How Commerce.AI helps companies control unstructured data

Commerce.AI automates product decisions and workflows for brands and retailers using AI which can read, see, hear and understand consumer feedback at scale

Commerce.AI was founded with the mission to harness the power of artificial intelligence (AI) to change the way commerce is done. 

According to the company, 95% of today's data is unstructured, in the form of text, voice, images, and video. Commerce.AI helps activate unstructured data to build next-gen customer experiences. 

Its technology, which is powered by thousands of pre-built machine learning models, processes and extracts business insights from voice, chat, text and video data. Leveraging 100+ data integrations, Commerce.AI helps enterprises unlock opportunities across all stages of the customer experience - from consumer to customer to client. 

"We created Commerce.AI because we saw a massive opportunity to help companies take control of unstructured data and unlock new business opportunities," said Andy Pandharikar, CEO and Founder of Commerce.AI.  

Commerce.AI is used by leading Fortune 500 organisations, including Unilever, Suzuki, Coca Cola, and Netgear.

Using unstructured data for customer solutions 

The company has recently announced its cloud-based data platform is a premium app now available on Genesys AppFoundry, the industry's largest dedicated marketplace focused on customer experience solutions. 

The AppFoundry allows Genesys customers from all market segments to discover and rapidly deploy a broad range of solutions that make it easier to interact with consumers, engage employees and optimise their workforce.

"Contact centers have had limited visibility into the customer, even though they are often the first line of engagement to address customer needs. Commerce.AI allows Genesys customers to obtain a holistic customer view by combining enterprise unstructured data insights - pre- and post-purchase - with traditional internal conversational data to empower agents to deliver greater business outcomes." Jay Acosta, VP of GTM for Commerce.AI.  

Embracing the power of AI 

Founded in 2016, the company believes in democratising AI. That means enabling AI to unleash its power to serve every person and every organisation in the world.

Microsoft Azure customers worldwide now gain access to Commerce.AI, giving companies and their teams an easy way to leverage the power of unstructured data to build next-generation customer experiences.

Last year Commerce.AI become available in Microsoft Azure Marketplace and on Microsoft Business Apps Store. This digital catalog with listings from independent software vendors makes it easy to find, buy and deploy software. This availability opens the door for Microsoft Azure customers worldwide to seamlessly deploy Commerce.AI's tools, integrations and visual interface to process unstructured data - such as text, voice & video - and extract insights in real-time.

"We have made it easy for anyone with an Microsoft Azure account to try our platform and start running CX projects in moments and without formal training. We give data scientists, CX operators, and teams across business functions real-time insights to optimize business decisions," said Pandharikar.

 

Share

Featured Articles

IBM's VP of Build on Where Embeddable AI Stands to Benefit

IBM EMEA's VP of Build Dawn Herndon explains what embeddable AI is and where its main use cases and benefits will come from

Davies Increasing AI Focus with First Group Chief AI Officer

Although the first Group Chief AI Officer role at the firm, the appointment of Paul O'Brien is one step in a long walk to building their AI strategy

Tech & AI LIVE New York: Speaker Announcement

Executives from Ping Identity, ServiceNow and Consumer Technology Association are announced to be joining the line-up at Tech & AI LIVE New York

MLOps: What Is It and How Can It Enhance Operations?

Machine Learning

Kyocera CISO Talks Good Data Security in the Age of Gen AI

Data & Analytics

Sony & AI Singapore Join to Build Language Diversity in LLMs

Machine Learning