Sep
18
2020
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SaaS Ventures takes the investment road less traveled

Most venture capital firms are based in hubs like Silicon Valley, New York City and Boston. These firms nurture those ecosystems and they’ve done well, but SaaS Ventures decided to go a different route: it went to cities like Chicago, Green Bay, Wisconsin and Lincoln, Nebraska.

The firm looks for enterprise-focused entrepreneurs who are trying to solve a different set of problems than you might find in these other centers of capital, issues that require digital solutions but might fall outside a typical computer science graduate’s experience.

Saas Ventures looks at four main investment areas: trucking and logistics, manufacturing, e-commerce enablement for industries that have not typically gone online and cybersecurity, the latter being the most mainstream of the areas SaaS Ventures covers.

The company’s first fund, which launched in 2017, was worth $20 million, but SaaS Ventures launched a second fund of equal amount earlier this month. It tends to stick to small-dollar-amount investments, while partnering with larger firms when it contributes funds to a deal.

We talked to Collin Gutman, founder and managing partner at SaaS Ventures, to learn about his investment philosophy, and why he decided to take the road less traveled for his investment thesis.

A different investment approach

Gutman’s journey to find enterprise startups in out of the way places began in 2012 when he worked at an early enterprise startup accelerator called Acceleprise. “We were really the first ones who said enterprise tech companies are wired differently, and need a different set of early-stage resources,” Gutman told TechCrunch.

Through that experience, he decided to launch SaaS Ventures in 2017, with several key ideas underpinning the firm’s investment thesis: after his experience at Acceleprise, he decided to concentrate on the enterprise from a slightly different angle than most early-stage VC establishments.

Collin Gutman from SaaS Ventures

Collin Gutman, founder and managing partner at SaaS Ventures (Image Credits: SaaS Ventures)

The second part of his thesis was to concentrate on secondary markets, which meant looking beyond the popular startup ecosystem centers and investing in areas that didn’t typically get much attention. To date, SaaS Ventures has made investments in 23 states and Toronto, seeking startups that others might have overlooked.

“We have really phenomenal coverage in terms of not just geography, but in terms of what’s happening with the underlying businesses, as well as their customers,” Gutman said. He believes that broad second-tier market data gives his firm an upper hand when selecting startups to invest in. More on that later.

Sep
18
2020
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Salesforce announces 12,000 new jobs in the next year just weeks after laying off 1,000

In a case of bizarre timing, Salesforce announced it was laying off 1,000 employees at the end of last month just a day after announcing a monster quarter with over $5 billion in revenue, putting the company on a $20 billion revenue run rate for the first time. The juxtaposition was hard to miss.

Earlier today, Salesforce CEO and co-founder Marc Benioff announced in a tweet that the company would be hiring 4,000 new employees in the next six months, and 12,000 in the next year. While it seems like a mixed message, it’s probably more about reallocating resources to areas where they are needed more.

While Salesforce wouldn’t comment further on the hirings, the company has obviously been doing well in spite of the pandemic, which has had an impact on customers. In the prior quarter, the company forecasted that it would have slower revenue growth due to giving some customers facing hard times with economic downturn time to pay their bills.

That’s why it was surprising when the CRM giant announced its earnings in August and that it had done so well in spite of all that. While the company was laying off those 1,000 people, it did indicate it would give those employees 60 days to find other positions in the company. With these new jobs, assuming they are positions the laid-off employees are qualified for, they could have a variety of positions from which to choose.

The company had 54,000 employees when it announced the layoffs, which accounted for 1.9% of the workforce. If it ends up adding the 12,000 news jobs in the next year, that would put the company at approximately 65,000 employees by this time next year.

Sep
17
2020
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Perigee infrastructure security solution from former NSA employee moves into public beta

Perigee founder Mollie Breen used to work for NSA where she built a security solution to help protect the agency’s critical infrastructure. She spent the last two years at Harvard Business School talking to Chief Information Security Officers (CISOs) and fine-tuning that idea she started at NSA into a commercial product.

