artificial intelligence
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Walnut raised $15 million in Series A funding, led by Eight Roads Ventures, to continue developing its sales experience platform.
Founders Yoav Vilner and Danni Friedland started the company in July 2020. Vilner told TechCrunch that while at a previous company, he was building a category called technology marketing in Israel. He realized that company sales people often ran into problems when it was time to demonstrate their product — the product would break, or they would have to ask another department to open something or add a feature, none of which happened instantaneously, Vilner added.
He and Friedland’s answer to that problem is a no-code platform for teams to create customized product demonstrations quickly, be able to integrate them into their sales and marketing processes and then generate insights from the demos.
Walnut engagement example. Image Credits: Walnut
“We let the sales and marketing teams replicate the SaaS product in our cloud environment, which is disconnected from the back end,” Vilner explained. “They can create a storyline to fit their customer and the demonstration, and then following the demo, sales leaders can get insight on what was good or bad. It encourages the sharing of knowledge and what story worked best for which kind of company.”
The company’s latest round gives it $21 million raised to date, and follows a $6 million seed round that included NFX, A Capital, Liquid2 Ventures and Graph Ventures, Vilner said.
Walnut serves over 60 business-to-business clients, including Adobe, NetApp, Varonis and People AI. In addition to Tel Aviv, the company has offices in New York and London.
Vilner intends to use the new funding to grow the team across the U.S, Europe and Israel and continue developing its technology and platform, including tools to embed demos into a website for product-led growth. He also expects to double the team of 25 over the next year.
Eyal Rabinovich, an investor at Eight Roads Ventures, said his brother is a Walnut customer, and the company fits with one of the firm’s theses around broad vertically integrated brands in SaaS and deep technology.
Rabinovich was tracking the sales enablement space for a while and said many companies claim to provide something unique, but it is usually workflow and processes. In Walnut’s case, it is solving something at the core of sales.
“They make everything measurable, and the ‘holy grail’ is conversion, and even just 1% conversion could mean millions of dollars,” he added. “Every company we spoke to wanted to use this product. Customers were telling us they closed the sales cycle within two weeks.”
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As artificial intelligence continues to weave its way into more enterprise applications, a startup that has built a platform to help businesses, especially non-tech organizations, build more customized AI decision-making tools for themselves has picked up some significant growth funding. Peak AI, a startup out of Manchester, England, that has built a “decision intelligence” platform, has raised $75 million, money that it will be using to continue building out its platform, expand into new markets and hire some 200 new people in the coming quarters.
The Series C is bringing a very big name investor on board. It is being led by SoftBank Vision Fund 2, with previous backers Oxx, MMC Ventures, Praetura Ventures and Arete also participating. That group participated in Peak’s Series B of $21 million, which only closed in February of this year. The company has now raised $119 million; it is not disclosing its valuation.
(This latest funding round was rumored last week, although it was not confirmed at the time and the total amount was not accurate.)
Richard Potter, Peak’s CEO, said the rapid follow-on in funding was based on inbound interest, in part because of how the company has been doing.
Peak’s so-called Decision Intelligence platform is used by retailers, brands, manufacturers and others to help monitor stock levels and build personalized customer experiences, as well as other processes that can stand to have some degree of automation to work more efficiently, but also require sophistication to be able to measure different factors against each other to provide more intelligent insights. Its current customer list includes the likes of Nike, Pepsico, KFC, Molson Coors, Marshalls, Asos and Speedy, and in the last 12 months revenues have more than doubled.
The opportunity that Peak is addressing goes a little like this: AI has become a cornerstone of many of the most advanced IT applications and business processes of our time, but if you are an organization — and specifically one not built around technology — your access to AI and how you might use it will come by way of applications built by others, not necessarily tailored to you, and the costs of building more tailored solutions can often be prohibitively high. Peak claims that those using its tools have seen revenues on average rise 5%, return on ad spend double, supply chain costs reduce by 5% and inventory holdings (a big cost for companies) reduce by 12%.
