UK-based Ravio reckons real-time data is the best way to arm businesses to win the global talent war.

Its new-to-market compensation benchmarking tool lets users see how the compensation (wages and benefits) they offer their own staff compares to the market by pooling data across its employer customers (data is anonymized at the platform level) — idea being to help them offer more competitive packages which help convince talent to sign on the dotted line.

“Customers can explore market data and compare themselves to companies with similar characteristics such as location, industry, funding raised and headcount growth,” it writes of its SaaS platform in a press release accompanying the official launch today.

The software also lets companies “identify compensation inequalities at individual, job type and company levels — and compare to peers in the market”, as it tells it.

“Whether it be gender, background or other criteria, this offers the most detailed diversity insights available to companies who want to make data-driven changes,” is another top-line claim.

The startup, founded out of London, has been operating in stealth mode since starting building work on the platform in January, though it quietly opened up access a couple of weeks ago. (Research work and the idea itself dates back to last year.)

It is now flipping on the light today for its official launch — and also announcing a total of $10 million in seed funding led by Northzone with participation from Cherry Ventures and Spark Capital.

Ravio’s first target is fast growth scale-ups which are of course at the coal-face of the fight for talent. These high profile, high pressure startups are also often on the front line for diversity issues — where Ravio believes its SaaS tool will also provide valuable benchmarking. 

“Hiring and retention is the biggest challenge right now for scaleups due to war for talent, fast moving markets, (over)funding, the shift to remote work and ‘the great resignation’,” argues co-CEO and co-founder, Raymond Siems.

“There’s a lot of clamour on closing pay and diversity gaps at startups but not many tangible numbers or practical solutions — it’s mostly high level reports and talk.”

The follow-on goal for Ravio is to deepen its database by getting more scale-ups signed up — and, ultimately, it hopes to be able to expand the approach to other sectors. After all, notes Siems, there are talent shortages across all sorts of sectors — so a tool that supports businesses to make more attractive compensation offers could have broad utility, assuming the benchmarking insights live up to billing. 

“We’re starting focused but the need for a solution is universal across industries and company sizes, it’s not confined to the tech industry. We validated this during our research phase, with strong feedback from SMEs all the way up to large enterprises in other industries,” he tells TechCrunch.

“High growth startups are the best place to start because they’re the least served by traditional benchmarking providers, and are feeling the talent crunch the most. Employee equity in private companies is one of the least transparent areas within compensation, so we’re tackling this early on.

“By having a focused approach we can build depth in our dataset, before expanding into other sectors.”

Ravio works by a sharing to participate model which means that organizations that want access to its jobs benchmarking data must agree to share info on their own teams, including salary data.

The basic service is free but the team is building additional modules to layer on top that will be paid — so it’s taking a freemium approach to grease its data pipe.

“We gather data from ‘source of truth’ systems including the HRIS, ATS and cap table management software of our customers,” explains Siems. “We primarily connect via API, so there’s no manual work for our customers in submitting and drawing together data, and this enables real-time updates rather than one-off static submissions — which are inefficient and hard to scale.”

“We currently support 28 HR systems which cover all the major players in Europe including Workday, Personio, BambooHR, HiBob and Namely,” he adds.

Per Siems, Ravio’s automated, scalable data-pipeline approach is made possible because of widespread accessibility of employer data held in HR and other business systems via API — it just needs to convince employers to give it API access.

He does not sound concerned that Ravio won’t get a critical enough mass of sign-ups to be able to generate genuinely valuable compensation insights. “We’re set to expand quickly so the market coverage of our dataset will enable us to provide the most useful and granular insights available in Europe, and the only-real time information,” he responds to a question on how comprehensive a view of the market Ravio has at this stage, before reiterating that the benchmarking and compensation analytics product is free.

“It’s a self-service onboarding that can be done in 15 minutes — so it’s as simple as possible for companies to join,” he adds.

Siems also says Ravio is doing some of its own processing of the data it acquires from user companies so that it can extrapolate “beyond our market penetration”.

This “special sauce” incorporates “macro data to predict wider trends”, per Siems, who points to what he says is the most “sophisticated” compensation work currently being done — “by internal teams at big tech players like Meta” — as the team’s inspiration. He says Ravio has also hired people who have experience working on compensation for Big Tech firms including Amazon and Meta, as well as other multinationals like Coca-Cola.

“They buy data from all of the major consultancies and then do their own data science work to build up a more detailed and comprehensive picture. We want to enable this depth and sophistication for companies of all sizes,” he says, adding: “We’re bringing a data science first approach into an area where there has been very little innovation, with a strong data science team from Cambridge and Oxford.”