Today, the solution that she built moves into public beta and will compete at TechCrunch Disrupt Battlefield with other startups for $100,000 and the Disrupt Cup.

Perigree helps protect things like heating and cooling systems or elevators that may lack patches or true security, yet are connected to the network in a very real way. It learns what normal behavior looks like from an operations system when it interacts with the network, such as what systems it interacts with and which individual employees tend to access it. It can then determine when something seems awry and stop an anomalous activity before it reaches the network. Without a solution like the one Breen has built, these systems would be vulnerable to attack.

Perigee is a cloud-based platform that creates a custom firewall for every device on your network,” Breen told TechCrunch. “It learns each device’s unique behavior, the quirks of its operational environment and how it interacts with other devices to prevent malicious and abnormal usage while providing analytics to boost performance.”

Perigee HVAC fan dashboard view

Image Credits: Perigee

One of the key aspects of her solution is that it doesn’t require an agent, a small piece of software on the device, to make it work. Breen says this is especially important since that approach doesn’t scale across thousands of devices and can also introduce bugs from the agent itself. What’s more, it can use up precious resources on these devices if they can even support a software agent.

“Our sweet spot is that we can protect those thousands of devices by learning those nuances and we can do that really quickly, scaling up to thousands of devices with our generalized model because we take this agentless-based approach,” she said.

By creating these custom firewalls, her company is able to place security in front of the device preventing a hacker from using it as a vehicle to get on the network.

“One thing that makes us fundamentally different from other companies out there is that we sit in front of all of these devices as a shield,” she said. That essentially stops an attack before it reaches the device.

While Breen acknowledges that her approach can add a small bit of latency, it’s a tradeoff that CISOs have told her they are willing to make to protect these kinds of operational systems from possible attacks. Her system is also providing real-time status updates on how these devices are operating, giving them centralized device visibility. If there are issues found, the software recommends corrective action.

It’s still very early for her company, which Breen founded last year. She has raised an undisclosed amount of pre-seed capital. While Perigee is pre-revenue with just one employee, she is looking to add paying customers and begin growing the company as she moves into a wider public beta.

Sep
17
2020
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APAC cloud infrastructure revenue reaches $9B in Q2 with Amazon leading the way

When you look at the Asia-Pacific (APAC) regional cloud infrastructure numbers, it would be easy to think that one of the Chinese cloud giants, particularly Alibaba, would be the leader in that geography, but new numbers from Synergy Research show Amazon leading across the region overall, which generated $9 billion in revenue in Q2.

The only exception to Amazon’s dominance was in China, where Alibaba leads the way with Tencent and Baidu coming in second and third, respectively. As Synergy’s John Dinsdale points out, China has its own unique market dynamics, and while Amazon leads in other APAC sub-regions, it remains competitive.

“China is a unique market and remains dominated by local companies, but beyond China there is strong competition between a range of global and local companies. Amazon is the leader in four of the five sub-regions, but it is not the market leader in every country,” he explained in a statement.

APAC Cloud Infrastructure leaders chart from Synergy Research

Image Credits: Synergy Research

The $9 billion in revenue across the region in Q2 represents less than a third of the more than $30 billion generated in the worldwide market in the quarter, but the APAC cloud market is still growing at more than 40% per year. It’s also worth pointing out as a means of comparison that Amazon alone generated more than the entire APAC region, with $10.81 billion in cloud infrastructure revenue in Q2.

While Dinsdale sees room for local vendors to grow, he says that the global nature of the cloud market in general makes it difficult for these players to compete with the largest companies, especially as they try to expand outside their markets.

“The challenge for local players is that in most ways cloud is a truly global market, requiring global presence, leading edge technology, strong brand name and credibility, extremely deep pockets and a long-term focus. For any local cloud companies looking to expand significantly beyond their home market, that is an extremely challenging proposition,” Dinsdale said in a statement.

