machine learning

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Sanas aims to convert one accent to another in real time for smoother customer service calls

In the customer service industry, your accent dictates many aspects of your job. It shouldn’t be the case that there’s a “better” or “worse” accent, but in today’s global economy (though who knows about tomorrow’s) it’s valuable to sound American or British. While many undergo accent neutralization training, Sanas is a startup with another approach (and a $5.5M seed round): using speech recognition and synthesis to change the speaker’s accent in near real time.

The company has trained a machine learning algorithm to quickly and locally (that is, without using the cloud) recognize a person’s speech on one end and, on the other, output the same words with an accent chosen from a list or automatically detected from the other person’s speech.

Screenshot of the Sanas desktop application.

Image Credits: Sanas.ai

It slots right into the OS’s sound stack so it works out of the box with pretty much any audio or video calling tool. Right now the company is operating a pilot program with thousands of people in locations from the USA and UK to the Philippines, India, Latin America, and others. Accents supported will include American, Spanish, British, Indian, Filipino and Australian by the end of the year.

To tell the truth, the idea of Sanas kind of bothered me at first. It felt like a concession to bigoted people who consider their accent superior and think others below them. Tech will fix it… by accommodating the bigots. Great!

But while I still have a little bit of that feeling, I can see there’s more to it than this. Fundamentally speaking, it is easier to understand someone when they speak in an accent similar to your own. But customer service and tech support is a huge industry and one primarily performed by people outside the countries where the customers are. This basic disconnect can be remedied in a way that puts the onus of responsibility on the entry-level worker, or one that puts it on technology. Either way the difficulty of making oneself understood remains and must be addressed — an automated system just lets it be done more easily and allows more people to do their job.

It’s not magic — as you can tell in this clip, the character and cadence of the person’s voice is only partly retained and the result is considerably more artificial sounding:

But the technology is improving and like any speech engine, the more it’s used, the better it gets. And for someone not used to the original speaker’s accent, the American-accented version may very well be more easily understood. For the person in the support role, this likely means better outcomes for their calls — everyone wins. Sanas told me that the pilots are just starting so there are no numbers available from this deployment yet, but testing has suggested a considerable reduction of error rates and increase in call efficiency.

It’s good enough at any rate to attract a $5.5M seed round, with participation from Human Capital, General Catalyst, Quiet Capital, and DN Capital.

“Sanas is striving to make communication easy and free from friction, so people can speak confidently and understand each other, wherever they are and whoever they are trying to communicate with,” CEO Maxim Serebryakov said in the press release announcing the funding. It’s hard to disagree with that mission.

While the cultural and ethical questions of accents and power differentials are unlikely to ever go away, Sanas is trying something new that may be a powerful tool for the many people who must communicate professionally and find their speech patterns are an obstacle to that. It’s an approach worth exploring and discussing even if in a perfect world we would simply understand one another better.

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UK-based Heroes raises $200M to buy up more Amazon merchants for its roll-up play

Heroes, one of the new wave of startups aiming to build big e-commerce businesses by buying up smaller third-party merchants on Amazon’s Marketplace, has raised another big round of funding to double down on that strategy. The London startup has picked up $200 million, money that it will mainly be using to snap up more merchants. Existing brands in its portfolio cover categories like babies, pets, sports, personal health and home and garden categories — some of them, like PremiumCare dog chews, the Onco baby car mirror, gardening tool brand Davaon and wooden foot massager roller Theraflow, category best-sellers — and the plan is to continue building up all of these verticals.

Crayhill Capital Management, a fund based out of New York, is providing the funding, and Riccardo Bruni — who co-founded the company with twin brother Alessio and third brother Giancarlo — said that the bulk of it will be going toward making acquisitions, and is therefore coming in the form of debt.

Raising debt rather than equity at this point is pretty standard for companies like Heroes. Heroes itself is pretty young: it launched less than a year ago, in November 2020, with $65 million in funding, a round comprised of both equity and debt. Other investors in the startup include 360 Capital, Fuel Ventures and Upper 90.

Heroes is playing in what is rapidly becoming a very crowded field. Not only are there tens of thousands of businesses leveraging Amazon’s extensive fulfillment network to sell goods on the e-commerce giant’s marketplace, but some days it seems we are also rapidly approaching a state of nearly as many startups launching to consolidate these third-party sellers.

Many a roll-up play follows a similar playbook, which goes like this: Amazon provides the marketplace to sell goods to consumers, and the infrastructure to fulfill those orders, by way of Fulfillment By Amazon and its Prime service. Meanwhile, the roll-up business — in this case Heroes — buys up a number of the stronger companies leveraging FBA and the marketplace. Then, by consolidating them into a single tech platform that they have built, Heroes creates better economies of scale around better and more efficient supply chains, sharper machine learning and marketing and data analytics technology, and new growth strategies. 

