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Venture capital undermines human rights
The future of technology is determined by a handful of venture capitalists. The world’s 10 leading venture capital firms have, together, invested over $150 billion in technology startups. The venture capitalists who run these firms decide which startups today will develop the new platforms and technologies that will shape our lives tomorrow.
There is a startling lack of diversity within the venture capital sector. This means that a small group of men — mostly white men — make decisions that affect all of us. Unsurprisingly, they all too often ignore the broader societal and human rights implications of these investment decisions.
We all live in a world shaped by venture capital. As of 2019, 81% of all venture capital funds worldwide are clustered in just a handful of countries, primarily in the U.S., Europe and China, which in turn are shaping the future of technology. If you spend time on Facebook or Twitter, use Google, travel in an Uber or stay in an Airbnb, then you’ve experienced firsthand the impact of venture capital funding.
Venture capital firms, which provide equity financing for early- and growth-stage startups, play a critical gatekeeper role, deciding which new technologies and technology companies will receive funding.
Venture capital firms need to institute human rights due diligence processes that meet the standards set forth in the UN Guiding Principles on Business and Human Rights.
All businesses — including venture capital — have a responsibility to respect human rights. In order to ensure that their investments are not undermining our human rights, it is therefore critical for venture capital firms to conduct due diligence processes before making investments.
Amnesty International recently surveyed the world’s largest venture capital firms and startup accelerators. Of the world’s 10 largest venture capital firms, not a single one had an adequate human rights due diligence process that met the standards set forth in the UN Guiding Principles on Business and Human Rights.
Unfortunately, this is true of the broader venture capital sector as well. Overall, of the 50 VC firms and three startup accelerators analyzed by Amnesty International, we found that almost all of them lacked adequate human rights due diligence policies and processes.
This failure to carry out adequate due diligence means that a vast majority of VC firms are failing in their responsibility to respect human rights.
This almost complete lack of respect for human rights among the world’s largest venture capital firms has three key impacts. First, and most immediately, it means that venture capital firms invest in companies whose products and services have been implicated in ongoing human rights abuses, such as companies that provide support to the Chinese government’s repression of the Uyghur population in Xinjiang and across China.
Second, it means that venture capital firms continue to fund companies whose business models have a significant negative impact on human rights, including our privacy and labor rights. For instance, leading venture capital firms continue to support companies that rely on app-based or “gig” workers, who often face exploitative or otherwise abusive work conditions, as well as companies whose “surveillance capitalism” business model undermines our right to privacy.
Third, the lack of human rights due diligence by venture capital firms dramatically increases the risk that they fund new and “frontier” technologies without ensuring that adequate human rights safeguards are in place.
For instance, the application of increasingly powerful artificial intelligence/machine learning (AI/ML) tools across a wide variety of sectors risks amplifying existing societal biases and discrimination. Seemingly objective algorithms can be biased by reliance on incomplete or unrepresentative training data, and/or by replicating the unconscious bias of those who developed the algorithms.
This is a critical blind spot, especially as VC-funded startups seek to disrupt such fundamental parts of our lives as education, finance and health.
The negative impacts of the VC firms’ lack of human rights due diligence — especially regarding issues like algorithmic bias — are magnified by these firms’ own lack of gender and racial diversity. For instance, women comprise only 23% of venture capital investment professionals (i.e., those involved in deciding which startups to fund).
The numbers are even worse when it comes to racial diversity — just 4% of investment professionals at VC firms in the U.S. are Latinx, and only 4% are Black. Groups like Blck VC, Diversity VC and digitalundivided have been calling attention to this issue for years, but venture capitalists have been slow to respond so far.
This lack of diversity is mirrored in the gender and racial composition of founders who receive VC funding. In 2018, all-female founding teams received just 2.2% of all U.S.-based venture funding. At the same time, Black and Latinx founders received less than 2.3% of all U.S.-based venture capital funding in 2019.
With power comes responsibility. Venture capital firms need to institute human rights due diligence processes that meet the standards set forth in the UN Guiding Principles on Business and Human Rights.
Further, they should provide support to their portfolio companies to ensure that they comply with human rights standards. Venture capital firms should also publicly commit to hiring more diverse teams, especially in investment-related positions. Finally, they should publicly commit to funding more diverse startup founders as part of their flagship funds.
VC firms have a responsibility to ensure that their investments are not causing harm. A responsibility that they have, to date, largely ignored.