Peak’s platform, I should point out, is not exactly a “no-code” approach to solving that problem — not yet at least: It’s aimed at data scientists and engineers at those organizations so that they can easily identify different processes in their operations where they might benefit from AI tools, and to build those out with relatively little heavy lifting.
There have also been different market factors that have played a role. COVID-19, for example, and the boost that we have seen both in increasing “digital transformation” in businesses and making e-commerce processes more efficient to cater to rising consumer demand and more strained supply chains have all led to businesses being more open and keen to invest in more tools to improve their automation intelligently.
This, combined with Peak AI’s growing revenues, is part of what interested SoftBank. The investor has been long on AI for a while; but it also has been building out a section of its investment portfolio to provide strategic services to the kinds of businesses in which it invests.
Those include e-commerce and other consumer-facing businesses, which make up one of the main segments of Peak’s customer base.
Notably, one of its recent investments specifically in that space was made earlier this year, also in Manchester, when it took a $730 million stake (with potentially $1.6 billion more down the line) in The Hut Group, which builds software for and runs D2C businesses.
“In Peak we have a partner with a shared vision that the future enterprise will run on a centralized AI software platform capable of optimizing entire value chains,” Max Ohrstrand, senior investor for SoftBank Investment Advisers, said in a statement. “To realize this a new breed of platform is needed and we’re hugely impressed with what Richard and the excellent team have built at Peak. We’re delighted to be supporting them on their way to becoming the category-defining, global leader in Decision Intelligence.”
It’s not clear that SoftBank’s two Manchester interests will be working together, but it’s an interesting synergy if they do, and most of all highlights one of the firm’s areas of interest.
Longer term, it will be interesting to see how and if Peak evolves to extend its platform to a wider set of users at the organizations that are already its customers.
Potter said he believes that “those with technical predispositions” will be the most likely users of its products in the near and medium term. You might assume that would cut out, for example, marketing managers, although the general trend in a lot of software tools has precisely been to build versions of the same tools used by data scientists for these less technical people to engage in the process of building what it is that they want to use.
“I do think it’s important to democratize the ability to stream data pipelines, and to be able to optimize those to work in applications,” Potter added.
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Urbanbase, a Seoul-based company that develops a 3D spatial data platform for interior planning and design, announced today it has raised $11.1 million (13 billion won) in a Series B+ round as it scales up.
This round of funding was led by Hanwha Hotel & Resort, which is a subsidiary of South Korean conglomerate Hanwha Corporation.
Urbanbase, founded in 2013 by chief executive officer and a former architect Jinu Ha, has now raised $20 million (approximately 23 billion won) in total.
Existing investors did not join this round. The company had raised Series A funding of $1.8 million and an additional $1.2 million in 2017 and its first Series B round in April 2020, from backers that included South Korea-based Shinsegae Information & Communication, Woomi Construction, SL Investment, KDB Capital, Shinhan Capital, Enlight Ventures, CKD Venture Capital, and Breeze Investment, Ha said.
The latest funding will be used for enhancing its B2B SaaS, investing in R&D for advanced virtual reality (VR), augmented reality (AR) and 3D tools, which are considered core technologies of metaverse that is its new business Urbanbase plans to enter, according to Ha. Global metaverse market size is projected to increase $280 billion by 2025 from $30.7 billion in 2021, based on Strategy Analytics’ report.
Companies that focus on opportunities in the so-called “metaverse” have been growing as part of a next-generation approach to building viable business models in areas like virtual and augmented reality, and all the hardware and software and new tech that are being built for them. Big tech corporations, ranging from Facebook, Intel to Microsoft, are targeting to move in the area. Apple also waded into the area of virtual reality, working on developing a high-end VR headset.
Urbanbase also plans to upgrade its home interior software platform, Urbanbase Studio, that has functions to transform 2D indoor space images into 3D displays via Urbanbase’s patented algorithm, visualize interior products in augmented reality and analyze spatial images based on the AI technology.
Urbanbase claims 50,000 monthly active users with 70,000 registered B2C users. The company has about 50 B2B customers.