What is notable about Heroes, though — apart from the fact that it’s the first roll-up player to come out of the U.K., and continues to be one of the bigger players in Europe — is that it doesn’t believe that the technology plays as important a role as having a solid relationship with the companies it’s targeting, key given that now the top marketplace sellers are likely being feted by a number of companies as acquisition targets.

“The tech is very important,” said Alessio in an interview. “It helps us build robust processes that tie all the systems together across multiple brands and marketplaces. But what we have is very different from a SaaS business. We are not building an app, and tech is not the core of what we do. From the acquisitions side, we believe that human interactions ultimately win. We don’t think tech can replace a strong acquisition process.”

Image Credits: Heroes

Heroes’ three founder-brothers (two of them, Riccardo and Alessio, pictured above) have worked across a number of investment, finance and operational roles (the CVs include Merrill Lynch, EQT Ventures, Perella Weinberg Partners, Lazada, Nomura and Liberty Global) and they say there have been strong signs so far of its strategy working: of the brands that it has acquired since launching in November, they claim business (sales) has grown five-fold.

Collectively, the roll-up startups are raising hundreds of millions of dollars to fuel these efforts. Other recent hopefuls that have announced funding this year include Suma Brands ($150 million); Elevate Brands ($250 million); Perch ($775 million); factory14 ($200 million); Thrasio (currently probably the biggest of them all in terms of reach and money raised and ambitions), HeydayThe Razor GroupBrandedSellerXBerlin Brands Group (X2), Benitago, Latin America’s Valoreo and Rainforest and Una Brands out of Asia. 

The picture that is emerging across many of these operations is that many of these companies, Heroes included, do not try to make their particular approaches particularly more distinctive than those of their competitors, simply because — with nearly 10 million third-party sellers today on Amazon globally — the opportunity is likely big enough for all of them, and more, not least because of current market dynamics.

“It’s no secret that we were inspired by Thrasio and others,” Riccardo said. “Combined with COVID-19, there has been a massive acceleration of e-commerce across the continent.” It was that, plus the realization that the three brothers had the right e-commerce, fundraising and investment skills between them, that made them see what was a ‘perfect storm’ to tackle the opportunity, he continued. “So that is why we jumped into it.”

In the case of Heroes, while the majority of the funding will be used for acquisitions, it’s also planning to double headcount from its current 70 employees before the end of this year with a focus on operational experts to help run their acquired businesses. 

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Workera.ai, a precision upskilling platform, taps $16M to close enterprise skills gap

Finding the right learning platform can be difficult, especially as companies look to upskill and reskill their talent to meet demand for certain technological capabilities, like data science, machine learning and artificial intelligence roles.

Workera.ai’s approach is to personalize learning plans with targeted resources — both technical and nontechnical roles — based on the current level of a person’s proficiency, thereby closing the skills gap.

The Palo Alto-based company secured $16 million in Series A funding, led by New Enterprise Associates, and including existing investors Owl Ventures and AI Fund, as well as individual investors in the AI field like Richard Socher, Pieter Abbeel, Lake Dai and Mehran Sahami.

Kian Katanforoosh, Workera’s co-founder and CEO, says not every team is structured or feels supported in their learning journey, so the company comes at the solution from several angles with an assessment on technical skills, where the employee wants to go in their career and what skills they need for that, and then Workera will connect those dots from where the employee is in their skillset to where they want to go. Its library has more than 3,000 micro-skills and personalized learning plans.

“It is what we call precision upskilling,” he told TechCrunch. “The skills data then can go to the organization to determine who are the people that can work together best and have a complementary skill set.”

Workera was founded in 2020 by Katanforoosh and James Lee, COO, after working with Andrew Ng, Coursera co-founder and Workera’s chairman. When Lee first connected with Katanforoosh, he knew the company would be able to solve the problem around content and basic fundamentals of upskilling.

It raised a $5 million seed round last October to give the company a total of $21 million raised to date. This latest round was driven by the company’s go-to-market strategy and customer traction after having acquired over 30 customers in 12 countries.

Over the past few quarters, the company began working with Fortune 500 companies, including Siemens Energy, across industries like professional services, medical devices and energy, Lee said. As spending on AI skills is expected to exceed $79 billion by 2022, he says Workera will assist in closing the gap.

“We are seeing a need to measure skills,” he added. “The size of the engagements are a sign as is the interest for tech and non-tech teams to develop AI literacy, which is a more pressing need.”

As a result, it was time to increase the engineering and science teams, Katanforoosh said. He plans to use the new funding to invest in more talent in those areas and to build out new products. In addition, there are a lot of natural language processes going on behind the scenes, and he wants the company to better understand it at a granular level so that the company can assess people more precisely.