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Enterprise AI 2.0: The acceleration of B2B AI innovation has begun
Two decades after businesses first started deploying AI solutions, one can argue that they’ve made little progress in achieving significant gains in efficiency and profitability relative to the hype that drove initial expectations.
On the surface, recent data supports AI skeptics. Almost 90% of data science projects never make it to production; only 20% of analytics insights through 2022 will achieve business outcomes; and even companies that have developed an enterprisewide AI strategy are seeing failure rates of up to 50%.
But the past 25 years have only been the first phase in the evolution of enterprise AI — or what we might call Enterprise AI 1.0. That’s where many businesses remain today. However, companies on the leading edge of AI innovation have advanced to the next generation, which will define the coming decade of big data, analytics and automation — Enterprise AI 2.0.
The difference between these two generations of enterprise AI is not academic. For executives across the business spectrum — from healthcare and retail to media and finance — the evolution from 1.0 to 2.0 is a chance to learn and adapt from past failures, create concrete expectations for future uses and justify the rising investment in AI that we see across industries.
Two decades from now, when business leaders look back to the 2020s, the companies who achieved Enterprise AI 2.0 first will have come to be big winners in the economy, having differentiated their services, scooped up market share and positioned themselves for ongoing innovation.
Framing the digital transformations of the future as an evolution from Enterprise AI 1.0 to 2.0 provides a conceptual model for business leaders developing strategies to compete in the age of automation and advanced analytics.
Enterprise AI 1.0 (the status quo)
Starting in the mid-1990s, AI was a sector marked by speculative testing, experimental interest and exploration. These activities occurred almost exclusively in the domain of data scientists. As Gartner wrote in a recent report, these efforts were “alchemy … run by wizards whose talents will not scale in the organization.”
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Financial concierge startup Zeni banks $34M to show SMBs their finances in real time
Zeni, a Palo Alto fintech company providing real-time financial services data to venture-backed startups, raised $34 million in Series B funding led by Elevation Capital.
The new investment comes just five months after Zeni announced $13.5 million in a combined seed and Series A round. The company has now raised $47.5 million in total since it was co-founded in 2019 by twin brothers Swapnil Shinde and Snehal Shinde.
Elevation was joined in the new round by new investors Think Investments and Neeraj Arora, as well as existing investors Saama Capital, Amit Singhal, Sierra Ventures, Twin Ventures, Dragon Capital and Liquid 2 Ventures. As part of the investment, Ravi Adusumalli, founder and managing partner at Elevation Capital, will join Zeni’s board.
The Shinde siblings started the company after selling their last company, Mezi, a travel concierge, to American Express in 2018. Zeni’s AI-powered finance concierge platform offers bookkeeping, accounting, tax and CFO services, managing these for a flat monthly fee starting at $299 per month. Founders have real-time access to financial insights via the Zeni Dashboard, including cash in and out, operating expenses, yearly taxes and financial projections. They can also download the financial data in the “slice” that they want.
At the time of its seed/Series A round, the company was managing more than $200 million in funds each month, and that has ballooned to more than $500 million, CEO Swapnil Shinde told TechCrunch. Its customers range from pre-revenue startups to businesses generating more than $100 million in annual revenue.
In addition to the cash in and cash out analysis, the company also created a search function for transactions and spend and income trends on every customer and vendor, Snehal Shinde, chief product officer, said.
Zeni’s dashboard
Zeni experienced 550% revenue growth year-over-year, while the company’s customer base grew 375%, driven by referrals and organic growth, Swapnil Shinde said.
Despite the growth, the Series B came as a surprise to the siblings. The company was already “very well capitalized,” with a majority of the previous round still around, Swapnil Shinde said.
However, Zeni began receiving so many inbound inquiries that he said it was too exciting to pass on. Especially with the addition of Elevation Capital as an investor. Shinde said that was appealing because the firm was an investor in Paytm, and “knows how to partner and build unicorns.”
The new funding will be used to continue scaling and building the bookkeeping and accounting functions and to accelerate hiring, particularly in the engineering, sales and finance team verticals. Shinde expects to double or triple the finance team in the next year.
“As our customers scale through to their Series B, the more you can use our solution in real time to see what is happening with your finances, especially with startups and businesses having more of a remote workforce,” Swapnil Shinde added. “Zeni fits with that.”
