Jun
04
2020
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Bryter raises $16M for a no-code platform for non-technical people to build enterprise automation apps

Automation is the name of the game in enterprise IT at the moment: We now have a plethora of solutions on the market to speed up your workflow, simplify a process and perform more repetitive tasks without humans getting involved. Now, a startup that is helping non-technical people get more directly involved in how to make automation work better for their tasks is announcing some funding to seize the opportunity.

Bryter — a no-code platform based in Berlin that lets workers in departments like accounting, legal, compliance and marketing who do not have any special technical or developer skills build tools like chatbots, trigger automated database and document actions and risk assessors — is today announcing that it has raised $16 million. This is a Series A round being co-led by Accel and Dawn Capital, with Notion Capital and Chalfen Ventures also participating.

The funding comes less than a year after Bryter raised a seed round — $6 million in November 2019 — and it was oversubscribed, with term sheets coming in from many of the bigger VCs in Europe and the U.S. With this funding, the company has now raised around $25 million, and while the valuation is considerably up on the last round, Bryter is not disclosing what it is.

Michael Grupp, the CEO who co-founded the company with Micha-Manuel Bues and Michael Hübl (pictured below), said that the whole Series A process took no more than a month to initiate and close, an impressive turnaround considering the chilling effect that the COVID-19 health pandemic has had on dealmaking.

Part of the reason for the enthusiasm is because of the traction that Bryter has had since launching in 2018. Its 50 enterprise customers include the likes of McDonald’s, Telefónica, banks, healthcare and industrial companies, and professional services firms PwC, KPMG and Deloitte (who in turn use it for themselves as well as for clients). (Note: Because of its target users being large enterprises, the company doesn’t publish per-person pricing on its site as such.)

Bryter’s been seeing a lot of attention from customers and investors because its platform speaks to a big opportunity within the wider world of software today.

Enterprise IT has long been thought of as the less-fun end of technology: It’s all about getting work done, and a lot of the software used in a business environment is complex and often requires technical knowledge to implement, use, fix and adapt in any way.

This may still the case for a lot of it, especially for the most sophisticated tools, but at the same time we have seen a lot of “consumerization” come into IT, where user-friendly hardware and software built for consumers — specifically non-technical consumers — either inspires new enterprise services, or are simply directly imported into the workplace environment.

No-code software — like automation, another big trend in enterprise IT right now — plays a big role in how enterprise tools are becoming more user-friendly. One of the biggest roadblocks in a lot of office environments is that when workers identify things that don’t work, or could work much better than they do, they need to file tickets and get IT teams — also often overworked — to do the fixing for them. No-code platforms can help circumvent some of that work — so long as the roadblock of IT approves the use, that is.

Bryter’s conception and existence comes out of the no-code trend. It plays on the same ideas as IFTTT or Zapier but is very firmly aimed at users who might use pieces of enterprise software as part of their jobs, but have never had to delve into figuring out how they actually work.

There are already a lot of “low-code” (minimal coding) and other no-code platforms on the market today for business (not consumer) use cases. They include Blender.io, Zapier, Tray.io (a London-founded startup that itself raised a big round last autumn), n8n (also German, backed by Sequoia), and also biggies like MuleSoft (acquired by Salesforce in 2018 at a $6.5 billion valuation).

Bryter’s contention is that many of these actually need more technical know-how than they initially claim. Grupp pointed out that the earliest automation tools for enterprise have been around for decades at this point, but even most of the very modern descendants of those “will require some coding.” Bryter’s toolbox essentially lets users create dialogues with users — which they can program based on the expertise that they will have in their particular fields — which then sources data they can then plug into other software via the Bryter platform in order to “perform” different tasks more quickly.

Grupp’s contention is that while these kinds of tools have long been used, they will be in even more demand going forward.

“After COVID-19, workers will be even more distributed,” he said. “Teams and individuals will need to access information in a faster way, and the only way for big organizations to distribute that knowledge is through more digital tools.” The idea is that Bryter can essentially help bridge those gaps in a more efficient way.

Bryter’s target user and its approach underscores why investors like Accel see accessible, no-code solutions as a big opportunity.

“No-code software is really reducing the barriers of adoption,” Luca Bocchio, a partner at Accel, said in an interview. “If people like you and I can use the software, then that means demand can multiply by big numbers.” That’s in contrast to a lot of enterprise software today, which is very limited in how it can grow, he added. “Plus, enterprises these days want to see more future visibility in terms of the products they adopt. They want to make sure something will stick around, and so they tend not to want to work with super young startups. But it’s happening for Bryter, and the is a testament to Bryter and to the market potential.”