Ravio isn’t disclosing the total number of scale-ups which signed up pre-launch but Siems, claims it’s already got a “healthy” list of customers who “heard about what we are building and wanted to join our testing phase”.

“Some of the names we’re working with include unicorns Deliveroo, Truelayer, Flink, Zego and other high-growth European startups including Healx, Zoomo and Plum Guide,” he says.

The paid version of the SaaS will be charged based on the number of employees the user has, according to Siems.

“We’re charging for advanced features and two additional modules that we’re releasing in the coming months: Manage and Communicate,” he notes, describing two of the premium products it has in the works. “Our Communicate module includes interactive offer letters for candidates, and a compensation portal for employees, to make it easier for them to understand every part of their package and their employer. This includes education on commonly misunderstood topics like equity and tax.

“Our Manage module provides a suite of tools to allow companies to manage compensation processes effortlessly. This includes organisation level budgeting and banding features, compensation review cycle management workflows, and growth scenario planning.”

On diversity, how ‘full spectrum’ Ravio’s proposition can be is less clear — given Siems says the data available to it “varies by company”.

“We’re seeing the most commonly available data relates to gender, age, country of citizenship, marital status and ethnicity — but increasingly HR systems are supporting wider data fields such as disability status, so we’ll be expanding our analytics as the data allows,” he says on that. 

While employers having better data on competitive rates of pay and broader compensation packages might be good for businesses to win talent, there is a question of whether this necessarily benefits employees — who aren’t being directly empowered to, for example, call out wage gaps inside their own organization, say if certain groups are being paid below market rates, since the tool looks intended for use by the HR department, rather than for general access.

Asked about the risk of one-way real-time information disproportionately empowering employers, Ravio says: “We will be releasing market reports that are freely available — covering both compensation and diversity — that will empower both candidates and other employers.”

“We believe that many of the current inequities in the market are not a result of bad intentions but rather the result of a lack of data,” he adds. “By becoming an objective source of information for employers in a notoriously opaque and difficult to understand space, Ravio’s product will ultimately benefit employees by empowering companies to make unbiased decisions.”

Flush with seed funding and post-official-launch, the team’s focus for the rest of this year is on growth in Europe — where Siems says they spy “huge opportunity”.

“However the talent war is global so we will be quickly expanding our reach to new shores next year,” he adds, noting the startup has grabbed backing from a number of international investors. He also flags the experience of his co-founders, Roy Blanga and Merten Wulfert, in helping to rapidly scale Deliveroo, Groupon and HotelTonight internationally — claiming: “We’re well positioned to execute on our vision.”

On the competitive front, Ravio’s view is “there’s very little on the fairness and team analytics side” — with most of the action focused on compensation benchmarking. So it will presumably be weighting its offering there.

“In some ways we are building a new age version of ‘salary surveys’ and compensation benchmarking services offered by Radford, Towers Watson and Mercer (the big 3 players) which have been around for decades,” Siems suggests, adding: “There is no one offering real-time data in Europe to our knowledge. Some new players are serving tech companies but only with static, manually collected data. In the US there are some new players and older static offerings like Option Impact.

“It’s also possible that HRIS players (e.g. Personio) will look to build their own version but with the market for HR tech being so fragmented — it will be tricky to get worthwhile breadth of data long term. The market is better structured for there to be a HRIS/ATS/Cap table agnostic player like us.”

Glassdoor does already pool user-contributed salary data — so can offer a snapshot of pay rates — but Siems believes Ravio is not directly competing since Glassdoor’s data is unverified, “patchy”, collected via one-off submissions and lacks enough breadth or depth to be useful to companies making compensation decisions.

“Companies need comprehensive data,” he argues. “It also doesn’t have access to information to enable team or fairness analytics.”

Commenting on Ravio’s seed in a statement, Michiel Kotting, partner at Northzone, said: “We see a huge market shift happening which is going to leave behind companies who don’t modernise their approach around compensation. Winning companies will be transparent with compensation in the context of rising prices (inflation, cost of living, logistic costs) and a tighter talent market, especially in tech. Roy, Merten and Raymond are going after a problem that all companies big or small have, and are poised to build a leading company in the category. They have the experience and leadership to tackle all things compensation from salaries to equity and benefits, all big pain points for fast-growing companies.”

BandLab, the Singapore-based app that lets users create and share music, announced today it has raised a $65 million Series B, at a post-money valuation of $315 million. The investment round was led by Vulcan Capital, with participation from Prosus, Caldecott Music Group and K3 Ventures.

