Pecan.ai, a startup that wants to help business analysts build machine learning models in an automated fashion, emerged from stealth today and announced an $11 million Series A.

The round was led by Dell Technologies Capital and S Capital. Along with a previously unannounced $4 million seed round, the company has raised a total of $15 million.

CEO Zohar Bronfman says he and co-founder Noam Brezis, whom he has known for more than a decade, started the company with the goal of building an automated machine learning platform. They observed that much of the work involved in building machine learning models is about getting data in a form that the algorithm can consume, something they’ve automated in Pecan.

“The innovative thing about Pecan is that we do all of the data preparation and data, engineering and data processing, and [complete the] various technical steps [for you],” Bronfman explained.

The target user is a business analyst using business intelligence and analytics tools, who wants to bring the power of machine learning to their data analysis, but lacks the skills to do it. “The business analyst knows the data very well, knows the business problem very well and speaks directly to the business owner of the problem — and they are currently conducting basic analytics,” he said.

Pecan includes a series of templates designed to answer common business questions. They divide these into two main categories. The first is customer questions like how much churn do we have, and the second is business operations questions related to things like risk or fraud. If the question doesn’t fall into one of these categories, it is possible to build your own template, but Bronfman says that is really for more advanced users.

After you select the template and point to a data source such as a database, data lake or CRM repository, Pecan does the work of connecting to the source and pulling data into a dashboard. You can also export the algorithm for use in an external service or application, or Pecan can automatically update a data repository with data the algorithm is measuring, such as churn rate.

The founders have been building this platform since 2016, when they founded the company, and have been working with beta customers for the last 18 months or so. Today, they emerge from stealth and bring Pecan to market in earnest.

Bronfman plans to move to New York City and open a sales and marketing office in the U.S., while Brezis will remain in Tel Aviv and oversee engineering. It’s early days for this startup, but with $11 million in capital, it has a chance to take the product to market and see what happens.

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Machine learning is a complex process. You build a model, test it in laboratory conditions, then put it out in the world. After that, how do you monitor how well it’s tracking what you designed it to do? Arthur wants to help, and today it emerged from stealth with a new platform to help you monitor machine learning models in production.

The company also announced it had closed a $3.3 million seed round, which closed in August.

Arthur CEO and co-founder Adam Wenchel says that Arthur is analogous to a performance-monitoring platform like New Relic or DataDog, but instead of monitoring your systems, it’s tracking the performance of your machine learning models.

“We are an AI monitoring and explainability company, which means when you put your models in production, we let you monitor them to know that they’re not going off the rails, that you can explain what they’re doing, that they’re not performing badly and are not being totally biassed — all of the ways models can go wrong,” Wenchel explained.

Data scientists build machine learning models and test them in the lab, but as Wenchel says, when that model leaves the controlled environment of the lab, lots can go wrong, and it’s hard to keep track of that. “Models always perform well in the lab, but then you put them out in the real world and there is often a drop-off in performance — in fact, almost always. So being able to measure and monitor that is a capability people really need,” he said.

Interestingly enough, AWS announced a new model-monitoring tool last week as part of SageMaker Studio. IBM also announced a similar tool for models built on the Watson platform earlier this year, but Wenchel says the involvement of the big guys could work to his company’s advantage as his product is platform-agnostic. “Having a neutral third party for your monitoring that works equally well across stacks is going to be pretty valuable,” he said.

As for the funding, it was co-led by Work-Bench  and Index Ventures, with participation from Hunter Walk at Homebrew, Jerry Yang at AME Ventures and others.

Jonathan Lehr, a general partner at Work-Bench, sees a company with a lot of potential. “We regularly speak with ML executives from Fortune 1000 companies and one of their biggest concerns as they become more data-driven is model behavior in production. The Arthur platform is by far the best solution we’ve seen for AI monitoring and transparency…” he said.

The company, which is based in New York City, currently has 10 people. It launched in 2018, and has been heads-down working on the product since. Today marks the release of the product publicly.

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