After years of working in global tech companies, as a software engineer, and forming his own companies, Alani Kuye created Relotis, Inc., a tech start-up for retailers and sellers.
Kuye, the founder and CEO of Relotis, said there has been a shift in technology from a decision-making system to a decision-support system, meaning the technology can either make sometimes flawed decisions for people, or people can use technology to leverage their choices.

He said the adaptation of technology has strengthened human decision-making skills by basing new ideas on accurate data. Along with others, Kuye said this idea is the aim of his company’s use of artificial intelligence.

Relotis is a technology company designed to use artificial intelligence (AI) and machine learning (ML) to track user data for companies, and apply that data to promotional tools and other applications to help their businesses grow.

Kuye said Relotis is not a retailer but an optimization tool for vendors and other sellers that can limit the cost of social media marketing strategies that may not always represent an increase in sales, which he said would be “vanity metrics.”

“Many companies spend too much on social media marketing,” he said. “[These companies] go spend $2 or $3 million on Facebook, Instagram, and other platforms and I always ask them: ‘Your campaign got you 100,000 likes and 10,000 followers. Did you gain 10,000 new customers?’ The answer is always ‘No.’”

On the consumer side, Relotis works with retailers, resellers, manufacturers, and similar distributors to have its QR code printed on the retailers’ packaging for customers to scan. Relotis then draws a profit from this scan and directs the consumer to promotional tools from their chosen retailer.

On the supply side, Relotis acquires internal information from the company and then uses its AI technology to analyze the data and depict it simply through Large Language Processing models (LLMs). These models understand and generate text similar to human speech.

Similar to ChatGPT, another LLM that uses natural language processing models, Relotis uses this technology to display data that is easy for companies to understand and track, to then modify their plans based on the data presented.

But many people cite the issues with ChatGPT as its misinformation and outdated information. Even with its recent update in March, the system lacks information after September 2021.

The software may also produce misinformation because it uses open-source data. This means the system scrapes information from millions of sites and compiles this data, which can sometimes lead to inaccuracies.

Kuye explained that his company differs since it only relies on user data that the company already records. He said, therefore, the AI and ML model used with Relotis can accurately depict data.

The misinterpretation of data is often cited in the example of the motion-detected soap dispenser. Several videos online depict a hand-operated soap dispenser not recognizing Black hands, while dispensing soap to white hands. This citation often leads to a conversation about a greater need for diversity in technology and its testing departments, since humans are becoming increasingly reliant on technology and possibly AI and ML models as these increase in popularity.

Kuye said his company also seeks a $12 million investment to expand its marketing strategies, product development, and staff. He also said he is excited about growing his company further and especially to work in the healthcare field.

Kuye said he wants his model to offer financial advice or incentives through AI and ML to help reduce costs for people in need. He provided an example of people having to choose between paying medical bills and rent, among other fiscal difficulties.

“Someone who lives in Westchester, N.Y., or the right side of Greenwich, Connecticut, probably doesn’t care if they can afford a $5,000 a month (expense),” Kuye said. “But that’s the 1 percent. Most people care when those medical bills come, especially [those who are] disadvantaged. If you start to look at the biases in AI itself—that’s how we can solve it: by having it look at the right data.”

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