The company says that over 40 million creators currently use Bandlab. The app’s tools for creating music include a Mix Editor, royalty-free sounds, Mastering and SongStarter, or royalty-free compositions. Co-founder and CEO Meng Ru Kuok told TechCrunch that the new funding will be used on hiring, developing new features and “prioritizing new ways to benefit from the creator economy for artists and rights holders.”

Bandlab is also known for its acquisitions. It once owner of half of Rolling Stone, which it exited in 2019, before acquiring stakes in NME and Uncut. Most recently, it bought musician discovery platform ReverbNation.

Several of ReverbNation’s features have already been integrated into BandLab, including Crowd Review, which lets musicians gauge how audiences feel about their music before it is released, and Promote Your Track, for developing ad campaigns on Facebook, Instagram and music websites.

When asked about the possibility of future acquisitions, Ru Kuok said the company sees “some extremely interesting opportunities in the market,” but none that they can disclose right now.

Companies that rode COVID-driven demand for their products during the first years of the pandemic are seeing their fortunes come back to Earth. Whether some of the biggest names in the cohort have a next act is becoming an open question.

Even more, could it be that companies that fell out of favor due to COVID-induced shifts in the economy are best prepared to excel this year?

The Exchange explores startups, markets and money.

Read it every morning on TechCrunch+ or get The Exchange newsletter every Saturday.

It’s never fun to sit around and list bad news. But the tally is starting to pile up: Today’s value of Robinhood, the consumer equities and crypto trading service boosted by the pandemic’s savings and investing boom, is worth just over $10 per share, far from its 52-week high of $85 per share. Coinbase, another company that saw demand for its fintech trading services soar during COVID, is worth just over $130 per share today, sharply lower than its $368.90 per-share 52-week high.

The list goes on: Instacart’s growth is slowing after a torrid period of expansion, leading to a valuation reset at the company. And recently, the Financial Times reported that the value of Hopin’s stock is off sharply on secondary exchanges, and some externally visible data could hint at a demand decline. The company executed layoffs earlier this year.

Seeing a rush of growth is never unwelcome at companies. And such a boom is especially coveted by companies usually valued more on growth than profitability. (Startups, in other words.)

That which has gone up is, it seems, coming down. Let’s talk about it.

Risk tolerance

The global economy is taking hits from many sides at once. Inflation concerns in some markets are stacked against growth woes in others. Geopolitical tensions are running high as the United States and China spar over trade and hot-button issues like the right of Taiwan to self-govern. Russia is busy digging into a quagmire in Ukraine, disrupting the energy market while supply chains creak and jam — not to mention the catastrophic loss of life. COVID lockdowns in China are also causing fears of more supply snarls, or worse.

The ebullient mood of late 2020 and most of 2021 this is not. And startups seeing their growth rates decelerate as their pandemic-led boom in demand fades, therefore taking stick twice at once.

Hello and welcome back to Equity, a podcast about the business of startups, where we unpack the numbers and nuance behind the headlines. Every Monday, Grace and Alex scour the news and record notes on what’s going on to kick off the week.

• Stocks are down, and cryptos not looking too impressive as the world gears up for a packed week of mega-tech earnings.
• The Twitter-Elon Musk deal could happen soon? As soon as today? It appears that after Musk dropped a filing indicating that he actually had the funds to buy the deal, talks shook loose. What’s ahead? I have precisely and exactly no idea.
• Hopin is perhaps enduring some turbulence, per the FT. The company, once riding a torrid wave of market demand, is seeing its business molt into a more steady form. That meant layoffs earlier in the year, and a decline in its share price on secondary exchanges.
• Startups! From the startup-realm this morning, new rounds for Zenda and Rooser. Not Rooster, mind, just Rooser.
• And there’s a general climate of fear out there, which won’t do much for market sentiment. Alas, 2022 is not 2021 when it comes to investor excitement.

And we have a live show coming this week! Get stoked, details to follow.

Even in markets where credit card penetration is high, shopping cart abandonment is still a major source of concern for online vendors. Now imagine the situation in Southeast Asia, where many countries have scores of e-wallets, buy now pay later services and other forms of payment. Bank transfers are also popular option for online purchases, but involve several steps, which increases the risk of cart abandonment.

Arrow wants to make the checkout process easier by acting as a layer on top of payment gateways. It supports more than 50 different payment methods, including all the major ones in Singapore, Malaysia and Indonesia (including Atome, GrabPay, Boost and GoPay).

The company announced today that it has raised $4.8 million led by Sequoia Capital India, with participation from Alpha JWC and Zinal Growth. Angel investors including AIG and Maxis board member Ooi Huey Tyng, Paysend chief operating officer Steve Vickers and Coinbase head of Southeast Asia Hassan Ahmed also participated in the round.