Carmen Chang, general partner and head of Asia at NEA, said she is a limited partner in Ng’s AI fund and in Coursera, and has looked at a lot of his companies.

She said she is “very excited” to lead the round and about Workera’s concept. The company has a good understanding of the employee skill set, and with the tailored learning program, will be able to grow with company needs, Chang added.

“You can go out and hire anyone, but investing in the people that you have, educating and training them, will give you a look at the totality of your employees,” Chang said. “Workera is able to go in and test with AI and machine learning and map out the skill sets within a company so they will be able to know what they have, and that is valuable, especially in this environment.”

 

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Bodo.ai secures $14M, aims to make Python better at handling large-scale data

Bodo.ai, a parallel compute platform for data workloads, is developing a compiler to make Python portable and efficient across multiple hardware platforms. It announced Wednesday a $14 million Series A funding round led by Dell Technologies Capital.

Python is one of the top programming languages used among artificial intelligence and machine learning developers and data scientists, but as Behzad Nasre, co-founder and CEO of Bodo.ai, points out, it is challenging to use when handling large-scale data.

Bodo.ai, headquartered in San Francisco, was founded in 2019 by Nasre and Ehsan Totoni, CTO, to make Python higher performing and production ready. Nasre, who had a long career at Intel before starting Bodo, met Totoni and learned about the project that he was working on to democratize machine learning and enable parallel learning for everyone. Parallelization is the only way to extend Moore’s Law, Nasre told TechCrunch.

Bodo does this via a compiler technology that automates the parallelization so that data and ML developers don’t have to use new libraries, APIs or rewrite Python into other programming languages or graphics processing unit code to achieve scalability. Its technology is being used to make data analytics tools in real time and is being used across industries like financial, telecommunications, retail and manufacturing.

“For the AI revolution to happen, developers have to be able to write code in simple Python, and that high-performance capability will open new doors,” Totoni said. “Right now, they rely on specialists to rewrite them, and that is not efficient.”

Joining Dell in the round were Uncorrelated Ventures, Fusion Fund and Candou Ventures. Including the new funding, Bodo has raised $14 million in total. The company went after Series A dollars after its product had matured and there was good traction with customers, prompting Bodo to want to scale quicker, Nasre said.

Nasre feels Dell Technologies Capital was “uniquely positioned to help us in terms of reserves and the role they play in the enterprise at large, which is to have the most effective salesforce in enterprise.”

Though he was already familiar with Nasre, Daniel Docter, managing director at Dell Technologies, heard about Bodo from a data scientist friend who told Docter that Bodo’s preliminary results “were amazing.”

Much of Dell’s investments are in the early-stage and in deep tech founders that understand the problem. Docter puts Totoni and Nasre in that category.

“Ehsan fits this perfectly, he has super deep technology knowledge and went out specifically to solve the problem,” he added. “Behzad, being from Intel, saw and lived with the problem, especially seeing Hadoop fail and Spark take its place.”

Meanwhile, with the new funding, Nasre intends to triple the size of the team and invest in R&D to build and scale the company. It will also be developing a marketing and sales team.

The company is now shifting from financing to customer- and revenue-focused as it aims to drive up adoption by the Python community.

“Our technology can translate simple code into the fast code that the experts will try,” Totoni said. “I joined Intel Labs to work on the problem, and we think we have the first solution that will democratize machine learning for developers and data scientists. Now, they have to hand over Python code to specialists who rewrite it for tools. Bodo is a new type of compiler technology that democratizes AI.”

 

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Kapacity.io is using AI to drive energy and emissions savings for real estate

Y Combinator-backed Kapacity.io is on a mission to accelerate the decarbonization of buildings by using AI-generated efficiency savings to encourage electrification of commercial real estate — wooing buildings away from reliance on fossil fuels to power their heating and cooling needs.

It does this by providing incentives to building owners/occupiers to shift to clean energy usage through a machine learning-powered software automation layer.

The startup’s cloud software integrates with buildings’ HVAC systems and electricity meters — drawing on local energy consumption data to calculate and deploy real-time adjustments to heating/cooling systems which not only yield energy and (CO2) emissions savings but generate actual revenue for building owners/tenants — paying them to reduce consumption such as at times of peak energy demand on the grid.

“We are controlling electricity consumption in buildings, focusing on heating and cooling devices — using AI machine learning to optimize and find the best ways to consume electricity,” explains CEO and co-founder Jaakko Rauhala, a former consultant in energy technology. “The actual method is known as ‘demand response’. Basically that is a way for electricity consumers to get paid for adjusting their energy consumption, based on a utility company’s demand.