“Most of our B2B clients are large conglomerates in South Korea and Japan, for example, LG Electronics, Japan-based Mitsubishi Real Estate Service, Nitori Holdings, Dentsu Group and SoftBank, but we would like to extend our B2B clients base to small, midsized companies and bring more B2C users after closing the Series B+ funding,” Ha mentioned.
Urbanbase is seeking an acquisition target in prop-tech and construction technology sectors, Ha told TechCrunch. Urbanbase currently focuses on developing the interior tools for apartment buildings because about 70-80 percent of total households in South Korea and Japan live in apartments, Ha said, adding that it will diversify its portfolio by acquiring a startup that covers different types of residence.
It currently operates the platform in Korean and Japanese, but it will add English language service prior to entering in Singapore in the end of 2021, Ha said.
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I’m a native French data scientist who cut his teeth as a research engineer in computer vision in Japan and later in my home country. Yet I’m writing from an unlikely computer vision hub: Stuttgart, Germany.
But I’m not working on German car technology, as one would expect. Instead, I found an incredible opportunity mid-pandemic in one of the most unexpected places: An ecommerce-focused, AI-driven, image-editing startup in Stuttgart focused on automating the digital imaging process across all retail products.
My experience in Japan taught me the difficulty of moving to a foreign country for work. In Japan, having a point of entry with a professional network can often be necessary. However, Europe has an advantage here thanks to its many accessible cities. Cities like Paris, London, and Berlin often offer diverse job opportunities while being known as hubs for some specialties.
While there has been an uptick in fully remote jobs thanks to the pandemic, extending the scope of your job search will provide more opportunities that match your interest.
I’m working at the technology spin-off of a luxury retailer, applying my expertise to product images. Approaching it from a data scientist’s point of view, I immediately recognized the value of a novel application for a very large and established industry like retail.
Europe has some of the most storied retail brands in the world — especially for apparel and footwear. That rich experience provides an opportunity to work with billions of products and trillions of dollars in revenue that imaging technology can be applied to. The advantage of retail companies is a constant flow of images to process that provides a playing ground to generate revenue and possibly make an AI company profitable.
Another potential avenue to explore are independent divisions typically within an R&D department. I found a significant number of AI startups working on a segment that isn’t profitable, simply due to the cost of research and the resulting revenue from very niche clients.
I was particularly attracted to this startup because of the potential access to data. Data by itself is quite expensive and a number of companies end up working with a finite set. Look for companies that directly engage at the B2B or B2C level, especially retail or digital platforms that affect front-end user interface.
Leveraging such customer engagement data benefits everyone. You can apply it towards further research and development on other solutions within the category, and your company can then work with other verticals on solving their pain points.
It also means there’s massive potential for revenue gains the more cross-segments of an audience the brand affects. My advice is to look for companies with data already stored in a manageable system for easy access. Such a system will be beneficial for research and development.
The challenge is that many companies haven’t yet introduced such a system, or they don’t have someone with the skills to properly utilize it. If you finding a company isn’t willing to share deep insights during the courtship process or they haven’t implemented it, look at the opportunity to introduce such data-focused offerings.
I have a sweet spot for early-stage companies that give you the opportunity to create processes and core systems. The company I work for was still in its early days when I started, and it was working towards creating scalable technology for a specific industry. The questions that the team was tasked with solving were already being solved, but there were numerous processes that still had to be put into place to solve a myriad of other issues.
Our year-long efforts to automate bulk image editing taught me that as long as the AI you’re building learns to run independently across multiple variables simultaneously (multiple images and workflows), you’re developing a technology that does what established brands haven’t been able to do. In Europe, there are very few companies doing this and they are hungry for talent who can.
So don’t be afraid of a little culture shock and take the leap.
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If you haven’t noticed yet, the hiring market is a hot one — and getting more complicated as enterprise talent acquisition leaders face technology gaps while assessing candidates. This leads to difficulty in determining compensation.
Enter Compa. The offer management platform provides “deal desk” software for recruiters to more easily manage their compensation strategies to create and communicate offers that are easy to understand and are unbiased.
Charlie Franklin, co-founder and CEO of Compa, told TechCrunch it was frustrating to lose a candidate at the compensation stage, so the company created its software to reduce the challenge of relying on crowdsourcing data or surveys to compare pay.