Launched 15 months ago, Arrow was founded by Liat Beng Neo and Sebastian Roervig and is now used by 100 merchants. Liat Beng Neo told TechCrunch that a major reason for cart abandonment is that “the current checkout processes in the region do not account for its incredible diversity. Southeast Asia is made up of eleven different countries, each with their own unique e-commerce habits and nuances.”

For example, he added that some regions have poor internet connectivity, so customers may drop out of the checkout process if it involves clicking multiple webpages. Even popular payments, like bank transfers, involve several steps, and each one means the risk of a customer changing their mind about a purchase.

In addition to payment methods, Arrow also integrates shipping information and affiliated loyalty programs, so customers see everything on a single checkout page. Arrow can be integrated into shopping platforms like WooCommerce or Magneto or through APIs that let merchants replace their existing storefronts with Arrow. For social commerce, retailers get a checkout link they can message to their customers.

Arrow can be used by all kinds of merchants, but is focused on FMCG and other discretionary goods and services, Liat Beng said, because those tend to have high cart abandonment rates. It also caters in particular to merchants who deal in high order volumes, since they would benefit the most from improvements to cart abandonment rates, he added.

Arrow is currently active in Singapore, Malaysia and Indonesia, and plans to focus on those three markets for now, while planning for expansion into the Philippines, Thailand and Vietnam.

MadEats, Y Combinator alum, claims to be the first “‘full-stack’ delivery-only startup in the Philippines,” with their own virtual storefront, ghost kitchens and fleet of drivers. More than that, they also conceptualize and launch their own brands, making them a delivery-only restaurant group.

The company announced today it has raised $1.7 million in seed funding led by JAM Fund, Crystal Towers Capital, Starling Ventures, MAIN and Rebel Fund.

Launched in November 2020, MadEats currently has three ghost kitchens: one each in Makati, Quezon City and the City of Manila. They aim to cover more of Metro Manila’s north, and eventually open physical storefronts, too.

Before founding MadEats, CEO Mikee Villareal told TechCrunch that the team worked for some of the top restaurant groups in the Philippines, launching, managing and working on over 20 restaurant concepts. “At the beginning of the pandemic, we were asked to operationalize these restaurants to be delivery-forward due to stringent quarantine restrictions,” she said. “Dine-in concepts were heavily affected and we saw the need for our business.”

She added that ghost kitchens have a different cost structure than traditional restaurants, which gives the team freedom to create product concepts that are more delivery-friendly.

MadEats now has six brands and is expanding its portfolio: Yang Gang (Korean fried chicken); Chow Time (Chinese takeout); Fried Nice (fried rice); Dot Coffee; MadBakes (a test kitchen for desserts), and MadMakes for bulk orders, corporate packages and packed meals. The company is currently adding more brands, including smash burgers and Japanese food.

MadEatsOS, its suite of internal tools, is what makes MadEats approach scalable. It includes an automated order routing system that makes sure orders are fulfilled at the nearest location, and analytics that show which brands and food items are performing well.

The company has its own MadEats riders and as demand for orders increases, have also worked with third-party logistics providers. It is available on third-party apps like GrabFood and Foodpanda, but Villareal said over 50% of its orders come in through its own platform, Madeats.co.

For years grocery retailers have been using data driven forecasting to help them predict demand to figure out which products to reorder to keep shelves stocked. That’s nothing new. But Berlin-based startup Freshflow is targeting a particular slice of this market: It’s built an AI-powered forecasting platform to help retailers optimize stock replenishment of fresh, perishable goods — such as fruit and vegetables, meat, dairy and bakery products — in order that food waste is minimized and retailer revenue maximized.

It says its first customer has seen a 28% reduction in food waste and a 16% increase in revenue after around eight months using its AI-powered system to automate fresh produce restocking — with average rates across the (handful of) early adopters standing at 30% less food waste and a 16.7% revenue boost.

A quirk of grocery retailing is that fresh produce reordering is often still done manually, says Freshflow co-founder Avik Mukhija, with supermarket staff taking what often amount to “gut instinct” decisions on how much fresh produce to reorder — which can lead to over-ordering that not only hits revenue but leads to food waste as unsold items spoil quickly and have to be thrown away; and also under-ordering — meaning retailers are losing out on extra revenue if shoppers are frustrated by empty shelves.

The reasons manual reordering has persisted for this (fresh) segment of grocery retail are myriad, according to Mukhija — including short (but non-uniform) shelf lives; quality variation; seasonality; and products often being sold by weight rather than piece, which complicates ERP inventory data. “Those challenges combined make fresh products inherently different to packaged,” he argues, saying it’s been almost a “retail mantra that a human being could still manually do this better than a system could.”

“And because that is the opinion … until now, for the most part, retailers have just relied upon people to do this part.”

