No-code startups continue to see a lot of traction among enterprises, where employees — strictly speaking, non-technical, but still using software every day — are getting hands-on and building apps to take on some of the more repetitive aspects of their jobs, the so-called “citizen coders” of the working world.

And in one of the latest developments, a Bryter — an AI-based no-code startup that has built a platforms used by some 100 global enterprises to date across some 2,000 business applications and workflows — is announcing a new round of funding to double down on that opportunity. The Berlin-based company has closed a Series B of $66 million, money that it will be investing into its platform and expanding in the U.S. out of a New York office it opened last year. The funding comes on the heels of seeing a lot of demand for its tools, CEO and co-founder Michael Grupp said in an interview.

“It was a great year for low-code and no-code platforms,” said Grupp, who co-founded the company with Micha-Manuel Bues and Michael Hübl. “What everyone has realized is that most people don’t actually care about the tech. They only care about the use cases. They want to get things done.” Customers using the service include the likes of McDonald’s, Telefónica, and PwC, KPMG and Deloitte in Europe, as well as banks, healthcare and industrial enterprises.

Tiger Global is leading this round, with previous backers Accel, Dawn Capital, Notion Capital and Cavalry Ventures all also participating, along with a number of individual backers (they include Amit Agharwal, CPO of DataDog; Lars Björk, former CEO of Qlik; Ulf Zetterberg, founder and CEO of Seal Software; and former ServiceNow global SVP James Fitzgerald). The valuation is not being disclosed; Bryter has raised around $90 million to date.

Accel and Dawn co-led Bryter’s Series A of $16 million less than a year ago, in June 2020, a rapid funding pace that underscores both interest in the no-code/low-code space — Bryter’s enterprise customer base has doubled from 50 since then — and the fact that startups in it are striking while the iron is hot.

Bryter’s not the only one: Airtable, Genesis, Rows, Creatio, and Ushur are among the many startups building ‘hands-on tech creation for non-techie people’ that have raised money in the last several months.

Automation has been the bigger trend that has propelled a lot of this activity. Knowledge workers today spend most of their time these days in apps — a state of affairs that pre-dates the Covid-19 pandemic, but has definitely been furthered throughout it. While some of that work still requires manual involvement and evaluation from those workers, software has automated large swathes of those jobs.

RPA — robotic process automation, where companies like UiPath, Automation Anywhere and Blue Prism have taken a big lead — has accounted for a significant chunk of that activity, especially when it comes to reading forms and lots of data entry. But there remains a lot of other transactions and activities within specific apps where RPA is typically not used (not yet at least!). And this is where non-tech workers are finding that no-code tools like Bryter, which use artificial intelligence to deliver more personalised, yet scalable, automation, can play a very useful role.

“We sit on top of RPA in many cases,” said Grupp.

The company says that business functions where its platform has been implemented include compliance, legal, tax, privacy and security, procurement, administration, and HR, and the kinds of features that are being built include virtual assistants, chatbots, interactive self-service tools, and more.

These don’t replace people as such but cut down the time they need to spend in specific tasks to process and handle information within them, and could in theory also be used to build tools for customers to interact with services more easily, cutting down on the amount of time that agents are getting details and handling engagements.

That scalability, and the rapid customer up-take from a pool of users that extends beyond tech early-adopters, are part of what attracted the funding.

“Bryter has all the characteristics of a top-tier software company: high quality product that solves a real customer pain point, a large market opportunity and a world-class founding team,” said John Curtius, a partner at Tiger Global, in a statement. “The feedback from Bryter’s customers was resoundingly positive in our research, and we are excited to see the company reach new heights over the coming years.”

“Bryter has seen explosive growth over the last year, signing landmark customers across a large number of sectors and use cases. This does not come as a surprise. In the pandemic-affected world, digitalisation is no longer a nice to have, it is an imperative,” added Evgenia Plotnikova, a partner at Dawn Capital.

The longer we continue to work with either all or part of our teams in remote, out-of-physical-office environments, the more imperative it becomes for those teams to have some tools in place to keep the channels of communication and management open, and for the individuals in those teams to have a sense of how well they are performing. Today, one of the startups that provides a team productivity app with that in mind is announcing a round of funding to fuel its growth.

Pathlight, which has built a performance management platform for customer-facing teams — sales, field service and support — to help managers and employees themselves to track and analyze how they are doing, to coach them when and where it’s needed, and to communicate updates and more, has picked up $25 million — money that it will be using to continue growing its customer base and the functionality across its app.

The funding is being led by Insight Partners, with previous backers Kleiner Perkins and Quiet Capital also participating, alongside Uncorrelated Ventures; Jeremy Stoppelman, CEO of Yelp; David Glazer, CFO of Palantir; and Michael Ovitz, co-founder of CAA and Owner of Broad Beach Ventures. Pathlight has now raised $35 million.

Pathlight today provides users with a range of tools to visualize team and individual performance across various parameters set by managers, using data that teams integrate from other platforms like Salesforce, Zendesk and Outreach, among others.

Using that data and specific metrics for the job in question, managers can then initiate conversations with individuals to focus in on specific areas where things need attention, and provide some coaching to help fix it. It can also be used to provide team-wide updates and encouragement, which sits alongside whatever other tools a person might use in their daily customer-facing work.

Since launching in March 2020, the startup has picked up good traction, with customers including Twilio, Earnin, Greenhouse, and CLEAR. But perhaps even more importantly, the pandemic and resulting switch to remote work has underscored how necessary tools like Pathlight’s have become: the startup says that engagement on its platform has shot up 300% in the last 12 months.