“Recruiters often lack the data and tools to figure out how much to pay people and communicate that effectively,” Franklin told TechCrunch. “We see talent acquisitions teams like a sales team. If you think of it from that perspective, they need to close a candidate, but to ask the recruiter to operate off of a spreadsheet slows that process down.”
Compa co-founders, from left, Charlie Franklin, Joe Malandruccolo and Taylor Cone. Image Credits: Compa
With Compa, recruiters can input pay expectations and compare recent offers and collaborate with other team members and hiring managers to reach pay consensus quicker. The software automates all of the market intelligence in real time and provides insights about compensation across similar industries and organizations.
The company, based in both California and Massachusetts, emerged from stealth Thursday with $3.9 million in seed funding led by Base10 Partners. Participation in the round also came from Crosscut Ventures and Acadian Ventures, as well as a group of strategic angel investors including 2.12 Angels, Oyster HR CEO Tony Jamous and Scout RFP co-founders Stan Garber and Alex Yakubovich.
Jamison Hill, partner at Base10 Partners, said via email his firm was doing research in the ESG “megatrend,” particularly looking for startups focused on compensation management, when it came across Compa.
He was attracted to the founders’ “clarity and conviction” on the company’s vision, their understanding of the pay gap in the market, how Compa’s solution would “create a new wave of smarter, more-data driven recruiting teams” and how it was enabling employers to use compensation and a positive offer management approach to differentiate itself from competitors.
“They deeply understand the nuances that come with enterprise-level HR teams and bring that expertise to every aspect of Compa’s product offering, which is why we believe Compa can emerge as a leader in this trend and chose to partner with this very special team,” Hill added.
Franklin, who previously led human resources M&A at Workday, founded Compa last year with Joe Malandruccolo, who was on the engineering side at Facebook and Oculus, and Taylor Cone, who has done innovation consulting for organizations like Stanford University.
The company was bootstrapped prior to going after the seed round and will use the capital to expand the team and create additional products that fit into its mission of “making compensation fair and competitive for everyone,” Franklin said.
Going forward, he adds that job offers and compensation need to catch up to how quickly the world is changing. As more people work remotely and companies want to attract a diverse workforce, compensation will be an important factor.
“This is a long-term trend we are seeing in HR — compensation becoming more transparent — not just a spreadsheet shared internally, but a transition from secretive to open and accountable, Franklin said. “Technology is catching up to that, and we have the ability to produce outcomes that drive differences in pay.”
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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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Tuna is on a mission to “fine tune” the payments space in Latin America and has raised two seed rounds totaling $3 million, led by Canary and by Atlantico.
Alex Tabor, Paul Ascher and Juan Pascual met each other on the engineering team of Peixe Urbano, a company Tabor co-founded and he referred to as a “Groupon for Brazil.” While there, they came up with a way to use A/B testing to create a way of dealing with payments in different markets.
They eventually left Peixe Urbano and started Tuna in 2019 to make their own payment product that enables merchants to use A/B testing of credit card processors and anti-fraud providers to optimize their payments processing with one integration and a no-code interface.
Tabor explained that the e-commerce landscape in Latin America was consolidated, meaning few banks controlled more of the market. The address verification system merchants use to verify a purchaser is who they say they are, involves sending information to a bank that is returned to the merchant with a score of whether that match is legitimate.
“In the U.S., that score is used to determine if the purchaser is legit, but they didn’t implement that in Latin America,” he added. “Instead, merchants in LatAm have to tap into other organizations that have that data.”
That process involves manual analysis and constant adjusting due to fraud. Instead, Tuna’s A/B tests between processors and anti-fraud providers in real time and provides a guarantee that a decision to swap providers is based on objective data that considers all components of performance, like approval rates, and not just fees.
Over the past year, the company added 12 customers and saw its revenue increase 15%. It boasts a customer list that includes the large Brazilian fashion chain Riachuelo, and its platform integrates with others including VTEX, Magento and WooCommerce.