Freshflow’s premise is that machine learning can do a far better, less wasteful job at restocking fresh food than the human eye, nose and gut by being able to weight a variety of factors that may affect demand (such as weather, season, local events) and by crunching available retailer data to do probabilistic modelling and predictions (such as to forecast the shelf life of different produce) to — overall — more accurately match supply and demand.

Mukhija says its early results (albeit for a small number of customers) stand that up. “Our prediction is definitely better than what has been done historically using gut feeling because we have reduced the waste and seen a significant revenue increase.”

“When you look at the graphs between what we predict in terms of sales and what actually happens it almost tracks it perfectly,” adds co-founder Carmine Paolino — who, when asked about the model’s accuracy, tells us the “mean absolute error” for its predictions so far is <1.

Assuming Freshflow’s AI can maintain this early performance as it scales to serve more retailers, the startup looks to be onto something big and important: As it notes, the grocery retail sector is responsible for some 5% of the total amount of food thrown away annually, equating to more than 4.5 million tonnes. While, in Europe, over-ordering caused by poor demand prediction contributes to $50 billion of fresh food being thrown away by retailers each year.

Food waste is also a huge contributor to climate change, generating what end up being totally unnecessary carbon emissions, which means shrinking wastage here isn’t just about optimizing retailer profits — it’s super important if humanity is to successfully tackle climate change.

Freshflow co-founders Carmine Paolino (L) and Avik Mukhija. Image Credits: Freshflow

The Berlin-based startup, which was founded just over a year ago, isn’t alone in spotting the opportunity to apply machine learning techniques such as probabilistic modelling to fresh food ordering. It’s competing with a number of U.S. startups like Afresh and Shelf Engine. While in Europe, the competitive field looks a bit thinner but there are more generalist retail demand planning platforms like Relex — as well as, of course, the German ERP giant SAP — but Freshflow argues that its dedicated focus on fresh produce gives it an edge for fresh groceries versus less specialist demand forecasters.

Beyond that, another differentiating factor it claims is around ease of integration for retailers. Freshflow’s platform is designed to sit as a layer on top of retailers’ existing ERP systems — and Mukhija says customers can be up and running with the platform in about a month.

“One of the key properties of Freshflow is that integration is super lightweight,” he tells TechCrunch. “Usually when supermarkets are onboarding new IT systems it’s several months or several years of integration time that needs to happen. Because they have very outdated ERP systems and there’s no proper IO/APIs that they have.

“With Freshflow we are able to onboard them within one month — and the reason is we are a lightweight layer on top of their existing ERP system so there’s no need to integrate specifically. We work off of data pipelines that deliver us data out of their system.”

On the store floor, the product takes the form of an iPad app that is used by the produce team — informing them of the recommended restocking levels per product. This reordering process is intended to be largely automated by Freshflow’s app but the human staff can step in and override the AI’s recommendation for a particular product order if required.

Freshflow says its system is live with one of Germany’s largest grocery retailers, as well as with an Eastern European quick commerce player. It’s not naming any customers as yet but, when pressed, tells us the SaaS is live in four stores in total.

It’s announcing a €1.7 million seed funding round today, led by German venture fund Capnamic and European climate tech VC, World Fund — with a number of strategic angel investors also participating, including Alexander Mrozek CEO at Dr. Oetker Digital and Jens Fiege and Felix Fiege, CEOs at FIEGE Logistics — and says it intends to use the new financing to expand its footprint in Europe with the goal of getting its SaaS into 100 stores.

Commenting on Freshflow’s seed raise in a statement, Tim Schumacher, general partner at World Fund, said: “Almost 40% of all food produced globally is wasted and this is causing six times the carbon impact as the entire aviation industry worldwide. That’s what makes Freshflow’s goals so admirable and was such a key reason that we at World Fund wanted to support them. Avik and Carmine have developed a very exciting state-of-the-art AI engine which is already seeing remarkable results and I have no doubt that the future is very bright for Freshflow.”

In another supporting statement, Dorothea Gotthardt, investment manager at Capnamic Ventures, added: “At Capnamic we were immediately impressed by the scale of ambition when it comes to Avik, Carmine and the team at Freshflow. Tackling the issue of food waste at the same time as boosting a retailer’s bottom line is an eye-catching proposition and given the results so far, one they are clearly delivering on. The sooner they can expand across the continent the better it will be for retailers and consumers. I can’t wait to see where Freshflow goes in the next 12 months.”

Freshflow also previously took in some pre-seed funding via the Berlin-based Entrepreneur First accelerator program where its two co-founders — who both have a background in machine learning — met and decided to join forces around the idea.

