Findability hopes to make AI adoption easier for enterprises

These days, “big data” simply isn’t enough. To provide meaningful insights and valuable analytics for optimal decision making, companies must adopt the concept of “wide data.”

Whereas big data focuses on the so-called “three V’s” — volume, velocity, and value — wide data homes in on value, according to Anand Mahurkar, founder and CEO of leading enterprise AI company Findability Sciences. That is, it’s not just a mass of data for data’s sake, or data derived from a few sources. It’s tying together data from a wide range of sometimes seemingly disparate sources to allow for deeper, more purposeful analysis.

“Wide data means not only the data in my organization; it’s going beyond the boundaries of my organization and combining external data, internal data, structured and unstructured data,” Mahurkar explained to VentureBeat. “If you want to know what will happen and what to do, you will need ‘wide data’ and not just ‘big data.'”

The evolution of enterprise AI

This is a foundational concept in enterprise AI, which involves embedding artificial intelligence methodologies into an organization’s data strategy. The software category is undergoing rapid growth as more companies across all sectors undergo digital transformation. Global Industry Analysts Inc. projects the global market for enterprise AI to reach $15.9 billion by 2026. That’s up from $1.8 billion in 2020.

Findability Sciences is working to set itself apart in a market whose dominant players include the likes of C3 AI, Abacus.ai, Microsoft, and Snowflake. Specifically, the Boston-headquartered company is lasering its focus on what Mahurkar called traditional companies — such as those in the manufacturing and retail spheres — that are still making use of legacy software products. This remains a sizable market: there are more than 60,000 companies worldwide with revenues of $200 million or more. 

Particularly post-pandemic, these enterprises are beginning to understand the necessity of digital transformation and AI, but they struggle with adoption and deployment, Mahurkar said. Undertaking a custom build to embed AI into existing infrastructure can be a paralyzing proposition, and outsourcing can be costly while taking an undue amount of time. 

Embedding AI technology

To help companies tackle — and ideally master — the transition, Findability today launched its new white-label suite Findability.Inside. Quickly deployable and repeatable, it allows companies to embed AI technology into their already existing hardware and software, in turn enhancing features and functionalities and driving new insights and efficiencies. The suite makes use of advanced capabilities including computer vision, machine learning, and natural language processing to aid with predictions and forecasting, price optimization, market targeting and segmentation, sales prospecting, online advertising, and customer service.

One unique feature is NLP-driven automatic summarization of video meeting recordings and industrial scale document scanning. With intelligent document processing, Mahurkar explained, embedded AI can analyze keywords and context in text-heavy documents and create automatic summaries that save valuable human reading time. 

It’s no secret that many external factors impact any given business, he noted. But in the past, such factors have been difficult to predict, or prepare for — which is where enterprise AI can prove so valuable. 

For example, say you’re a manufacturer of air conditioner units that procures your coils from China. Economists have hinted at market disruptions and fluctuations that could impact supply chain, cost, and time-to-market; these imperative details can help you pivot to make up for any gaps. Similarly, being apprised of predicted weather patterns in your top sales areas can prompt you to market more heavily there or to branch out into other areas. 

Mahurkar provided another example of a Silicon Valley-based digital signal processor company that used the Findability platform to track propensity-to-click patterns. In studying those, it made adjustments to online advertising and real-time bid-optimization to offer more competitive rates when it comes to cost per impression and cost per mille.

For businesses dealing in physical products, Mahurkar added, enterprise AI can help by tracking and predicting inventory, supply chain issues, market conditions, and price fluctuations. “Most software will tell you what happened, they’ll tell you about the past,” such as revenues and profits, he said. “But customers are looking for the ability to know what will happen and what to do. They want leading indicators.”

He described Findability.Inside as a low-code, low-cost, easy-to-use suite that can be rapidly integrated. Companies see end results in enhanced legacy products, improved customer service, bolstered revenues, and customer satisfaction and retention. They can drive digital transformation without having to develop code or invest significant human talent that is critical elsewhere. 

Founding Findability

A first-generation immigrant and entrepreneur who came to the U.S. 20 years ago, Mahurkar established Findability in 2018 with an initial investment from SoftBank Group. In just a short time, the company has garnered high-profile customers such as IBM, Snowflake, and Red Hat, and its products have been used in conversational computing, and to analyze advertisement efficacy, assess propensity to pay, forecast apartment rentals and occupancy, and optimize supply chains.

As Mahurkar explained, he set out to build a technology that connects internal, external, structured, and unstructured data to “improve a company’s ability to find information,” and allow them to realize the potential of data and apply that to business improvement. 

When rolling out a suite like Findability’s, he added, “Now within just a few months their products can be enabled with AI, and it’ll start telling their end customers that ‘This will happen to your business. And this is what you should do.'”