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Massachusetts-headquartered Akkio, a company offering a no-code platform to help businesses deploy artificial intelligence (AI) in minutes, today announced $15 million in a series A funding round of funding. The company said it will use the capital to accelerate the commercialization of its platform and deliver an easy-to-use AI assistant to anyone working with data.

“This investment allows us to scale and advance our platform so business analysts can leverage AI technologies to work faster, unlock new insights, and make a bigger impact for their organization,” Abe Parangi, cofounder and CEO of Akkio, said. 

The round, which was led by Bain Capital Ventures and Pandome, Inc., takes the total capital raised by Akkio to $18 million. It comes at a time when companies of all sizes are bullish on data and AI and looking for ways to drive maximum business value from them.

How does Akkio help?

AI is the need of the hour today, but taking advantage of its capabilities is difficult owing to the engineering complexities involved. Akkio tackles this challenge by providing enterprises with no-code tools to quickly build and deploy AI for tasks like churn reduction, attrition prediction, fraud detection and sales funnel optimization.


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Users simply have to choose their dataset and train the neural network. Within minutes, the solution prepares the model, allowing users to ship ML-enabled workflows and features to power internal applications and tools critical to streamlining day-to-day operations and improving business outcomes. 

As part of the effort to simplify AI development and deployment, Akkio’s platform also offers AI-driven capabilities that handle certain crucial aspects of working with data. For example, its chat data prep feature allows users to automate data cleaning and preparation (combining columns, summarizing records and performing complex calculations) through natural language chat. This is a critical step before using the data for AI.

Then, with the new Chat Explore capability, data analysts can tap GPT-4 to directly chat with their data, identify patterns and instantly build live charts and visualizations. There’s also a Forecasting model that understands patterns in live data and creates forecasts predicting things like inventory availability and sales and marketing performance.

“From the beginning, we have been laser-focused on…building out an end-to-end solution for working with data. We go from extracting data from the systems in which it is generated or stored, by transforming and analyzing it, to building and deploying ML models. Building the end-to-end system is critical to enabling a self-serve data assistant – our vision is that we are the only tool you need to make better data-driven decisions,” Jon Reilly, COO at Akkio, told VentureBeat.

Strong customer and competition base

Currently, Akkio claims that “hundreds of customers,” including Ellipsis Marketing, AngioDynamics and Standard Industries, use its offerings to use AI with their data and improve internal processes.

“We’ve been developing the platform for three years and just started signing up customers in 2022. Our customers range from a 2-person marketing shop to a multi-billion freight management company. This stuff is hard and we’re focused on ease of use. It takes a long time to make complex tech. Now we’re already in the hundreds of customers and this thing is moving,” Reilly added.

The no-code AI development space has been growing gradually, particularly in light of the pandemic and the shortage of data science talent. Other players operating in the same segment are Datarobot, Google AutoML, Obviously AI and Fritz AI.

“While developing, we admired companies like DataRobot who pioneered the space; however, we’re targeting a different segment within it. We’re focused on any small business that wants more actionable value from their data but doesn’t have the data scientist on staff and/or can’t afford hefty consulting fees,” Reilly said.

According to Gartner’s Magic Quadrant report, 65% of application development will take place on no-code and low-code machine learning platforms by 2024.

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