Sep
16
2020
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Narrator raises $6.2M for a new approach to data modelling that replaces star schema

Snowflake went public this week, and in a mark of the wider ecosystem that is evolving around data warehousing, a startup that has built a completely new concept for modelling warehoused data is announcing funding. Narrator — which uses an 11-column ordering model rather than standard star schema to organise data for modelling and analysis — has picked up a Series A round of $6.2 million, money that it plans to use to help it launch and build up users for a self-serve version of its product.

The funding is being led by Initialized Capital along with continued investment from Flybridge Capital Partners and Y Combinator — where the startup was in a 2019 cohort — as well as new investors, including Paul Buchheit.

Narrator has been around for three years, but its first phase was based around providing modelling and analytics directly to companies as a consultancy, helping companies bring together disparate, structured data sources from marketing, CRM, support desks and internal databases to work as a unified whole. As consultants, using an earlier build of the tool that it’s now launching, the company’s CEO Ahmed Elsamadisi said he and others each juggled queries “for eight big companies single-handedly,” while deep-dive analyses were done by another single person.

Having validated that it works, the new self-serve version aims to give data scientists and analysts a simplified way of ordering data so that queries, described as actionable analyses in a story-like format — or “Narratives,” as the company calls them — can be made across that data quickly — hours rather than weeks — and consistently. (You can see a demo of how it works below provided by the company’s head of data, Brittany Davis.)

The new data-as-a-service is also priced in SaaS tiers, with a free tier for the first 5 million rows of data, and a sliding scale of pricing after that based on data rows, user numbers and Narratives in use.

Image Credits: Narrator

Elsamadisi, who co-founded the startup with Matt Star, Cedric Dussud and Michael Nason, said that data analysts have long lived with the problems with star schema modelling (and by extension the related format of snowflake schema), which can be summed up as “layers of dependencies, lack of source of truth, numbers not matching and endless maintenance,” he said.

“At its core, when you have lots of tables built from lots of complex SQL, you end up with a growing house of cards requiring the need to constantly hire more people to help make sure it doesn’t collapse.”

(We)Work Experience

It was while he was working as lead data scientist at WeWork — yes, he told me, maybe it wasn’t actually a tech company, but it had “tech at its core” — that he had a breakthrough moment of realising how to restructure data to get around these issues.

Before that, things were tough on the data front. WeWork had 700 tables that his team was managing using a star schema approach, covering 85 systems and 13,000 objects. Data would include information on acquiring buildings, to the flows of customers through those buildings, how things would change and customers might churn, with marketing and activity on social networks, and so on, growing in line with the company’s own rapidly scaling empire.  All of that meant a mess at the data end.

“Data analysts wouldn’t be able to do their jobs,” he said. “It turns out we could barely even answer basic questions about sales numbers. Nothing matched up, and everything took too long.”

The team had 45 people on it, but even so it ended up having to implement a hierarchy for answering questions, as there were so many and not enough time to dig through and answer them all. “And we had every data tool there was,” he added. “My team hated everything they did.”

The single-table column model that Narrator uses, he said, “had been theorised” in the past but hadn’t been figured out.

The spark, he said, was to think of data structured in the same way that we ask questions, where — as he described it — each piece of data can be bridged together and then also used to answer multiple questions.

“The main difference is we’re using a time-series table to replace all your data modelling,” Elsamadisi explained. “This is not a new idea, but it was always considered impossible. In short, we tackle the same problem as most data companies to make it easier to get the data you want but we are the only company that solves it by innovating on the lowest-level data modelling approach. Honestly, that is why our solution works so well. We rebuilt the foundation of data instead of trying to make a faulty foundation better.”

Narrator calls the composite table, which includes all of your data reformatted to fit in its 11-column structure, the Activity Stream.

Elsamadisi said using Narrator for the first time takes about 30 minutes, and about a month to learn to use it thoroughly. “But you’re not going back to SQL after that, it’s so much faster,” he added.

