Aug
31
2021
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Peak raises $75M for a platform that helps non-tech companies build AI applications

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.

Aug
26
2021
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Job offer management platform Compa emerges from stealth with $3.9M

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.”

 

Aug
26
2021
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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.”

 

Aug
26
2021
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Tuna raises $3M to address complexity of e-commerce payments in Latin America

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.

 

Aug
25
2021
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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.”

 

Aug
24
2021
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Tango dances in with $5.7M, making employee onboarding easier

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.”

 

Aug
23
2021
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Virtual dressing room startup Revery.ai applying computer vision to the fashion industry

Figuring out size and cut of clothes through a website can suck the fun out of shopping online, but Revery.ai is developing a tool that leverages computer vision and artificial intelligence to create a better online dressing room experience.

Under the tutelage of University of Illinois Center for Computer Science advisrr David Forsyth, a team consisting of Ph.D. students Kedan Li, Jeffrey Zhang and Min Jin Chong, is creating what they consider to be the first tool using existing catalog images to process at a scale of over a million garments weekly, something previous versions of virtual dressing rooms had difficulty doing, Li told TechCrunch.

Revery.ai co-founders Jeffrey Zhang, Min Jin Chong and Kedan Li. Image Credits: Revery.ai

California-based Revery is part of Y Combinator’s summer 2021 cohort gearing up to complete the program later this month. YC has backed the company with $125,000. Li said the company already has a two-year runway, but wants to raise a $1.5 million seed round to help it grow faster and appear more mature to large retailers.

Before Revery, Li was working on another startup in the personalized email space, but was challenged in making it work due to free versions of already large legacy players. While looking around for areas where there would be less monopoly and more ability to monetize technology, he became interested in fashion. He worked with a different adviser to get a wardrobe collection going, but that idea fizzled out.

The team found its stride working with Forsyth and making several iterations on the technology in order to target business-to-business customers, who already had the images on their websites and the users, but wanted the computer vision aspect.

Unlike its competitors that use 3D modeling or take an image and manually clean it up to superimpose on a model, Revery is using deep learning and computer vision so that the clothing drapes better and users can also customize their clothing model to look more like them using skin tone, hair styles and poses. It is also fully automated, can work with millions of SKUs and be up and running with a customer in a matter of weeks.

Its virtual dressing room product is now live on many fashion e-commerce platforms, including Zalora-Global Fashion Group, one of the largest fashion companies in Southeast Asia, Li said.

Revery.ai landing page. Image Credits: Revery.ai

“It’s amazing how good of results we are getting,” he added. “Customers are reporting strong conversion rates, something like three to five times, which they had never seen before. We released an A/B test for Zalora and saw a 380% increase. We are super excited to move forward and deploy our technology on all of their platforms.”

This technology comes at a time when online shopping jumped last year as a result of the pandemic. Just in the U.S., the e-commerce fashion industry made up 29.5% of fashion retail sales in 2020, and the market’s value is expected to reach $100 billion this year.

Revery is already in talks with over 40 retailers that are “putting this on their roadmap to win in the online race,” Li said.

Over the next year, the company is focusing on getting more adoption and going live with more clients. To differentiate itself from competitors continuing to come online, Li wants to invest body type capabilities, something retailers are asking for. This type of technology is challenging, he said, due to there not being much in the way of diversified body shape models available.

He expects the company will have to collect proprietary data itself so that Revery can offer the ability for users to create their own avatar so that they can see how the clothes look.

“We might actually be seeing the beginning of the tide and have the right product to serve the need,” he added.

Aug
23
2021
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Y Combinator-backed Adra wants to turn all dentists into cavity-finding ‘super dentists’

Like other areas of healthcare, the dental industry is steadily embracing technology. But while much of it is in the orthodontic realm, other startups, like Adra, are bringing artificial intelligence into a dentist’s day-to-day workflow, particularly in finding cavities, of what will be a $435.08 billion global dental services market this year.

The Singapore-based company was founded in 2021, but was an idea that started last year. Co-founder Hamed Fesharaki has been a dentist for over a decade and owns two clinics in Singapore.

He said dentists learn to read X-rays in dental school, but it can take a few years to get good at it. Dentists also often have just minutes to read them as they hop between patients.

As a result, dentists end up misdiagnosing cavities up to 40% of the time, co-founder Yasaman Nematbakhsh said. Her background is in imaging, where she developed an artificial intelligence machine identifying hard-to-see cancers, something Fesharaki thought could also be applied to dental medicine.