“For example if there is a lot of wind power production and suddenly the wind drops or the weather changes and the utility company is running power grids they need to balance that reduction — and the way to do that is either you can fire up natural gas turbines or you can reduce power consumption… Our product estimates how much can we reduce electricity consumption at any given minute. We are [targeting] heating and cooling devices because they consume a lot of electricity.”

“The way we see this is this is a way we can help our customers electrify their building stocks faster because it makes their investments more lucrative and in addition we can then help them use more renewable electricity because we can shift the use from fossil fuels to other areas. And in that we hope to help push for a more greener power grid,” he adds.

Kapcity’s approach is applicable in deregulated energy markets where third parties are able to play a role offering energy saving services and fluctuations in energy demand are managed by an auction process involving the trading of surplus energy — typically overseen by a transmission system operator — to ensure energy producers have the right power balance to meet customer needs.

Demand for energy can fluctuate regardless of the type of energy production feeding the grid but renewable energy sources tend to increase the volatility of energy markets as production can be less predictable versus legacy energy generation (like nuclear or burning fossil fuels) — wind power, for example, depends on when and how strongly the wind is blowing (which both varies and isn’t perfectly predictable). So as economies around the world dial up efforts to tackle climate change and hit critical carbon emissions reduction targets there’s growing pressure to shift away from fossil fuel-based power generation toward cleaner, renewable alternatives. And the real estate sector specifically remains a major generator of CO2, so is squarely in the frame for “greening”.

Simultaneously, decarbonization and the green shift looks likely to drive demand for smart solutions to help energy grids manage increasing complexity and volatility in the energy supply mix.

“Basically more wind power — and solar, to some extent — correlates with demand for balancing power grids and this is why there is a lot of talk usually about electricity storage when it comes to renewables,” says Rauhala. “Demand response, in the way that we do it, is an alternative for electricity storage units. Basically we’re saying that we already have a lot of electricity consuming devices — and we will have more and more with electrification. We need to adjust their consumption before we invest billions of dollars into other systems.”

“We will need a lot of electricity storage units — but we try to push the overall system efficiency to the maximum by utilising what we already have in the grid,” he adds.

There are of course limits to how much “adjustment” (read: switching off) can be done to a heating or cooling system by even the cleverest AI without building occupants becoming uncomfortable.

But Kapacity’s premise is that small adjustments — say turning off the boilers/coolers for five, 15 or 30 minutes — can go essentially unnoticed by building occupants if done right, allowing the startup to tout a range of efficiency services for its customers; such as a peak-shaving offering, which automatically reduces energy usage to avoid peaks in consumption and generate significant energy cost savings.

“Our goal — which is a very ambitious goal — is that the customers and occupants in the buildings wouldn’t notice the adjustments. And that they would fall into the normal range of temperature fluctuations in a building,” says Rauhala.

Kapacity’s algorithms are designed to understand how to make dynamic adjustments to buildings’ heating/cooling without compromising “thermal comfort”, as Rauhala puts it — noting that co-founder (and COO) Sonja Salo, has both a PhD in demand response and researched thermal comfort during a stint as a visiting researcher at UC Berkley — making the area a specialist focus for the engineer-led founding team.

At the same time, the carrots it’s dangling at the commercial real estate to sign up for a little algorithmic HVAC tweaking look substantial: Kapacity says its system has been able to achieve a 25% reduction in electricity costs and a 10% reduction in CO2-emissions in early pilots. Although early tests have been limited to its home market for now.

Its other co-founder, Rami El Geneidy, researched smart algorithms for demand response involving heat pumps for his PhD dissertation — and heat pumps are another key focus for the team’s tech, per Rauhala.

Heat pumps are a low-carbon technology that’s fairly commonly used in the Nordics for heating buildings, but whose use is starting to spread as countries around the world look for greener alternatives to heat buildings.

In the U.K., for example, the government announced a plan last year to install hundreds of thousands of heat pumps per year by 2028 as it seeks to move the country away from widespread use of gas boilers to heat homes. And Rauhala names the U.K. as one of the startup’s early target markets — along with the European Union and the U.S., where they also envisage plenty of demand for their services.

While the initial focus is the commercial real estate sector, he says they are also interested in residential buildings — noting that from a “tech core point of view we can do any type of building”.

“We have been focusing on larger buildings — multifamily buildings, larger office buildings, certain types of industrial or commercial buildings so we don’t do single-family detached homes at the moment,” he goes on, adding: “We have been looking at that and it’s an interesting avenue but our current pilots are in larger buildings.”

The Finnish startup was only founded last year — taking in a pre-seed round of funding from Nordic Makers prior to getting backing from YC — where it will be presenting at the accelerator’s demo day next week. (But Rauhala won’t comment on any additional fund raising plans at this stage.)