“Our AI works from a combination of data that comes from the stores — from the retailers — we have sales data, ordering data, we have shrink [produce wastage] data, we have the data about the products … sometimes also nutritional info, all kinds of information we get from them. We also get external information such as the weather data. And sometimes also location data — if there are local events and stuff. And we combine it all together in our machine learning models,” says Paolino, explaining how the AI cooks up its demand predictions.

“Also we use probabilistic inventory that basically knows what are the shelf lives on the products. Which is one of the biggest challenges in our space — especially with perishable products — because we’re able to actually model the shelf lives on the data that we get from supermarkets and this is because every single supermarket handles fresh products in a different way.”

Paolino gives the example of a store leaving a certain type of vegetable unrefrigerated (say after it arrives and before it’s shelved and/or put into cold storage) even for a relatively short period of time, which may nonetheless substantially impact the overall shelf life for that product — meaning that’s another variable the system needs to factor in.

To do that, Freshflow isn’t in the store surveillance business; rather it’s working backward from data points the supermarkets provide as part of their standard produce tracking processes — i.e., information on unsold/spoiled fresh food which, combined with data on when that produce arrived at the store, can be used to probabilistically predict shelf life.

“So we have this machine learning system that figures out by itself the self lives of products — then we have an inventory that is probabilistic and we know more or less what is in the inventory and we combine it all together in our machine learning model to essentially predict what should they order,” adds Paolino.

Freshflow expects its predictions to further improve over time, as they ingest more data by onboarding more customers — and as customers use the system and feed back more data points (including, for example, any staff decisions to override a restocking suggestion) — as its algorithms get better at understanding the longevity of perishable goods and at predicting human demand for fresh foodstuffs.

It also points to steadily increasing consumer demand for fresh produce — while noting that supermarkets’ cold storage space remains fixed — hence the team predicts growing demand from grocery retailers for smarter approaches to replenishing these sought for fresh goods.

“Especially when you’re looking at brick-and-mortar retailers, their revenue in fresh products is the only product category that’s actually increasing versus online because people like to go inside the store and buy fresh stuff. And storage space is obviously remaining the same size,” adds Mukhija. “We’ve noticed that with our existing clients; those cold storage rooms go full very quickly and then things go bad very quickly too, obviously.”

The startup’s longer-term vision is to expand from serving customer-facing grocery retailers to warehousing facilities, suppliers and even, ultimately, farmers — with a goal of automating the whole fresh food supply chain and reducing global food waste by 50%. Or, well, that’s the moonshot.

Couldn’t supermarkets do this kind of predictive modelling themselves — given they already own a lot of the core data needed to train forecasting AIs in the vagaries of perishable groceries?

“Generally supermarkets do what they do best and this is how we see the future in the retail world,” replies Mukhija. “We always hear this from store managers — they want to make sure they’re attending to their customers. That the fresh produce section looks really nice and presentable and a lot of quality checking is going on on the shelves. But they don’t want to be spending time doing data analytics and building AI teams in-house. It’s extremely expensive — you’re reinventing the wheel every time. And it’s also hard to attract top notch AI talent.”

Paolino also chips in on that to say Freshflow is planning to experiment with applying reinforcement learning technology to its AI-powered predictions, emphasizing: “And that is really, really hard to do — also for retailers.”

French startup Greenly has raised a $23 million Series A round. The company has built a software-as-a-service platform that lets you calculate your company’s carbon emissions, store and track them in one place, generate a certified report of your carbon footprint and get some insights about ways to reduce your emissions.

Energy Impact Partners (EIP) and XAnge are leading today’s funding round. Several business angels are also participating in the round, such as Jean-Baptiste Rudelle.

Greenly isn’t the first carbon management startup — competitors include Sweep, Persefoni and Watershed. But Greenly focuses specifically on small and medium companies. And the market is still mostly dominated by big consulting firms.

The company tries to automate as many steps as possible with integrations with dozens of data sources. After that, Greenly helps you go one step further with instructions to improve your reporting process and get more granular data. It can then generate a carbon report that follows the Greenhouse Gas Protocol.

Customers who start using Greenly first connect the service with their accounting and financial data. The startup is using Codat to integrate seamlessly with a wide range of financial services.

For low-priority items, Greenly can automatically calculate an estimate of carbon emissions. For instance, some software companies publish a dollar-to-carbon ratio in their own carbon footprint reports. If you spent $850 in Salesforce product, Greenly can evaluate the carbon impact of that.

For bigger sources of emissions, the platform can ask you questions to specify what you bought exactly. Greenly has gathered hundreds of thousands of data points from public database. It tries to learn over time so that you don’t have to answer the same questions over and over again.