Nov
14
2019
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Eigen nabs $37M to help banks and others parse huge documents using natural language and ‘small data’

One of the bigger trends in enterprise software has been the emergence of startups building tools to make the benefits of artificial intelligence technology more accessible to non-tech companies. Today, one that has built a platform to apply the power of machine learning and natural language processing to massive documents of unstructured data has closed a round of funding as it finds strong demand for its approach.

Eigen Technologies, a London-based startup whose machine learning engine helps banks and other businesses that need to extract information and insights from large and complex documents like contracts, is today announcing that it has raised $37 million in funding, a Series B that values the company at around $150 million – $180 million.

The round was led by Lakestar and Dawn Capital, with Temasek and Goldman Sachs Growth Equity (which co-led its Series A) also participating. Eigen has now raised $55 million in total.

Eigen today is working primarily in the financial sector — its offices are smack in the middle of The City, London’s financial center — but the plan is to use the funding to continue expanding the scope of the platform to cover other verticals such as insurance and healthcare, two other big areas that deal in large, wordy documentation that is often inconsistent in how its presented, full of essential fine print, and typically a strain on an organisation’s resources to be handled correctly — and is often a disaster if it is not.

The focus up to now on banks and other financial businesses has had a lot of traction. It says its customer base now includes 25% of the world’s G-SIB institutions (that is, the world’s biggest banks), along with others that work closely with them, like Allen & Overy and Deloitte. Since June 2018 (when it closed its Series A round), Eigen has seen recurring revenues grow sixfold with headcount — mostly data scientists and engineers — double. While Eigen doesn’t disclose specific financials, you can see the growth direction that contributed to the company’s valuation.

The basic idea behind Eigen is that it focuses what co-founder and CEO Lewis Liu describes as “small data.” The company has devised a way to “teach” an AI to read a specific kind of document — say, a loan contract — by looking at a couple of examples and training on these. The whole process is relatively easy to do for a non-technical person: you figure out what you want to look for and analyse, find the examples using basic search in two or three documents and create the template, which can then be used across hundreds or thousands of the same kind of documents (in this case, a loan contract).

Eigen’s work is notable for two reasons. First, typically machine learning and training and AI requires hundreds, thousands, tens of thousands of examples to “teach” a system before it can make decisions that you hope will mimic those of a human. Eigen requires a couple of examples (hence the “small data” approach).

Second, an industry like finance has many pieces of sensitive data (either because it’s personal data, or because it’s proprietary to a company and its business), and so there is an ongoing issue of working with AI companies that want to “anonymise” and ingest that data. Companies simply don’t want to do that. Eigen’s system essentially only works on what a company provides, and that stays with the company.

Eigen was founded in 2014 by Dr. Lewis Z. Liu (CEO) and Jonathan Feuer (a managing partner at CVC Capital Partners, who is the company’s chairman), but its earliest origins go back 15 years earlier, when Liu — a first-generation immigrant who grew up in the U.S. — was working as a “data-entry monkey” (his words) at a tire manufacturing plant in New Jersey, where he lived, ahead of starting university at Harvard.

A natural computing whiz who found himself building his own games when his parents refused to buy him a games console, he figured out that the many pages of printouts he was reading and re-entering into a different computing system could be sped up with a computer program linking up the two. “I put myself out of a job,” he joked.

His educational life epitomises the kind of lateral thinking that often produces the most interesting ideas. Liu went on to Harvard to study not computer science, but physics and art. Doing a double major required working on a thesis that merged the two disciplines together, and Liu built “electrodynamic equations that composed graphical structures on the fly” — basically generating art using algorithms — which he then turned into a “Turing test” to see if people could detect pixelated actual work with that of his program. Distill this, and Liu was still thinking about patterns in analog material that could be re-created using math.

Then came years at McKinsey in London (how he arrived on these shores) during the financial crisis where the results of people either intentionally or mistakenly overlooking crucial text-based data produced stark and catastrophic results. “I would say the problem that we eventually started to solve for at Eigen became tangible,” Liu said.

Then came a physics PhD at Oxford where Liu worked on X-ray lasers that could be used to decrease the complexity and cost of making microchips, cancer treatments and other applications.

While Eigen doesn’t actually use lasers, some of the mathematical equations that Liu came up with for these have also become a part of Eigen’s approach.

“The whole idea [for my PhD] was, ‘how do we make this cheaper and more scalable?,’ ” he said. “We built a new class of X-ray laser apparatus, and we realised the same equations could be used in pattern matching algorithms, specifically around sequential patterns. And out of that, and my existing corporate relationships, that’s how Eigen started.”

Five years on, Eigen has added a lot more into the platform beyond what came from Liu’s original ideas. There are more data scientists and engineers building the engine around the basic idea, and customising it to work with more sectors beyond finance. 

