Artificial intelligence (AI) adoption is on the upswing. More than 77 percent of marketers responding to a recent Blueshift survey anticipate they’ll increase their use of AI in the next 12 months. And according to Oracle, 80 percent of businesses plan to adopt AI as a customer service solution by 2020. But implementing AI isn’t necessarily easy — particularly if your business lacks in-house data science expertise.
That’s why Pedro Alves, former director of data science at Sentient Technologies and chief data scientist at Banjo, founded Ople, a Silicon Valley software firm that aims to ease the deployment of AI with an automated development platform. At VB Summit 2018 this week, the company announced an $8 million Series A funding round led by Triage Ventures, with participation from Hack VC and existing seed investors.
Alves said Ople will use the capital to expand its product team, sales, and marketing efforts.
“We are engineering intelligence,” he said. “Our product is a major leap forward in the simplification and automation of the most laborious tasks in data science. This means that more teams, across more industries, will deliver more AI projects in less time.”
Ople enables customers to quickly develop AI models by using datasets to detect patterns and relationships. In that way, it’s not unlike R2.ai, a startup with a suite of machine learning services that train high-quality models, and Feature Labs, which automates feature engineering by using techniques that automatically create algorithms from datasets.
But Ople’s differentiated in key ways. Its platform is available on Amazon Web Services, and it continuously learns from every model built while simultaneously gaining speed and accuracy. Alves calls the approach behavioral simulation, or BASS, and claims it can increase a data science team’s ability to create a model by a factor of 10 compared to competing solutions.
“By using Ople, companies are leaping past their competition, making better decisions — faster and positioned to seize new market opportunities first,” Alves said.
Here’s how it works: First, customers upload their datasets in comma-separated value (CSV) format with index, numeric, categorical, and target columns clearly labeled. (Somewhat uniquely, Ople doesn’t require data to be “cleansed” before it’s processed.) Next up is validation: Ople ingests the data and uses it to train the AI model and then maps and generates preliminary results for human review.
The penultimate step is configuration optimization, during which Ople’s platform produces a custom AI model and automatically compares the results to leading models. Finally, when it comes time for deployment, Ople runs through the data in the customized model to ensure a baseline level of confidence.
Its stack works across virtually all domains, Alves said, including insurance claim prediction, churn prediction, dynamic pricing optimization, network throughput optimization, user behavior prediction and classification, route optimization and delivery predictions, and others.
Ople, which has more than doubled in size in the last year, has raised about $10 million to date.
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