Narrator’s initial market has been providing services to other tech companies, and specifically startups, but the plan is to open it up to a much wider set of verticals. And in a move that might help with that, longer term, it also plans to open source some of its core components so that third parties can build data products on top of the framework more quickly.

As for competitors, he says that it’s essentially the tools that he and other data scientists have always used, although “we’re going against a ‘best practice’ approach (star schema), not a company.” Airflow, DBT, Looker’s LookML, Chartio’s Visual SQL, Tableau Prep are all ways to create and enable the use of a traditional star schema, he added. “We’re similar to these companies — trying to make it as easy and efficient as possible to generate the tables you need for BI, reporting and analysis — but those companies are limited by the traditional star schema approach.”

So far the proof has been in the data. Narrator says that companies average around 20 transformations (the unit used to answer questions) compared to hundreds in a star schema, and that those transformations average 22 lines compared to 1,000+ lines in traditional modelling. For those that learn how to use it, the average time for generating a report or running some analysis is four minutes, compared to weeks in traditional data modelling. 

“Narrator has the potential to set a new standard in data,” said Jen Wolf, ?Initialized Capital COO and partner and new Narrator board member?, in a statement. “We were amazed to see the quality and speed with which Narrator delivered analyses using their product. We’re confident once the world experiences Narrator this will be how data analysis is taught moving forward.”

Sep
16
2020
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Luther.AI is a new AI tool that acts like Google for personal conversations

When it comes to pop culture, a company executive or history questions, most of us use Google as a memory crutch to recall information we can’t always keep in our heads, but Google can’t help you remember the name of your client’s spouse or the great idea you came up with at a meeting the other day.

Enter Luther.AI, which purports to be Google for your memory by capturing and transcribing audio recordings, while using AI to deliver the right information from your virtual memory bank in the moment of another online conversation or via search.

The company is releasing an initial browser-based version of their product this week at TechCrunch Disrupt where it’s competing for the $100,000 prize at TechCrunch Disrupt Battlefield.

Luther.AI’s founders say the company is built on the premise that human memory is fallible, and that weakness limits our individual intelligence. The idea behind Luther.AI is to provide a tool to retain, recall and even augment our own brains.

It’s a tall order, but the company’s founders believe it’s possible through the growing power of artificial intelligence and other technologies.

“It’s made possible through a convergence of neuroscience, NLP and blockchain to deliver seamless in-the-moment recall. GPT-3 is built on the memories of the public internet, while Luther is built on the memories of your private self,” company founder and CEO Suman Kanuganti told TechCrunch.

It starts by recording your interactions throughout the day. For starters, that will be online meetings in a browser, as we find ourselves in a time where that is the way we interact most often. Over time though, they envision a high-quality 5G recording device you wear throughout your day at work and capture your interactions.

If that is worrisome to you from a privacy perspective, Luther is building in a few safeguards starting with high-end encryption. Further, you can only save other parties’ parts of a conversation with their explicit permission. “Technologically, we make users the owner of what they are speaking. So for example, if you and I are having a conversation in the physical world unless you provide explicit permission, your memories are not shared from this particular conversation with me,” Kanuganti explained.

Finally, each person owns their own data in Luther and nobody else can access or use these conversations either from Luther or any other individual. They will eventually enforce this ownership using blockchain technology, although Kanuganti says that will be added in a future version of the product.

Luther.ai search results recalling what person said at meeting the other day about customer feedback.

Image Credits: Luther.ai

Kanuganti says the true power of the product won’t be realized with a few individuals using the product inside a company, but in the network effect of having dozens or hundreds of people using it, even though it will have utility even for an individual to help with memory recall, he said.

While they are releasing the browser-based product this week, they will eventually have a stand-alone app, and can also envision other applications taking advantage of the technology in the future via an API where developers can build Luther functionality into other apps.