Ash Lilani, managing partner at Saama Capital, one of Zeni’s earliest and largest investors, said he knew how big the total addressable market was — $200 billion — and how much these kinds of financial services were a giant pain point for startup companies.
“To know where you stand financially in real time is hard to do, usually, you get that information at month-end,” Lilani said. “I believe we have the opportunity to build a large company. Though Zeni is going after startups today, the small and medium markets can be leveraged. As they grow, Zeni will become their controller on the back end, while companies can just hire a CFO for the strategic decisions.”
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Buildots raises $30M to put eyes on construction sites
One year after raising $16 million, construction technology company Buildots is back to claim another $30 million, this time in Series B funding.
Lightspeed Venture Partners led the round, with participation from previous investors TLV Partners, Future Energy Ventures, Tidhar Construction Group and Maor Investments. This gives the company $46 million in total funding, Roy Danon, co-founder and CEO of Buildots, told TechCrunch.
The three-year-old company, with headquarters in Tel Aviv and London, is leveraging artificial intelligence computer vision technology to address construction inefficiencies. Danon said though construction accounts for 13% of the world’s GDP and employs hundreds of millions of people, construction productivity continues to lag, only growing 1% in the past two decades.
Danon spent six months on construction sites talking to workers to understand what was happening and learned that control was one of the areas where efficiency was breaking down. While construction processes would seem similar to manufacturing processes, building to the design or specs didn’t happen often due to different rules and reliance on numerous entities to get their jobs done first, he said.
Buildots’ technology is addressing this gap using AI algorithms to automatically validate images captured by hardhat-mounted 360-degree cameras, detecting immediately any gaps between the original design, scheduling and what is actually happening on the construction site. Project managers can then make better decisions to speed up construction.
“It even finds events where contractors are installing out of place and streamline payments so that information is transparent and clear,” Danon said. “Buildots also creates a collaborative environment and trust by having a single source telling everyone what is going on. There is no more blaming or cutting corners because the system validates that and also makes construction a healthier industry to work in.”
Buildots went after new funding once it was able to show product market fit and was expanding into other countries. The platform is being utilized on major building projects in countries like the U.S., U.K., Germany, Switzerland, Scandinavia and China. To meet demand, Buildots will use the new funding to continue that expansion; double the size of its global team with a focus on sales, marketing and R&D; and grow on the business side. Danon’s aim is “to get to the point where we are the standard for every construction site.” The company is also looking at areas outside construction where its technology would be applicable.
Tal Morgenstern, partner at Lightspeed Venture Partners, said he keeps an eye on graduates of the Israel Defense Forces, where the three Buildots founders came from. However, in the case of this company, Lightspeed actually passed on both the seed and Series A.
Morgenstern admits the decision was a mistake, but at the time, he thought the technology Buildots was trying to build “first, impossible and second, I knew construction was difficult to sell into.” He felt that Buildots, with such a premium product, would have a challenge selling to a low-margin industry that was late to adopt technology in general.
By the time the Series B came round, he said Buildots had solved both of those issues, proving that it works, but also that customers were adopting the technology without much sales and marketing. In addition, other solutions in construction tech were still relying on lasers or people to manually input or tap photos.
“Buildots is seamlessly capturing images and providing a level of insights that is so high, and that is why the company is able to command the price structure they have and are receiving interesting commercial results,” Morgenstern said.
Walking around today’s construction site, Danon said the adoption of technology is enabling Buildots to move quickly to build processes for the industry.
As such, the company saw more than 50% growth quarter over quarter over the past year in three of the countries in which it operates. It is now working with four of the top 10 construction companies in Europe and around the world.
“We did a good job selling remotely, but now we need local offices,” Danon added. “We are also sitting on piles of data from construction sites. We learn from one project to another and want to look for the challenges where data will help make a financial impact. It’s a natural next step for the company.”
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Product-led revenue startup Correlated launches with $8.3M seed
Correlated on Wednesday announced it raised $8.3 million in seed funding to launch its product-led growth platform for sales teams.
NextView Ventures and Harrison Metal co-led the round and were joined by Apollo Projects, Attentive co-founders Brian Long and Andrew Jones, Cockroach Labs co-founder Ben Darnell and Atrium’s Pete Kazanjy. The round includes funding raised last year and more recent follow-on funding from both NextView and Harrison, co-founder and CEO Tim Geisenheimer told TechCrunch.