He says it’s spun up five pilot projects over the last seven months involving commercial landlords, utilities, real estate developers and engineering companies (all in Finland for now), although — again — full customer details are not yet being disclosed. But Rauhala tells us they expect to move to their first full commercial deals with pilot customers this year.

“The reason why our customers are interested in using our products is that this is a way to make electrification cheaper because they are being paid for adjusting their consumption and that makes their operating cost lower and it makes investments more lucrative if — for example — you need to switch from natural gas boilers to heat pumps so that you can decarbonize your building,” he also tells us. “If you connect the new heat pump running on electricity — if you connect that to our service we can reduce the operating cost and that will make it more lucrative for everybody to electrify their buildings and run their systems.

“We can also then make their electricity consumed more sustainable because we are shifting consumption away from hours with most CO2 emissions on the grid. So we try to avoid the hours when there’s a lot of fossil fuel-based production in the grid and try to divert that into times when we have more renewable electricity.

“So basically the big question we are asking is how do we increase the use of renewables and the way to achieve that is asking when should we consume? Well we should consume electricity when we have more renewable in the grid. And that is the emission reduction method that we are applying here.”

In terms of limitations, Kapacity’s software-focused approach can’t work in every type of building — requiring that real estate customers have some ability to gather energy consumption (and potentially temperature) data from their buildings remotely, such as via IoT devices.

“The typical data that we need is basic information on the heating system — is it running at 100% or 50% or what’s the situation? That gets us pretty far,” says Rauhala. “Then we would like to know indoor temperatures. But that is not mandatory in the sense that we can still do some basic adjustments without that.”

It also of course can’t offer much in the way of savings to buildings that are running 100% on natural gas (or oil) — i.e. with electricity only used for lighting (turning lights off when people are inside buildings obviously wouldn’t fly); there must be some kind of air conditioning, cooling or heat pump systems already installed (or the use of electric hot water boilers).

“An old building that runs on oil or natural gas — that’s a target for decarbonization,” he continues. “That’s a target where you could consider installing heat pumps and that is where we could help some of our customers or potential customers to say OK we need to estimate how much would it cost to install a heat pump system here and that’s where our product can come in and we can say you can reduce the operating cost with demand response. So maybe we should do something together here.”

Rauhala also confirms that Kapacity’s approach does not require invasive levels of building occupant surveillance, telling TechCrunch: “We don’t collect information that is under GDPR [General Data Protection Regulation], I’ll put it that way. We don’t take personal data for this demand response.”

So any guestimates its algorithms are making about building occupants’ tolerance for temperature changes are, therefore, not going to be based on specific individuals — but may, presumably, factor in aggregated information related to specific industry/commercial profiles.

The Helsinki-based startup is not the only one looking at applying AI to drive energy cost and emissions savings in the commercial buildings sector — another we spoke to recently is Düsseldorf-based Dabbel, for example. And plenty more are likely to take an interest in the space as governments start to pump more money into accelerating decarbonization.

Asked about competitive differentiation, Rauhala points to a focus on real-time adjustments and heat pump technologies.

“One of our key things is we’re developing a system so that we can do close to real-time control — very, very short-term control. That is a valuable service to the power grid so we can then quickly adjust,” he says. “And the other one is we are focusing on heat pump technologies to get started — heat pumps here in the Nordics are a very common and extremely good way to decarbonize and understanding how we can combine these to demand response with new heat pumps that is where we see a lot of advantages to our approach.”

“Heat pumps are a bit more technically complex than your basic natural gas boiler so there are certain things that have to be taken it account and that is where we have been focusing our efforts,” he goes on, adding: “We see heat pumps as an excellent way to decarbonize the global building stock and we want to be there and help make that happen.”

Per capita, the Nordics has the most heat pump installations, according to Rauhala — including a lot of ground source heat pump installations which can replace fossil fuel consumption entirely.

“You can run your building with a ground source heat pump system entirely — you don’t need any supporting systems for it. And that is the area where we here in Europe are more far ahead than in the U.S.,” he says on that.

“The U.K. government is pushing for a lot of heat pump installations and there are incentives in place for people to replace their existing natural gas systems or whatever they have. So that is very interesting from our point of view. The U.K. also has a lot of wind power coming online and there have been days when the U.K. has been running 100% with renewable electricity which is great. So that actually is a really good thing for us. But then in the longer term in the U.S. — Seattle, for example, has banned the use of fossil fuels in new buildings so I’m very confident that the market in the U.S. will open up more and quickly. There’s a lot of opportunities in that space as well.

“And of course from a cooling perspective air conditioning in general in the U.S. is very widespread — especially in commercial buildings so that is already an existing opportunity for us.”