The company was founded at the beginning of this year by Kanuganti and three co-founders including CTO Sharon Zhang, design director Kristie Kaiser and scientist Marc Ettlinger . It has raised $500,000 and currently has 14 employees including the founders.

Sep
16
2020
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ServiceNow updates its workflow automation platform

ServiceNow today announced the latest release of its workflow automation platform. With this, the company is emphasizing a number of new solutions for specific verticals, including for telcos and financial services organizations. This focus on verticals extends the company’s previous efforts to branch out beyond the core IT management capabilities that defined its business during its early years. The company is also adding new features for making companies more resilient in the face of crises, as well as new machine learning-based tools.

Dubbed the “Paris” release, this update also marks one of the first major releases for the company since former SAP CEO Bill McDermott became its president and CEO last November.

“We are in the business of operating on purpose,” McDermott said. “And that purpose is to make the world of work work better for people. And frankly, it’s all about people. That’s all CEOs talk about all around the world. This COVID environment has put the focus on people. In today’s world, how do you get people to achieve missions across the enterprise? […] Businesses are changing how they run to drive customer loyalty and employee engagement.”

He argues that at this point, “technology is no longer supporting the business, technology is the business,” but at the same time, the majority of companies aren’t prepared to meet whatever digital disruption comes their way. ServiceNow, of course, wants to position itself as the platform that can help these businesses.

“We are very fortunate at ServiceNow,” CJ Desai, ServiceNow’s chief product officer, said. “We are the critical platform for digital transformation, as our customers are thinking about transforming their companies.”

As far as the actual product updates, ServiceNow is launching a total of six new products. These include new business continuity management features with automated business impact analysis and tools for continuity plan development, as well as new hardware asset management for IT teams and legal service delivery for legal operations teams.

Image Credits: ServiceNow

With specialized solutions for financial services and telco users, the company is also now bringing together some of its existing solutions with more specialized services for these customers. As ServiceNow’s Dave Wright noted, this goes well beyond just putting together existing blocks.

“The first element is actually getting familiar with the business,” he explained. “So the technology, actually building the product, isn’t that hard. That’s relatively quick. But the uniqueness when you look at all of these workflows, it’s the connection of the operations to the customer service side. Telco is a great example. You’ve got the telco network operations side, making sure that all the operational equipment is active. And then you’ve got the business service side with customer service management, looking at how the customers are getting service. Now, the interesting thing is, because we’ve got both things sitting on one platform, we can link those together really easily.”

Image Credits: ServiceNow

On the machine learning side, ServiceNow made six acquisitions in the area in the last four years, Wright noted — and that is now starting to pay off. Specifically, the company is launching its new predictive intelligence workbench with this release. This new service makes it easier for process owners to detect issues, while also suggesting relevant tasks and content to agents, for example, and prioritizing incoming requests automatically. Using unsupervised learning, the system can also identify other kinds of patterns and with a number of pre-built templates, users can build their own solutions, too.

“The ServiceNow advantage has always been one architecture, one data model and one born-in-the-cloud platform that delivers workflows companies need and great experiences employees and customers expect,” said Desai. “The Now Platform Paris release provides smart experiences powered by AI, resilient operations, and the ability to optimize spend. Together, they will provide businesses with the agility they need to help them thrive in the COVID economy.”

Sep
15
2020
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Dropbox CEO Drew Houston says the pandemic forced the company to reevaluate what work means

Dropbox CEO and co-founder Drew Houston, appearing at TechCrunch Disrupt today, said that COVID has accelerated a shift to distributed work that we have been talking about for some time, and these new ways of working will not simply go away when the pandemic is over.

“When you think more broadly about the effects of the shift to distributed work, it will be felt well beyond when we go back to the office. So we’ve gone through a one-way door. This is maybe one of the biggest changes to knowledge work since that term was invented in 1959,” Houston told TechCrunch Editor-In-Chief Matthew Panzarino.