The New York-based company was founded in 2020 by Geisenheimer and Diana Hsieh, who overlapped at TimescaleDB, and John Pena, who Geisenheimer met at Facet. In their previous roles, they saw a need to connect product data to sales tools.
While at Timescale, Geisenheimer said there were thousands of free users to talk to, and he and Hsieh built a similar version of a product-led growth platform there, but secretly wished there was something more like Correlated available.
What they saw was data across multiple tools being stored manually on spreadsheets so that actionable insights could be generated. The data would quickly become outdated. Add in that the way customers use products now is different. Traditionally, customers would not be able to use a product until they talked to the sales team. Today, customers start using products for free and either get value from it or not, but sales teams don’t have real-time data on their experience.
“Sales needs to know how customers are using the product and the right time for sales to engage based on maturity of the experience,” Geisenheimer said. “That was the missing piece of it and sales teams ended up talking to the wrong people. With Correlated, they can close more deals efficiently.”
Correlated’s technology pulls in product usage data from tools and data warehouses and connects to a management platform like Salesforce or HubSpot, stitching it together into a data graph to show how customers are using a product. For example, within a company of 200 to 500 employees, a salesperson can see the frequency employees logged in and be alerted of when the best opportunity is to make the sale.
The company has a SaaS pricing model and is already working with mid-market companies like Ally, Pulumi, ReadMe and LaunchNotes. To support its launch out of beta, Geisenheimer intends to use the new funding for hiring across functions like engineering and go-to-market. The company has 11 employees currently.
There are other product-led growth platforms out there that raised venture capital funding recently, for example, Endgame, and similarly Geisenheimer said the competition is often in-house product teams building their own systems. Correlated’s differentiator is that it has taken on that task itself and enables customers to quickly see value once they are up-and-running, he added.
David Beisel, co-founder and partner at NextView Ventures, said his firm invests in category stage companies and is currently operating out of its fourth fund, infusing business-to-business SaaS and e-commerce companies. Beisel has known Geisenheimer for nearly a decade now, having met him when NextView invested in one of Geisenheimer’s previous companies, TapCommerce.
“At the end of the day with Tim, he knows sales and the company is selling a product that has a strong founder market fit,” Beisel said. “We are moving toward a world where end-user adoption of software — not the initial engagement — is growing over time. Instead, Correlated empowers that initial sale and account expansion and that will align with where the industry is going.”
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Can your startup support a research-based workflow?
The President’s Council of Advisors on Science and Technology predicts that U.S. companies will spend upward of $100 billion on AI R&D per year by 2025. Much of this spending today is done by six tech companies — Microsoft, Google, Amazon, IBM, Facebook and Apple, according to a recent study from CSET at Georgetown University. But what if you’re a startup whose product relies on AI at its core?
Can early-stage companies support a research-based workflow? At a startup or scaleup, the focus is often more on concrete product development than research. For obvious reasons, companies want to make things that matter to their customers, investors and stakeholders. Ideally, there’s a way to do both.
Before investing in staffing an AI research lab, consider this advice to determine whether you’re ready to get started.
Compile the right research team
Assuming it’s your organization’s priority to do innovative AI research, the first step is to hire one or two researchers. At Unbabel, we did this early by hiring Ph.D.s and getting started quickly with research for a product that hadn’t been developed yet. Some researchers will build from scratch and others will take your data and try to find a pre-existing model that fits your needs.
While Google’s X division may have the capital to focus on moonshots, most startups can only invest in innovation that provides them a competitive advantage or improves their product.
From there, you’ll need to hire research engineers or machine learning operations professionals. Research is only a small part of using AI in production. Research engineers will then release your research into production, monitor your model’s results and refine the model if it stops predicting well (or otherwise is not operating as planned). Often they’ll use automation to simplify monitoring and deployment procedures as opposed to doing everything manually.
None of this falls within the scope of a research scientist — they’re most used to working with the data sets and models in training. That said, researchers and engineers will need to work together in a continuous feedback loop to refine and retrain models based on actual performance in inference.
Choose the problems you want to solve
The CSET research cited above shows that 85% of AI labs in North America and Europe do some form of basic AI research, and less than 15% focus on development. The rest of the world is different: A majority of labs in other countries, such as India and Israel, focus on development.