“My estimate on how valuable electricity use for heating and cooling is it’s tens of billions of dollars annually in the U.S. and EU,” he adds. “There’s a lot of electricity being used already for this and we expect the market to grow significantly.”

On the business model front, the startup’s cloud software looks set to follow a SaaS model but the plan is also to take a commission of the savings and/or generated income from customers. “We also have the option to provide the service with a fixed fee, which might be easier for some customers, but we expect the majority to be under a commission,” adds Rauhala.

Looking ahead, were the sought-for global shift away from fossil fuels to be wildly successful — and all commercial buildings’ gas/oil boilers got replaced with 100% renewable power systems in short order — there would still be a role for Kapacity’s control software to play, generating energy cost savings for its customers, even though our (current) parallel pressing need to shrink carbon emissions would evaporate in this theoretical future.

“We’d be very happy,” says Rauhala. “The way we see emission reductions with demand response now is it’s based on the fact that we do still have fossil fuels power system — so if we were to have a 100% renewable power system then the electricity does nothing to reduce emissions from the electricity consumption because it’s all renewable. So, ironically, in the future we see this as a way to push for a renewable energy system and makes that transition happen even faster. But if we have a 100% renewable system then there’s nothing [in terms of CO2 emissions] we can reduce but that is a great goal to achieve.”

 

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Shelf.io closes huge $52.5M Series B after posting 4x ARR growth in the last year

Covering public companies can be a bit of a drag. They grow some modest amount each year, and their constituent analysts pester them with questions about gross margin expansion and sales rep efficiency. It can be a little dull. Then there are startups, which grow much more quickly — and are more fun to talk about.

That’s the case with Shelf.io. The company announced an impressive set of metrics this morning, including that from July 2020 to July 2021, it grew its annual recurring revenue (ARR) 4x. Shelf also disclosed that it secured a $52.5 million Series B led by Tiger Global and Insight Partners.

That’s quick growth for a post-Series A startup. Crunchbase reckons that the company raised $8.2 million before its Series B, while PitchBook pegs the number at $6.5 million. Regardless, the company was efficiently expanding from a limited capital base before its latest fundraising event.

What does the company’s software do? Shelf plugs into a company’s information systems, learns from the data and then helps employees respond to queries without forcing them to execute searches or otherwise hunt for information.

The company is starting with customer service as its target vertical. According to Shelf CEO Sedarius Perrotta, Shelf can absorb information from, say, Salesforce, SharePoint, legacy knowledge management platforms and Zendesk. Then, after training models and staff, the company’s software can begin to provide support staff with answers to customer questions as they talk to customers in real time.

The company’s tech can also power responses to customer queries not aimed at a human agent and provide a searchable database of company knowledge to help workers more quickly solve customer issues.

Per Perrotta, Shelf is targeting the sales market next, with others to follow. How might Shelf fit into sales? According to the company, its software may be able to offer staff already written proposals for similar-seeming deals and other related content. The gist is that at companies that have lots of workers doing similar tasks — clicking around in Salesforce, or answering support queries, say — Shelf can learn from the activity and get smarter in helping employees with their tasks. I presume that the software’s learning ability will improve over time, as well.

Shelf, around 100 people today, hopes to double in size by the end of the year, and then double again next year.

That’s where the new capital comes in. Hiring folks in the worlds of machine learning and data science is very expensive. And because the company wants to scale those hires quickly, it will need a large bank balance to lean on.

Quick ARR growth was not the only reason Shelf was able to secure such an outsized Series B, at least when compared to how much capital it had raised before. Per Perrotta, Shelf has 130% net dollar retention and no churn to report, meaning its customers are both sticky and expand organically.

While Shelf is interesting today and has certainly found niches it can sell into in its current form, I am more curious about how far the company can take its machine learning system, called MerlinAI. If its tech can get sufficiently smart, its ability to prompt and help employees could reduce onboarding time and the overall cost of employee training. That would be a huge market.

This is the sort of deal that we expect to see Tiger in — an outsized investment (compared to prior rounds) into a high-growth company that has lots of market room. Whatever price Tiger just paid for the company’s stock, a few years of continued growth should de-risk the investment. By our read, Tiger is really just the market-leading bull on software market growth in the long term. Shelf fits into that thesis neatly.

 

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Companies betting on data must value people as much as AI

The Pareto principle, also known as the 80-20 rule, asserts that 80% of consequences come from 20% of causes, rendering the remainder way less impactful.

Those working with data may have heard a different rendition of the 80-20 rule: A data scientist spends 80% of their time at work cleaning up messy data as opposed to doing actual analysis or generating insights. Imagine a 30-minute drive expanded to two-and-a-half hours by traffic jams, and you’ll get the picture.