That change has prompted Dropbox to completely rethink the product set over the last six months, as the company has watched the way people work change in such a dramatic way. He said even though Dropbox is a cloud service, no SaaS tool in his view was purpose-built for this new way of working and we have to reevaluate what work means in this new context.

“Back in March we started thinking about this, and how [the rapid shift to distributed work] just kind of happened. It wasn’t really designed. What if you did design it? How would you design this experience to be really great? And so starting in March we reoriented our whole product road map around distributed work,” he said.

He also broadly hinted that the fruits of that redesign are coming down the pike. “We’ll have a lot more to share about our upcoming launches in the future,” he said.

Houston said that his company has adjusted well to working from home, but when they had to shut down the office, he was in the same boat as every other CEO when it came to running his company during a pandemic. Nobody had a blueprint on what to do.

“When it first happened, I mean there’s no playbook for running a company during a global pandemic so you have to start with making sure you’re taking care of your customers, taking care of your employees, I mean there’s so many people whose lives have been turned upside down in so many ways,” he said.

But as he checked in on the customers, he saw them asking for new workflows and ways of working, and he recognized there could be an opportunity to design tools to meet these needs.

“I mean this transition was about as abrupt and dramatic and unplanned as you can possibly imagine, and being able to kind of shape it and be intentional is a huge opportunity,” Houston said.

Houston debuted Dropbox in 2008 at the precursor to TechCrunch Disrupt, then called the TechCrunch 50. He mentioned that the Wi-Fi went out during his demo, proving the hazards of live demos, but offered words of encouragement to this week’s TechCrunch Disrupt Battlefield participants.

Although his is a public company on a $1.8 billion run rate, he went through all the stages of a startup, getting funding and eventually going public, and even today as a mature public company, Dropbox is still evolving and changing as it adapts to changing requirements in the marketplace.

Sep
15
2020
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In 2020, Warsaw’s startup ecosystem is ‘a place to observe carefully’

If you listed the trends that have captured the attention of 20 Warsaw-focused investors who replied to our recent surveys, automation/AI, enterprise SaaS, cleantech, health, remote work and the sharing economy would top the list. These VCs said they are seeking opportunities in the “digital twin” space, proptech and expanded blockchain tokenization inside industries.

Investors in Central and Eastern Europe are generally looking for the same things as VCs based elsewhere: startups that have a unique value proposition, capital efficiency, motivated teams, post-revenue and a well-defined market niche.

Out of the cohort we interviewed, several told us that COVID-19 had not yet substantially transformed how they do business. As Micha? Papuga, a partner at Flashpoint VC put it, “the situation since March hasn’t changed a lot, but we went from extreme panic to extreme bullishness. Neither of these is good and I would recommend to stick to the long-term goals and not to be pressured.”

Said Pawel Lipkowski of RBL_VC, “Warsaw is at its pivotal point — think Berlin in the ‘90s. It’s a place to observe carefully.”

Here’s who we interviewed for part one:

For the conclusion, we spoke to the following investors:

Karol Szubstarski, partner, OTB Ventures

What trends are you most excited about investing in, generally?
Gradual shift of enterprises toward increased use of automation and AI, that enables dramatic improvement of efficiency, cost reduction and transfer of enterprise resources from tedious, repeatable and mundane tasks to more exciting, value added opportunities.

What’s your latest, most exciting investment?
One of the most exciting opportunities is ICEYE. The company is a leader and first mover in synthetic-aperture radar (SAR) technology for microsatellites. It is building and operating its own commercial constellation of SAR microsatellites capable of providing satellite imagery regardless of the cloud cover, weather conditions and time of the day and night (comparable resolution to traditional SAR satellites with 100x lower cost factor), which is disrupting the multibillion dollar satellite imagery market.

Are there startups that you wish you would see in the industry but don’t? What are some overlooked opportunities right now?
I would love to see more startups in the digital twin space; technology that enables creation of an exact digital replica/copy of something in physical space — a product, process or even the whole ecosystem. This kind of solution enables experiments and [the implementation of] changes that otherwise could be extremely costly or risky – it can provide immense value added for customers.