The share of e-commerce in overall retail is less than 10% in Latin America. Marcos Toledo, Canary’s managing partner, said via email that e-commerce in LatAm is currently at an inflexion point: not only has the global pandemic driven more online purchases, but also fintech innovation that has occurred in recent years.
In Brazil alone, e-commerce sales grew 73.88% in 2020, but Toledo said there was much room for improvement. What Tuna is building will help companies navigate the situation and make it easier for more customers to buy online.
Toledo met the Tuna team from his partner, Julio Vasconcellos, who was one of the co-founders of Peixe Urbano. When the firm heard that the other Tuna co-founders were starting a business that was applying some of the optimization methods they had created at Peixe Urbano, but for every company, they saw it as an opportunity to get involved.
“The vast tech expertise that Alex, Paul and Juan bring to a very technical business is something that we really admire, as well as their vision to create a solution that can impact companies throughout Latin America,” Toledo said. “The no-code solution that Tuna is building is exciting because it is scalable and can help companies not only get better margins, but also drive their developers to other efforts — and developers have been a very scarce workforce in the region.”
To meet demand for an e-commerce industry that surpassed $200 billion in 2020, Tuna plans to use the new funding to build out its team and grow outbound customer success and R&D, Tabor said.
Up next, he wants to be able to show traction in payments optimization and facilitators in Brazil before moving on to other countries. He has identified Mexico, Colombia and Argentina as potential new markets.
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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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Ken Babcock and his co-founders, Dan Giovacchini and Brian Shultz, were in the midst of Harvard Business School in March 2020 when they felt the call to start Tango, a Chrome extension that auto-captures workflow best practices so that teams can learn from their top performers.
“This window of opportunity was driven by the pandemic as we saw a lot of companies become distributed and go remote,” CEO Babcock told TechCrunch. “Team leaders were remotely onboarding people, for perhaps the first time, and accelerating ramp times. There was no longer the opportunity to tap on people’s shoulders in the office, so much of the training was left to people’s own devices.”
They dropped out of their program to start Los Angeles-based Tango, and today, announced a $5.7 million seed round for its workflow intelligence platform. Wing Venture Capital led the round and was joined by General Catalyst, Global Silicon Valley, Outsiders Fund and Red Sea Ventures. A group of angel investors also joined, including former Yelp executive Michael Stoppelman, former Uber head of data Jai Ranganathan, KeepTruckin CEO Shoaib Makani and Awesome People Ventures’ Julia Lipton.
Tango is designed to help employees, particularly in customer success and sales enablement, get back as much as 20% of their workweek spent searching for that one piece of information or tracking down the right colleague to assist with a task. Its technology creates tutorials by recording a users’ workflow — actions, links to pages, URLs and screenshots — and turns that into step-by-step documentation with a video.
Previously the co-founders bootstrapped the company, and decided to go after seed funding to expand the product and growth teams and invest in product development so that Tango could take a product-led growth strategy, Babcock said. The team now has 13 employees.
Since starting last year, Tango has secured 10 pilots to figure out the data and capabilities before it is set to launch publicly in September. Babcock said the company will always have a free version of the product, as well as premium and enterprise versions that will unlock additional capabilities.
“The big thing is around integrations and meeting people where the consumer content is,” Babcock added. “We are reducing that burden of creating documentation, and for companies that already have Wikis or other materials, learning how to inject ourselves into those systems.”
Zach DeWitt, partner at Wing Venture Capital, said he met the company three years ago through a mutual friend.
His firm invests in early-stage, business-to-business startups unlocking a novel data set. In Tango’s case, the company was creating a new data set for the enterprise and business, where users can analyze workflow.
With the average tech company using 150 SaaS apps, up from 20 a decade ago, there are permutations about which app to use, how to use them, what happens if the user gets stuck and what if none of the data is being captured, Dewitt said. Tango works in the background and captures workflow, which is the foundation to the business’ success.
“I was blown away by the approach,” he added. “You have to meet people where they get stuck and even anticipate where they get stuck so you can serve the Tango tutorial to get unstuck. It can also change the company’s culture when it rewards people to share knowledge. The whole idea is beneficial to multiple parties: to those who are getting stuck and to new hires. That is powerful.”
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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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