As tempting as it may be to think of a future where there is a machine learning model for every business process, we do not need to tread that far right now.

While most data scientists spend more than 20% of their time at work on actual analysis, they still have to waste countless hours turning a trove of messy data into a tidy dataset ready for analysis. This process can include removing duplicate data, making sure all entries are formatted correctly and doing other preparatory work.

On average, this workflow stage takes up about 45% of the total time, a recent Anaconda survey found. An earlier poll by CrowdFlower put the estimate at 60%, and many other surveys cite figures in this range.

None of this is to say data preparation is not important. “Garbage in, garbage out” is a well-known rule in computer science circles, and it applies to data science, too. In the best-case scenario, the script will just return an error, warning that it cannot calculate the average spending per client, because the entry for customer #1527 is formatted as text, not as a numeral. In the worst case, the company will act on insights that have little to do with reality.

The real question to ask here is whether re-formatting the data for customer #1527 is really the best way to use the time of a well-paid expert. The average data scientist is paid between $95,000 and $120,000 per year, according to various estimates. Having the employee on such pay focus on mind-numbing, non-expert tasks is a waste both of their time and the company’s money. Besides, real-world data has a lifespan, and if a dataset for a time-sensitive project takes too long to collect and process, it can be outdated before any analysis is done.

What’s more, companies’ quests for data often include wasting the time of non-data-focused personnel, with employees asked to help fetch or produce data instead of working on their regular responsibilities. More than half of the data being collected by companies is often not used at all, suggesting that the time of everyone involved in the collection has been wasted to produce nothing but operational delay and the associated losses.

The data that has been collected, on the other hand, is often only used by a designated data science team that is too overworked to go through everything that is available.

All for data, and data for all

The issues outlined here all play into the fact that save for the data pioneers like Google and Facebook, companies are still wrapping their heads around how to re-imagine themselves for the data-driven era. Data is pulled into huge databases and data scientists are left with a lot of cleaning to do, while others, whose time was wasted on helping fetch the data, do not benefit from it too often.

The truth is, we are still early when it comes to data transformation. The success of tech giants that put data at the core of their business models set off a spark that is only starting to take off. And even though the results are mixed for now, this is a sign that companies have yet to master thinking with data.

Data holds much value, and businesses are very much aware of it, as showcased by the appetite for AI experts in non-tech companies. Companies just have to do it right, and one of the key tasks in this respect is to start focusing on people as much as we do on AIs.

Data can enhance the operations of virtually any component within the organizational structure of any business. As tempting as it may be to think of a future where there is a machine learning model for every business process, we do not need to tread that far right now. The goal for any company looking to tap data today comes down to getting it from point A to point B. Point A is the part in the workflow where data is being collected, and point B is the person who needs this data for decision-making.

Importantly, point B does not have to be a data scientist. It could be a manager trying to figure out the optimal workflow design, an engineer looking for flaws in a manufacturing process or a UI designer doing A/B testing on a specific feature. All of these people must have the data they need at hand all the time, ready to be processed for insights.

People can thrive with data just as well as models, especially if the company invests in them and makes sure to equip them with basic analysis skills. In this approach, accessibility must be the name of the game.

Skeptics may claim that big data is nothing but an overused corporate buzzword, but advanced analytics capacities can enhance the bottom line for any company as long as it comes with a clear plan and appropriate expectations. The first step is to focus on making data accessible and easy to use and not on hauling in as much data as possible.

In other words, an all-around data culture is just as important for an enterprise as the data infrastructure.

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UiPath CEO Daniel Dines is coming to TC Sessions: SaaS to talk RPA and automation

UiPath came seemingly out of nowhere in the last several years, going public last year in a successful IPO during which it raised more than $527 million. It raised $2 billion in private money prior to that with its final private valuation coming in at an amazing $35 billion. UiPath CEO Daniel Dines will be joining us on a panel to discuss automation at TC Sessions: SaaS on October 27th.

The company has been able to capture all this investor attention doing something called robotic process automation (RPA), which provides a way to automate a series of highly mundane tasks. It has become quite popular, especially to help bring a level of automation to legacy systems that might not be able to handle more modern approaches to automation involving artificial intelligence and machine learning. In 2019 Gartner found that RPA was the fastest growing category in enterprise software.

In point of fact, UiPath didn’t actually come out of nowhere. It was founded in 2005 as a consulting company and transitioned to software over the years. The company took its first VC funding, a modest $1.5 million seed round, in 2015, according to Crunchbase data.

As RPA found its market, the startup began to take off, raising gobs of money, including a $568 million round in April 2019 and $750 million in its final private raise in February 2021.

Dines will be appearing on a panel discussing the role of automation in the enterprise. Certainly, the pandemic drove home the need for increased automation as masses of office workers moved to work from home, a trend that is likely to continue even after the pandemic slows.