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Tech leaders can be the secret weapon for supercharging ESG goals
Environmental, social and governance (ESG) factors should be key considerations for CTOs and technology leaders scaling next generation companies from day one. Investors are increasingly prioritizing startups that focus on ESG, with the growth of sustainable investing skyrocketing.
What’s driving this shift in mentality across every industry? It’s simple: Consumers are no longer willing to support companies that don’t prioritize sustainability. According to a survey conducted by IBM, the COVID-19 pandemic has elevated consumers’ focus on sustainability and their willingness to pay out of their own pockets for a sustainable future. In tandem, federal action on climate change is increasing, with the U.S. rejoining the Paris Climate Agreement and a recent executive order on climate commitments.
Over the past few years, we have seen an uptick in organizations setting long-term sustainability goals. However, CEOs and chief sustainability officers typically forecast these goals, and they are often long term and aspirational — leaving the near and midterm implementation of ESG programs to operations and technology teams.
Until recently, choosing cloud regions meant considering factors like cost and latency to end users. But carbon is another factor worth considering.
CTOs are a crucial part of the planning process, and in fact, can be the secret weapon to help their organization supercharge their ESG targets. Below are a few immediate steps that CTOs and technology leaders can take to achieve sustainability and make an ethical impact.
Reducing environmental impact
As more businesses digitize and more consumers use devices and cloud services, the energy needed by data centers continues to rise. In fact, data centers account for an estimated 1% of worldwide electricity usage. However, a forecast from IDC shows that the continued adoption of cloud computing could prevent the emission of more than 1 billion metric tons of carbon dioxide from 2021 through 2024.
Make compute workloads more efficient: First, it’s important to understand the links between computing, power consumption and greenhouse gas emissions from fossil fuels. Making your app and compute workloads more efficient will reduce costs and energy requirements, thus reducing the carbon footprint of those workloads. In the cloud, tools like compute instance auto scaling and sizing recommendations make sure you’re not running too many or overprovisioned cloud VMs based on demand. You can also move to serverless computing, which does much of this scaling work automatically.
Deploy compute workloads in regions with lower carbon intensity: Until recently, choosing cloud regions meant considering factors like cost and latency to end users. But carbon is another factor worth considering. While the compute capabilities of regions are similar, their carbon intensities typically vary. Some regions have access to more carbon-free energy production than others, and consequently the carbon intensity for each region is different.
So, choosing a cloud region with lower carbon intensity is often the simplest and most impactful step you can take. Alistair Scott, co-founder and CTO of cloud infrastructure startup Infracost, underscores this sentiment: “Engineers want to do the right thing and reduce waste, and I think cloud providers can help with that. The key is to provide information in workflow, so the people who are responsible for infraprovisioning can weigh the CO2 impact versus other factors such as cost and data residency before they deploy.”
Another step is to estimate your specific workload’s carbon footprint using open-source software like Cloud Carbon Footprint, a project sponsored by ThoughtWorks. Etsy has open-sourced a similar tool called Cloud Jewels that estimates energy consumption based on cloud usage information. This is helping them track progress toward their target of reducing their energy intensity by 25% by 2025.
Make social impact
Beyond reducing environmental impact, CTOs and technology leaders can have significant, direct and meaningful social impact.
Include societal benefits in the design of your products: As a CTO or technology founder, you can help ensure that societal benefits are prioritized in your product roadmaps. For example, if you’re a fintech CTO, you can add product features to expand access to credit in underserved populations. Startups like LoanWell are on a mission to increase access to capital for those typically left out of the financial system and make the loan origination process more efficient and equitable.
When thinking about product design, a product needs to be as useful and effective as it is sustainable. By thinking about sustainability and societal impact as a core element of product innovation, there is an opportunity to differentiate yourself in socially beneficial ways. For example, Lush has been a pioneer of package-free solutions, and launched Lush Lens — a virtual package app leveraging cameras on mobile phones and AI to overlay product information. The company hit 2 million scans in its efforts to tackle the beauty industry’s excessive use of (plastic) packaging.
Responsible AI practices should be ingrained in the culture to avoid social harms: Machine learning and artificial intelligence have become central to the advanced, personalized digital experiences everyone is accustomed to — from product and content recommendations to spam filtering, trend forecasting and other “smart” behaviors.