“We started with financial and accounting data, but we are progressively adding more data sources,” co-founder and CEO Alexis Normand told me.

The startup is building out integrations with electricity providers, cloud services or e-commerce platforms. For instance, Greenly can automatically fetch your Shopify inventory or the number of vCPUs you are using in your cloud infrastructure.

But the most difficult part of the carbon footprint equation is what’s happening upstream and downstream. How can you evaluate the carbon impact of your suppliers?

Greenly has turned this hurdle into an advantage as it creates a viral loop. Customers can send a link to a Greenly portal to their suppliers so that they can share more information about their carbon emissions.

“If you want to go further, you have to engage your suppliers. We ask our clients to send a request to gather more granular data. After that, we consolidate everything once we have all the information, and we can grade suppliers,” Normand said.

Once these suppliers have tried the product, they can start using it for their own company. “We want to build the Quickbooks of the carbon footprint,” Normand said.

There are also some legal requirements that are fostering this software industry. For instance, in France, companies with more than 500 employees have to release a carbon report. In a few years, the limit will be lowered to 250 employees.

90% of the startup’s customers are SMEs, but some bigger companies are also using the platform to compare their suppliers. Once you have onboarded all your suppliers on the platform, you can choose to work more with one over another due to environmental criteria.

There are 400 companies currently using Greenly. While most of them are currently based in France, the company has opened an office in the U.S. to address the American market.

Ordinary Folk, a Singapore-based telehealth startup dedicated to men and women’s health issues, has raised $5 million in pre-seed funding from Monk’s Hill Ventures. The funding will be used for hiring and expand into Hong Kong while scaling in Singapore.

Founded in 2020 by Sean Low, the startup has two main platforms: Noah is for men’s sexual health, mental wellness, hair care and weight management, while Zoey focuses on sexual wellness, fertility, mental health and wellbeing.

Low says he started Ordinary Folk to ease the pain points of an in-person clinical visit, while also making it easier to seek care for stigmatized conditions like erectile dysfunction.

“Men’s and women’s health conditions are intimate problems that affect all of us at some point of our lives, whether directly or through your partner,” he told TechCrunch. “And before we started Noah and Zoey, there weren’t any good solutions in Singapore and Hong Kong.”

The company chose Hong Kong as its next market to expand into because there are many similarities between Singapore and Hong Kong, Low added. For example, both are densely-populated and fast-paced, with healthcare systems that have the same issues, he said.

“While there are nuances, Singaporeans and Hong Kongers also identify similarly on issues such as high healthcare costs, fear of illegitimate medication, inconvenience of visiting a doctor and the stigma attached to men’s and women’s health conditions,” he explained.

Ordinary Folk says that since its launch, its revenue has grown by over 130% and it has had over a million unique visitors. It differentiates from other telemedicine startups by building a full healthcare stack, Low said, including healthcare and logistics for medication in non-description packaging.

This also means Ordinary Folk was able to create a health assessment patients take before scheduling an appointment, allowing doctors to make more detailed diagnoses.

“In the case of sexual health, having to answer intimate questions can be tough and what more to a stranger whom you’ve never met,” said Low. The health assessment was developed in partnerships with doctors and health experts. Ordinary Folk’s network of providers include physicians, psychologists, therapists and other specialists.

In a prepared statement, Peng T. Ong, the co-founder and managing partner of Monk’s Hill Ventures, said, “Millions of people across Asia find it difficult to access proper treatment and care for health conditions that have tremendous taboo attached. Through Noah and Zoey, Ordinary Folk is uniquely positioned to bring in value through the consumer journey of healthcare services, creating an ecosystem where patients have access to medical experts and products, and a wide range of treatment options.

Everstage founders Vivek Suriyamoorthy and Siva Rajamani

For sales reps, commission plans are often complicated and lack transparency, leading to accounting errors and frustration. Everstage, a sales commission platform, solves that, letting sales rep see exactly how much they earned. It also has features to estimate how much commissions they can potentially make from their deals pipeline. Today, the startup announced it has raised $13 million in Series funding led by Elevation Capital.

TechCrunch first covered Everstage last August when it announced a $1.7 million seed round from 3one4 Capital, which also returned for this round.

Everstage was founded in 2020, by Siva Rajamani, the former lead of Freshworks’ global revenue operations team, and Vivek Suriyamoorthy. Its customers include Chargebee, Postman, Nitro, Hackerrank and CleverTap.

Everstage has added new features over past year: visibility of instant commissions on Slack, contract management and incentives gamification. For example, sales reps can input different scenarios and see how that will affect their payout estimates. Everstage also has leaderboards, so sales reps know how they are ranked among their team.