Alexander Kvamme, the CEO of Pathlight, said that he first became aware of the challenges of communicating across customer-facing teams, and having transparency on how they are doing as individuals and as a group, when he was at Yelp. Yelp had acquired his startup, reservations service SeatMe, and used the acquisition to build and run Yelp Reservations.

He was quick to realize that there weren’t really effective tools for him to see how individuals in the sales team were doing, how they were doing compared to goals the company wanted to achieve and based on the sales data they already had in other systems, how to work more effectively with people to communicate when something needed changing, and how to tailor all that in line with new variations in the formula — in their case, how to sell new products like a reservations service alongside advertising and other Yelp services for businesses.

“Whether it’s five or 3,000 people, the problem doesn’t go away,” he said. “Everyone uses their own systems, and it hurts front line employees when they don’t know how they are doing, or don’t get recognition when they are doing well, or don’t get coaching when they are not. Our thesis was that if software is eating the world, and you as a company are buying more software and analytics, over time managers will be more like data analysts. So we are providing a way for managers to be more data-driven.”

Five years down the line, Kvamme got the bug again to start a company and decided to return to that problem, teaming up with co-founder Trey Doig, the engineer who designed SeatMe and then turned it into Yelp Reservations and is now Pathlight’s CTO.

As they see it, the challenge has still not really been addressed. That’s not to say that there are not a number of companies — competitors to Pathlight — looking to fill that gap as well. Another people management platform called Lattice last year picked up $45 million  (I’m guessing it will be raising money again around about now); HubSpot, Zoho, SalesLoft and a number of others also are taking different approaches to the same challenge: front-line customer-facing people spend the majority of their time and attention on interacting with people, and so there need to be better tools in place to help them figure out how to make that communication more effective, figure out what is working and what is not.

And all of this, of course, is not at all new: it’s not like we all woke up one day and suddenly wanted to know how we are doing at work, or managers suddenly felt they needed to communicate with staff.

What has changed, however, is how we work: many of us have not seen the inside of our offices for more than a year at this point, and for a large proportion of us, we may never return again, or if we do it will be under different circumstances.

All of this means that some of the more traditional metrics and indicators of our performance, praising, management relationships, and learning from team mates simply is not there anymore.

In customer-facing areas like sales, support and field service, that lack of contact may be even more acute, since many of the teams working in these environments have long relied on huddles and communication throughout the day, week and month to continuously tweak work and improve it. So while tools like Pathlight’s will be useful as data analytics provision for teams regardless of how we work, it can be argued that they are even more important right now.

“I think people have started to realize that if you can empower front line to be more independent, your numbers will go up and do better,” Kvamme said.

This is part of what went into the investment decision made here.

“With the acceleration of digital transformation across the enterprise, it’s not enough to rethink the way we work—we must also rethink the way we manage,” said Jeff Lieberman, MD at Insight Partners. “Pathlight is ushering in a new age of data-driven management, an ethos that we believe every enterprise will need to embrace—quickly. We are excited to partner with the Pathlight team as they bring their powerful platform to companies across the world.”

Machine learning (ML) models are only as good as the data you feed them. That’s true during training, but also once a model is put in production. In the real world, the data itself can change as new events occur and even small changes to how databases and APIs report and store data could have implications on how the models react. Since ML models will simply give you wrong predictions and not throw an error, it’s imperative that businesses monitor their data pipelines for these systems.

That’s where tools like Aporia come in. The Tel Aviv-based company today announced that it has raised a $5 million seed round for its monitoring platform for ML models. The investors are Vertex Ventures and TLV Partners.

Image Credits: Aporia

Aporia co-founder and CEO Liran Hason, after five years with the Israel Defense Forces, previously worked on the data science team at Adallom, a security company that was acquired by Microsoft in 2015. After the sale, he joined venture firm Vertex Ventures before starting Aporia in late 2019. But it was during his time at Adallom where he first encountered the problems that Aporio is now trying to solve.

“I was responsible for the production architecture of the machine learning models,” he said of his time at the company. “So that’s actually where, for the first time, I got to experience the challenges of getting models to production and all the surprises that you get there.”

The idea behind Aporia, Hason explained, is to make it easier for enterprises to implement machine learning models and leverage the power of AI in a responsible manner.

“AI is a super powerful technology,” he said. “But unlike traditional software, it highly relies on the data. Another unique characteristic of AI, which is very interesting, is that when it fails, it fails silently. You get no exceptions, no errors. That becomes really, really tricky, especially when getting to production, because in training, the data scientists have full control of the data.”

But as Hason noted, a production system may depend on data from a third-party vendor and that vendor may one day change the data schema without telling anybody about it. At that point, a model — say for predicting whether a bank’s customer may default on a loan — can’t be trusted anymore, but it may take weeks or months before anybody notices.

Aporia constantly tracks the statistical behavior of the incoming data and when that drifts too far away from the training set, it will alert its users.

One thing that makes Aporio unique is that it gives its users an almost IFTTT or Zapier-like graphical tool for setting up the logic of these monitors. It comes pre-configured with more than 50 combinations of monitors and provides full visibility in how they work behind the scenes. That, in turn, allows businesses to fine-tune the behavior of these monitors for their own specific business case and model.

Initially, the team thought it could build generic monitoring solutions. But the team realized that this wouldn’t only be a very complex undertaking, but that the data scientists who build the models also know exactly how those models should work and what they need from a monitoring solution.

“Monitoring production workloads is a well-established software engineering practice, and it’s past time for machine learning to be monitored at the same level,” said Rona Segev, founding partner at  TLV Partners. “Aporia‘s team has strong production-engineering experience, which makes their solution stand out as simple, secure and robust.”