What are you looking for in your next investment, in general?
A company with unique value proposition to its customers, deep tech component that provides competitive edge over other players in the market and a founder with global vision and focus on execution of that vision.

Which areas are either oversaturated or would be too hard to compete in at this point for a new startup? What other types of products/services are you wary or concerned about?
No market/sector is too saturated and has no room for innovation. Some markets seem to be more challenging than others due to immense competitive landscape (e.g., food delivery, language-learning apps) but still can be the subject of disruption due to a unique value proposition of a new entrant.

How much are you focused on investing in your local ecosystem versus other startup hubs (or everywhere) in general? More than 50%? Less?
OTB is focused on opportunities with links to Central Eastern European talent (with no bias toward any hub in the region), meaning companies that leverage local engineering/entrepreneurial talent in order to build world-class products to compete globally (usually HQ outside CEE).

Which industries in your city and region seem well-positioned to thrive, or not, long term? What are companies you are excited about (your portfolio or not), which founders?
CEE region is recognized for its sizable and highly skilled talent pool in the fields of engineering and software development. The region is well-positioned to build up solutions that leverage deep, unique tech regardless of vertical (especially B2B). Historically, the region was especially strong in AI/ML, voice/speech/NLP technologies, cybersecurity, data analytics, etc.

How should investors in other cities think about the overall investment climate and opportunities in your city?
CEE (including Poland and Warsaw) has always been recognized as an exceptionally strong region in terms of engineering/IT talent. Inherent risk aversion of entrepreneurs has driven, for a number of years, a more “copycat”/local market approach, while holding back more ambitious, deep tech opportunities. In recent years we are witnessing a paradigm shift with a new generation of entrepreneurs tackling problems with unique, deep tech solutions, putting emphasis on global expansion, neglecting shallow local markets. As such, the quality of deals has been steadily growing and currently reflects top quality on global scale, especially on tech level. CEE market demonstrates also a growing number of startups (in total), which is mostly driven by an abundance of early-stage capital and success stories in the region (e.g., DataRobot, Bolt, UiPath) that are successfully evangelizing entrepreneurship among corporates/engineers.

Do you expect to see a surge in more founders coming from geographies outside major cities in the years to come, with startup hubs losing people due to the pandemic and lingering concerns, plus the attraction of remote work?
I believe that local hubs will hold their dominant position in the ecosystem. The remote/digital workforce will grow in numbers but proximity to capital, human resources and markets still will remain the prevalent force in shaping local startup communities.

Which industry segments that you invest in look weaker or more exposed to potential shifts in consumer and business behavior because of COVID-19? What are the opportunities startups may be able to tap into during these unprecedented times?
OTB invests in general in companies with clearly defined technological advantage, making quantifiable and near-term difference to their customers (usually in the B2B sector), which is a value-add regardless of the market cycle. The economic downturn works generally in favor of technological solutions enabling enterprise clients to increase efficiency, cut costs, bring optimization and replace manual labour with automation — and the vast majority of OTB portfolio fits that description. As such, the majority of the OTB portfolio has not been heavily impacted by the COVID pandemic.

How has COVID-19 impacted your investment strategy? What are the biggest worries of the founders in your portfolio? What is your advice to startups in your portfolio right now?
The COVID pandemic has not impacted our investment strategy in any way. OTB still pursues unique tech opportunities that can provide its customers with immediate value added. This kind of approach provides a relatively high level of resilience against economic downturns (obviously, sales cycles are extending but in general sales pipeline/prospects/retention remains intact). Liquidity in portfolio is always the number one concern in uncertain, challenging times. Lean approach needs to be reintroduced, companies need to preserve cash and keep optimizing — that’s the only way to get through the crisis.

