A few years back, startups focusing on artificial intelligence had a whiff of bullshit about them; venture capitalists became inured to young tech companies claiming that their new AI-powered product was going to change the world as hype exceeded product reality.
But in the time since, AI-powered startups have matured into real companies, with investors stepping up to fund their growth. In niches, from medical imaging, of course, to speech recognition, machine learning and deep learning and neural nets and everything else that one might scoop into the AI bucket has seemed to have grown neatly in recent quarters.
Indeed, AI investing has become so popular amongst VCs that this publication wound up debating the finer points of AI-focused startup revenue quality earlier this year.
But AI is not the only startup niche appearing to enjoy tailwinds lately. No-code and low-code startups have also enjoyed increasing media recognition, and, as TechCrunch has covered, notable venture capital rounds.
Sitting in the middle of the two trends, a startup called MonkeyLearn wants to bring low-code AI to companies of all sizes. And the firm just raised $2.2 million. Let’s take a look.
No-code AI
Starting with the round, MonkeyLearn has raised $2.2 million in a round led by Uncork Capital and Bling Capital. Speaking with Raúl Garreta, a co-founder at the company and also its CEO, TechCrunch learned that MonkeyLearn started off as a more developer-focused service that provided machine learning tooling via an API. But after demand materialized from people who couldn’t code to use the company’s tech for text analysis, the company wound up heading in a slightly different direction.
Garreta gave TechCrunch a demo of the company’s service, which allows users to upload data — think rows of text in an Excel file, for example — and quickly train MonkeyLearn’s software to parse out what they are looking for. After the model is trained over the course of a few minutes, it can then be set to work on a full data set.
According to Garreta, text analysis has a lot of demand in corporate environments, from categories like support ticket sorting to sentiment analysis.
But MonkeyLearn’s product that TechCrunch saw is not the company’s final vision. Today the service focuses on data analysis. In time, Garreta wants it to do more with data visualization, providing graphing and other similar outputs to give more of a dashboard-feel to its product.
Demand
At the core of MonkeyLearn’s early market traction that helped it land its seed round is the ever-increasing need for non-developers to collect, parse, act on and share data inside of their workplace. If you’ve ever worked nearby to a startup’s marketing or customer success team, you understand this phenomenon. MonkeyLearn wants to give non-developer teams the tools they need to understand data sets without forcing them to go find the engineering team and argue for a spot on the roadmap.
“Our vision is to make AI approachable by providing a toolkit for teams to actually use AI in their daily operations,” Garreta said in a release. MonkeyLearn is theoretically well-situated in the market. Companies are increasingly data-driven at the same time as the market is strapped for employees who can make data sing.
The startup has a free tier, and a few paid tiers, along with add-ons and a one-off option. You can call that the “all of the above” pricing model, which is fine, given the youth of the company; startups are allowed to experiment.
After slower than anticipated early fundraising, MonkeyLearn told TechCrunch that it could have raised double in its seed round what it wound up accepting.
What plans does the company have for the new capital? A more aggressive go-to-market motion, and a more formal sales team, it said. As MonkeyLearn sells to to mid-market and enterprise firms, Garreta explained, a more formal sales team is needed, though he also emphasized that founders must start the selling process at a startup.
As with most seed companies that raise capital, there’s a lot to like with MonkeyLearn. Let’s see how well it executes and how fast it can get to a Series A.