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multi-model sets for higher accuracy

worth of debt processed monthly

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increase in debt collection