Providing the perspective of a more experienced dentist, Adra’s intent is to make every dentist “a super dentist,” Fesharaki told TechCrunch. Its software detects cavities and other dental problems on dental X-rays faster and 25% more accurately, so that clinics can use that time to better serve patients and increase revenue.

Example of Adra’s software. Image Credits: Adra

“We are coming from the eye of an experienced dentist to help illustrate the problems by turning the X-rays into images to better understand what to look for,” he added. “Ultimately, the dentist has the final say, but we bring the experience element to help them compare and give them suggestions.”

By quickly pointing out the problem and the extent of it, dentists can decide in what way they want to treat it — for example, do a filling, a fluoride treatment or wait.

Along with third co-founder Shifeng Chen, the company is finishing up its time in Y Combinator’s summer cohort and has raised $250,000 so far. Fesharaki intends to do more formalized seed fundraising and wants to bring on more engineers to tackle user experience and add more features.

The company has a few clinics doing pilots and wants to attract more as it moves toward a U.S. Food and Drug Administration clearance. Fesharaki expects it to take six to nine months to receive the clearance, and then Adra will be able to hit the market in late 2022 or early 2023.

Aug
20
2021
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Communication software startup Channels takes on event management with text workflow

Three University of Michigan students are building Channels Inc., a communication software tailored for physical workers, and already racking up some big customers in the event management industry.

Siddharth Kaul, 18, Elan Rosen, 20, and Ibrahim Mohammed, 20, started the company after finding some common ground in retail and events. The company’s customer list boasts names like Marriott Hotels, and it announced a $520,000 seed round, led by Sahra Growth Capital, to give it nearly $570,000 in total funding.

Kaul grew up going to a lot of events in Kuwait and Dubai, but started noticing there was a delay in things that should happen and many processes were being done on pen and paper.

“The technology that was available was inharmonious and made it hard for physical workers to fulfill tasks,” Kaul told TechCrunch. “We saw it happening in the event management space, forcing workers to coordinate across technologies.”

Legacy communication platforms like Slack are aggregating communications, but are better for remote workers; for physical workers, they rely more on text communication, he said. However, the disadvantage with texting is that you have to keep scrolling to get to the new message, and old communication is lost amid all of the replies.

They began developing a platform for small hotels to help them transition to digital and provide communication in a non-chronological order that is easier to access, enables discussion and can be searched. Users of the SaaS platform can build live personnel maps to see where employees are and what the event floor looks like, prioritize alerts and automate tasks while monitoring progress.

Marriott became a customer after one of its employees saw the Channels platform was being tested at an event. He saw employees pulling out their phones and asked the manager why they were doing that, and was told they were testing out the product and referred him to Kaul.

“What they thought was helpful was that it was communication, and though the employees were checking their phones, it was quick and they remained attentive,” Kaul said.

Channels provides a solid platform in terms of analytics and graphical representation, which is a major selling point for customers, leading to initial traction and revenue for the company that Rosen said he expects can occur at the convention level the company is striving for.

The new funding will be used to grow in development and bring additional engineering talent to the team. In addition, it will allow Kaul and Rosen to continue with their studies, while Mohammed will be doing more full-time work. They want to increase their recurring revenue in the Middle East while building up operations in the United States.

Jamal Al-Barrak, managing partner of Sahra Growth Capital, said Channels was on his firm’s radar ever since they won the 2020 Dubai X-Series competition it sponsors. As a result of winning the competition, he was able to see the founders on multiple occasions and hear their growth.

Sahra doesn’t typically invest in companies like Channels, but the firm started a “seed sourcing effort” to make investments of between $200,000 and $800,000 into early-stage companies, Al-Barrak said. Channels is one of the first investments with that effort.

“Channels is one of our first investments in this initiative and they look very promising so far even compared to our investments before we started this initiative,” Al-Barrak said. He liked the founders’ work ethic and their focus on the event industry, which he called, “historically outdated and bereft of technological innovation.”

“Sid, Elan and Ibrahim are some of the youngest yet brightest entrepreneurs I have come across to this day and I have invested in over 25 technology startups,” he said. “Additionally, I enjoyed that they had proof of concept with a prior customer base and revenue. I was most impressed by their vision past their current industry and bounds as they want to encapsulate communication for all physical workers, whether it is events, retail or more.”

 

Aug
19
2021
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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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