Are you seeing “green shoots” regarding revenue growth, retention or other momentum in your portfolio as they adapt to the pandemic?
A good example in our portfolio is Segron, a provider of an automated testing platform for applications, databases and enterprise network infrastructure. Software development, deployment and maintenance in enterprise IT ecosystem requires continuous and rigorous testing protocols and as such a lot of manual heavy lifting with highly skilled engineering talent being involved (which can be used in a more productive way elsewhere). The COVID pandemic has kept engineers home (with no ability for remote testing) while driving demand for digital services (and as such demand for a reliable IT ecosystem). The Segron automated framework enables full automation of enterprise testing leading to increased efficiency, cutting operating costs and giving enterprise customers peace of mind and a good night’s sleep regarding their IT infrastructure in the challenging economic environment.

What is a moment that has given you hope in the last month or so? This can be professional, personal or a mix of the two.
I remain impressed by the unshakeable determination of multiple founders and their teams to overcome all the challenges of the unfavorable economic ecosystem.

Sep
15
2020
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Latent AI makes edge AI workloads more efficient

Latent AI, a startup that was spun out of SRI International, makes it easier to run AI workloads at the edge by dynamically managing workloads as necessary.

Using its proprietary compression and compilation process, Latent AI promises to compress library files by 10x and run them with 5x lower latency than other systems, all while using less power thanks to its new adaptive AI technology, which the company is launching as part of its appearance in the TechCrunch Disrupt Battlefield competition today.

Founded by CEO Jags Kandasamy and CTO Sek Chai, the company has already raised a $6.5 million seed round led by Steve Jurvetson of Future Ventures and followed by Autotech Ventures .

Before starting Latent AI, Kandasamy sold his previous startup OtoSense to Analog Devices (in addition to managing HPE Mid-Market Security business before that). OtoSense used data from sound and vibration sensors for predictive maintenance use cases. Before its sale, the company worked with the likes of Delta Airlines and Airbus.

Image Credits: Latent AI

In some ways, Latent AI picks up some of this work and marries it with IP from SRI International .

“With OtoSense, I had already done some edge work,” Kandasamy said. “We had moved the audio recognition part out of the cloud. We did the learning in the cloud, but the recognition was done in the edge device and we had to convert quickly and get it down. Our bill in the first few months made us move that way. You couldn’t be streaming data over LTE or 3G for too long.”

At SRI, Chai worked on a project that looked at how to best manage power for flying objects where, if you have a single source of power, the system could intelligently allocate resources for either powering the flight or running the onboard compute workloads, mostly for surveillance, and then switch between them as needed. Most of the time, in a surveillance use case, nothing happens. And while that’s the case, you don’t need to compute every frame you see.

“We took that and we made it into a tool and a platform so that you can apply it to all sorts of use cases, from voice to vision to segmentation to time series stuff,” Kandasamy explained.

What’s important to note here is that the company offers the various components of what it calls the Latent AI Efficient Inference Platform (LEIP) as standalone modules or as a fully integrated system. The compressor and compiler are the first two of these and what the company is launching today is LEIP Adapt, the part of the system that manages the dynamic AI workloads Kandasamy described above.

Image Credits: Latent AI

In practical terms, the use case for LEIP Adapt is that your battery-powered smart doorbell, for example, can run in a low-powered mode for a long time, waiting for something to happen. Then, when somebody arrives at your door, the camera wakes up to run a larger model — maybe even on the doorbell’s base station that is plugged into power — to do image recognition. And if a whole group of people arrives at ones (which isn’t likely right now, but maybe next year, after the pandemic is under control), the system can offload the workload to the cloud as needed.

Kandasamy tells me that the interest in the technology has been “tremendous.” Given his previous experience and the network of SRI International, it’s maybe no surprise that Latent AI is getting a lot of interest from the automotive industry, but Kandasamy also noted that the company is working with consumer companies, including a camera and a hearing aid maker.

The company is also working with a major telco company that is looking at Latent AI as part of its AI orchestration platform and a large CDN provider to help them run AI workloads on a JavaScript backend.

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