Rajamani told TechCrunch that since Everstage’s seed funding, its revenues have gone up by 5x, thanks to a 6x increase in customers. While most of Everstage’s customer base is in the United States, it also now has clients in Europe, the Asia Pacific and Africa. It is used by companies in verticals including tech, business services, financial services, insurance, health tech and logistics. Everstage also increased the size of its team from 20 at the time of its seed funding to 70 now.

The funding will be used to grow Everstage’s go-to-market, product and engineering teams, and also on branding and marketing.

In a prepared statement, Akarsh Shrivastava, Principal at Elevation Capital said, “We were super impressed at how unlike the current legacy vendors, Everstage, elegantly allowed companies to design and manage even the most complex plans, while ensuring a modern UI/UX and consumer app like experience.”

In an expected move, European music streaming company Deezer announced plans yesterday to go public via a French SPAC.

The deal values the company at a pre-money equity valuation of €1.05 billion and at €1.08 billion in enterprise value terms. Notably, those prices are similar to those at which Deezer last raised known external capital, a €160 million round in 2018 at a valuation of €1.0 billion on a post-money basis.

Naturally, with Spotify battling Apple Music for global music streaming ascendancy, and rivals Amazon, YouTube, and others competing for market share, you might have forgotten about Deezer, especially if you are not located in Europe. Still, the deal is happening, and that means we’ve been given a sheaf of information about the company.

The Exchange procured the company’s release and investor presentation. Let’s parse the data and see what the economics of a smaller music streaming business look like today.

Inside the Deezer SPAC

Starting with some top-level numbers, Deezer had 9.6 million subscribers at the end of 2021, leading to it calling itself the “#2 independent music platform globally.” Fair enough; that’s more subscribers than I would have guessed. The company generated €400 million in 2021 revenue.

Here are the company’s core financials, via a release:

Image Credits: Deezer release

A few things jump out at once, including slow revenue growth in 2021 and negative historical growth in 2020. The company has also posted decreasing gross profit results — and falling gross margins — in recent years. Those declines are contrasted against rising sales and marketing costs, leaving the company with stiff — and growing — deficits.

Even if we allow Deezer to dramatically tweak its profit numbers through the adjusted EBITDA moniker — more here — the results are still a bit blah:

Image Credits: Deezer release

Looking ahead, Deezer said in a release that it has “the ambition to achieve €1 billion revenue by 2025.” Again, fair enough. So, really, how good of a shot does the company have at growing into those numbers in the timespan listed?

How will Deezer grow?

As with all SPAC decks, the Deezer presentation is a hoot. Observe the following slide excerpt:

How much will the changing valuation profile of software companies impact the highest-flying private unicorns? Also, why hasn’t Databricks gone public yet? The answer to the former might be the answer to the latter.

TechCrunch has spent ample time since the end of 2021 tracking changes to the value of software revenues. To catch you up: A number of factors commingled to create a climate in which software companies are worth less now than they were during much of 2021 when valued on a revenue-multiple basis.

The Exchange explores startups, markets and money.

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One outcome of this particular matter has been concern that many startups that raised capital last year at a high price will struggle to defend — let alone advance — their valuations when they next raise capital. And since it is not expected that the startup asset class will suddenly become cash-flow-breakeven, more funds will be needed. This sets up an awkward situation in which external observers — which include potential employees, mind — cannot tell which startups that attracted external capital at an aggressive valuation are hollow and which are not.

But there is a particular class of company to consider, a subset of the high-priced startup cohort: the mega-unicorns. These companies — private-market former startups that have the highest valuations — are in theory those closest to going public.

My question this morning is just how the change in market valuation conditions impacts this small set of companies. So let’s go back to our prior work on Databricks, which most recently raised $1.6 billion at a $38 billion valuation, and see what the new reality can tell us.

What’s Databricks worth today?

A few data points to remind you of where the company stands:

• February 2021: Databricks raises $1 billion at a $28 billion valuation against ARR of $425 million.
• August 2021: Databricks raises $1.6 billion at a $38 billion valuation against ARR of $600 million.
• February 2022: Databricks announces that it closed 2021 with more than $800 million worth of ARR.

Those work out to revenue multiples of roughly 66x, 63x, and 47.5x.

While we don’t know precisely when Databricks reached each revenue milestone, and we are not able to know exactly how far ahead of $800 million in ARR the company was at the close of 2021, we can infer that the company was adding around $50 million in ARR per month in the final quarter of last year. As it has been nearly four months since the start of the year, we can loosely say that Databricks should be at the $1 billion ARR mark today, more or less.

That brings the company’s revenue multiple down to a far more modest 38x. Our question is whether that number makes any sense.