There are a number of AI companies building tools for non-technical business end-users, and one of the areas that comes close to what Eigen is doing is robotic process automation, or RPA. Liu notes that while this is an important area, it’s more about reading forms more readily and providing insights to those. The focus of Eigen is more on unstructured data, and the ability to parse it quickly and securely using just a few samples.

Liu points to companies like IBM (with Watson) as general competitors, while startups like Luminance is another taking a similar approach to Eigen by addressing the issue of parsing unstructured data in a specific sector (in its case, currently, the legal profession).

Stephen Nundy, a partner and the CTO of Lakestar, said that he first came into contact with Eigen when he was at Goldman Sachs, where he was a managing director overseeing technology, and the bank engaged it for work.

“To see what these guys can deliver, it’s to be applauded,” he said. “They’re not just picking out names and addresses. We’re talking deep, semantic understanding. Other vendors are trying to be everything to everybody, but Eigen has found market fit in financial services use cases, and it stands up against the competition. You can see when a winner is breaking away from the pack and it’s a great signal for the future.”

Jun
25
2019
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Showpad, a sales enablement platform for presentations and other collateral, raises $70M

Sales teams have long turned to tech solutions to help improve how they source leads, develop relationships and close deals. Now, one of the startups that helps out at a key point in that trajectory is announcing a round of growth funding to help fuel its own rapid growth. Showpad, a sales enablement platform that lets salespeople source and organise relevant content and other collateral that they use in their deals, has raised a Series D of $70 million.

The funding, which brings the total raised by Showpad to $160 million, is coming in the form of debt and equity. The equity part is co-led by Dawn Capital and Insight Partners, with existing investors Hummingbird Ventures, and Korelya Capital also participating. Silicon Valley Bank is providing debt financing. This is one of the first big investments out of Dawn’s Opportunities Fund that we wrote about last week.

The company is not disclosing its valuation but Pieterjan Bouten, the CEO who co-founded the company with Louis Jonckheere (currently CPO) and Peter Minne (CTO), confirmed that it has doubled since last year, and is seven times the valuation it had when it raised a $50 million Series C in 2016. The company is growing 90% year-on-year at the moment in terms of revenues.

And as a point of reference, another sales enablement player, Seismic, last December raised a Series E of $100 million at a $1 billion valuation.

Founded in Ghent, Belgium, Showpad today operates across two main headquarters, its original European base and Chicago. The latter was the homebase of LearnCore, a company that Showpad acquired last year that focuses on sales coaching and training. This became a strategic acquisition to expand Showpad’s primary product, a platform that acts as a kind of content management system for sales collateral. (Today, while Chicago is where Showpad builds its go-to market efforts and professional services, Ghent focuses on engineering and product, he said.) As it happens, Chicago is also the headquarters of Seismic.

As Bouten described in an interview, Showpad is part of what he considers to be the fourth pillar of the technology marketing stack: storage (the cloud services where you keep all your data), CRM, marketing automation and sales enablement, where Showpad sits.

While the first three are key to helping to manage a salesperson’s activities and work, the fourth is a crucial one for helping to make sure a salesperson can do his or her job more effectively.

Traditionally a lot of the content that salespeople used — presentations, white papers, other materials — to help make their cases and close their deals would be managed offline and directly by individual salespeople. Showpad has taken some of that process and made it digital, which means that now teams of salespeople can more effectively share materials amongst each other; and interestingly the material and its link to successful sales becomes part of how Showpad “learns” what works and what doesn’t.

That, in turn, helps build Showpad’s own artificial intelligence algorithms, to help suggest the best materials for a particular sales effort either to someone else in that team, or to other salespeople using the platform.

“To date there has been enormous innovation in automating the marketing and sales workflow. However, in the end, sales comes down to one person selling to another,” said Norman Fiore, General Partner at Dawn Capital and member of the Showpad Board, in a statement. “Historically, this has been an offline process that has been wildly inconsistent and opaque. Showpad’s suite of products succeeds in bringing this process online for the first time with data-rich feedback loops on the effectiveness of teams, managers, salespeople and even individual pieces of sales content.”

This is a crowded area of the market with a number of standalone companies building sales enablement solutions, but also other companies within the sales stack also adding on enablement as a value-added service.

For now, though, Bouten notes that these are more strategic partners than competitors. For example, Salesforce and Microsoft are partners, and, he adds, “We integrate with Salesloft to make sure sure emails that are sent out are using the right content. We become the single source of truth but also are being used for outreach.”

Today, the company has around 1,200 enterprise customers, including Johnson & Johnson, GE Healthcare, Bridgestone, Honeywell, and Merck. The plan going forward will be to continue building out the services that it offers around its sales enablement software, alongside the core product itself.

“You can equip sales people with the best content, but if they are not trained and coached in the right way, it goes nowhere,” Bouten said.

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