As the RPA market leader, he is uniquely positioned to discuss how this software and other similar types will evolve in the coming years and how it could combine with related trends like no-code and process mapping. Dines will be joined on the panel by investor Laela Sturdy from CapitalG and ServiceNow’s Dave Wright, where they will discuss the state of the automation market, why it’s so hot and where the next opportunities could be.

In addition to our discussion with Dines, the conference will also include Databricks’ Ali Ghodsi, Salesforce’s Kathy Baxter and Puppet’s Abby Kearns, as well as investors Casey Aylward and Sarah Guo, among others. We hope you’ll join us. It’s going to be a stimulating day.

Buy your pass now to save up to $100. We can’t wait to see you in October!

Is your company interested in sponsoring or exhibiting at TC Sessions: SaaS 2021? Contact our sponsorship sales team by filling out this form.

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Employee talent predictor retrain.ai raised another $7M, adds Splunk as strategic investor

Automation will displace 85 million jobs while simultaneously creating 97 million new jobs by 2025, according to the World Economic Forum. Although that sounds like good news, the hard reality is that millions of people will have to retrain in the jobs of the future.

A number of startups are addressing these problems of employee skills, and are looking at talent development, neuroscience-based assessments and prediction technologies for staffing. These include Pymetrics (raised $56.6 million), Eightfold (raised $396.8 million) and EmPath (raised $1 million). But this sector is by no means done yet.

Retrain.ai bills itself as a “Talent Intelligence Platform”, and it’s now closed an additional $7 million from its current investors Square Peg, Hetz Ventures, TechAviv, .406 Ventures and Schusterman Family Investments. It’s also now added Splunk Ventures as a strategic investor. The new round of funding takes its total raised to $20 million.

Retrain.ai says it uses AI and machine learning to help governments and organizations retrain and upskill talent for jobs of the future, enable diversity initiatives, and help employees and jobseekers manage their careers.

Dr. Shay David, co-founder and CEO of retrain.ai said: “We are thrilled to have Splunk Ventures join us on this exciting journey as we use the power of data to solve the widening skills gap in the global labor markets.”

The company says it helps companies tackle future workforce strategies by “analyzing millions of data sources to understand the demand and supply of skill sets.”

The new funding will be used for U.S. expansion, hiring talent and product development.

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ThirdAI raises $6M to democratize AI to any hardware

Houston-based ThirdAI, a company building tools to speed up deep learning technology without the need for specialized hardware like graphics processing units, brought in $6 million in seed funding.

Neotribe Ventures, Cervin Ventures and Firebolt Ventures co-led the investment, which will be used to hire additional employees and invest in computing resources, Anshumali Shrivastava, Third AI co-founder and CEO, told TechCrunch.

Shrivastava, who has a mathematics background, was always interested in artificial intelligence and machine learning, especially rethinking how AI could be developed in a more efficient manner. It was when he was at Rice University that he looked into how to make that work for deep learning. He started ThirdAI in April with some Rice graduate students.

ThirdAI’s technology is designed to be “a smarter approach to deep learning,” using its algorithm and software innovations to make general-purpose central processing units (CPU) faster than graphics processing units for training large neural networks, Shrivastava said. Companies abandoned CPUs years ago in favor of graphics processing units that could more quickly render high-resolution images and video concurrently. The downside is that there is not much memory in graphics processing units, and users often hit a bottleneck while trying to develop AI, he added.

“When we looked at the landscape of deep learning, we saw that much of the technology was from the 1980s, and a majority of the market, some 80%, were using graphics processing units, but were investing in expensive hardware and expensive engineers and then waiting for the magic of AI to happen,” he said.

He and his team looked at how AI was likely to be developed in the future and wanted to create a cost-saving alternative to graphics processing units. Their algorithm, “sub-linear deep learning engine,” instead uses CPUs that don’t require specialized acceleration hardware.

Swaroop “Kittu” Kolluri, founder and managing partner at Neotribe, said this type of technology is still early. Current methods are laborious, expensive and slow, and for example, if a company is running language models that require more memory, it will run into problems, he added.

“That’s where ThirdAI comes in, where you can have your cake and eat it, too,” Kolluri said. “It is also why we wanted to invest. It is not just the computing, but the memory, and ThirdAI will enable anyone to do it, which is going to be a game changer. As technology around deep learning starts to get more sophisticated, there is no limit to what is possible.”

AI is already at a stage where it has the capability to solve some of the hardest problems, like those in healthcare and seismic processing, but he notes there is also a question about climate implications of running AI models.

“Training deep learning models can be more expensive than having five cars in a lifetime,” Shrivastava said. “As we move on to scale AI, we need to think about those.”

 

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