It is therefore critical to incorporate responsible AI practices, so benefits from AI and ML can be realized by your entire user base and that inadvertent harm can be avoided. Start by establishing clear principles for working with AI responsibly, and translate those principles into processes and procedures. Think about AI responsibility reviews the same way you think about code reviews, automated testing and UX design. As a technical leader or founder, you get to establish what the process is.
Impact governance
Promoting governance does not stop with the board and CEO; CTOs play an important role, too.
Create a diverse and inclusive technology team: Compared to individual decision-makers, diverse teams make better decisions 87% of the time. Additionally, Gartner research found that in a diverse workforce, performance improves by 12% and intent to stay by 20%.
It is important to reinforce and demonstrate why diversity, equity and inclusion is important within a technology team. One way you can do this is by using data to inform your DEI efforts. You can establish a voluntary internal program to collect demographics, including gender, race and ethnicity, and this data will provide a baseline for identifying diversity gaps and measuring improvements. Consider going further by baking these improvements into your employee performance process, such as objectives and key results (OKRs). Make everyone accountable from the start, not just HR.
These are just a few of the ways CTOs and technology leaders can contribute to ESG progress in their companies. The first step, however, is to recognize the many ways you as a technology leader can make an impact from day one.
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Mixlab raises $20M to provide purrfect pharmacy experience for pet parents
Pet pharmacy Mixlab has developed a digital platform enabling veterinarians to prescribe medications and have them delivered — sometimes on the same day — to pet parents.
The New York-based company raised a $20 million Series A in a round of funding led by Sonoma Brands and including Global Founders Capital, Monogram Capital, Lakehouse Ventures and Brand Foundry. The new investment gives Mixlab total funding of $30 million, said Fred Dijols, co-founder and CEO of Mixlab.
Dijols and Stella Kim, chief experience officer, co-founded Mixlab in 2017 to provide a better pharmacy experience, with the veterinarian at the center.
Dijols’ background is in medical devices as well as healthcare investment banking, where he became interested in the pharmacy industry, following TruePill and PillPack, which he told TechCrunch were “creating a modern pharmacy model.”
As more pharmacy experiences revolved around at-home delivery, he found the veterinary side of pharmacy was not keeping up. He met Kim, a user experience expert, whose family owns a pharmacy, and wanted to bring technology into the industry.
“The pharmacy industry is changing a lot, and technology allows us to personalize the care and experience for the veterinarian, pet parent and the pet,” Kim said. “Customer service is important in healthcare as is dignity and empathy. We kept that in mind when starting Mixlab. Many companies use technology to remove the human element, but we use it to elevate it.”
Mixlab’s technology includes a digital service for veterinarians to streamline their daily medication workflow and gives them back time to spend with patient care. The platform manages the home delivery of medications across branded, generic and over-the-counter medications, as well as reduces a clinic’s on-site pharmacy inventories. Veterinarians can write prescriptions in seconds and track medication progress and therapy compliance.
The company also operates its own compound pharmacy where it specializes in making medications on-demand that are flavored and dosed.
On the pet parent side, they no longer have to wait up to a week for medications nor have to drive over to the clinic to pick them up. Medications come in a personalized care package that includes a note from the pharmacist, clear and easy-to-read instructions and a new toy.
Over the past year, adoptions of pets spiked as more people were at home, also leading to an increase in vet visits. This also caused the global pet care industry to boom, and it is now projected to reach $343 billion by 2030, when it had been valued at $208 billion in 2020.
Pet parents are also spending more on their pets, and a Morgan Stanley report showed that they see pets as part of their family, and as a result, 37% of people said they would take on debt to pay for a pet’s medical expenses, while 29% would put a pet’s needs before their own.
To meet the increased demand in veterinary care, the company will use the new funding to improve its technology and expand into more locations where it can provide same-day delivery. Currently it is shipping to 47 states and Dijols expects to be completely national by the end of the year. He also expects to hire more people on both the sales team and in executive leadership positions.
The company is already operating in New York and Los Angeles and growing 3x year over year, though Dijols admits operating during the pandemic was a bit challenging due to “a massive surge of orders” that came in as veterinarians had to shut down their offices.
As part of the investment, Keith Levy, operating partner at Sonoma Brands and former president of pet food manufacturer Royal Canin USA, will join Mixlab’s board of directors. Sonoma Brands is focused on growth sectors of the consumer economy, and pets was one of the areas that investors were interested in.
Over time, Sonoma found that within the veterinary community, there was space for a lot of players. However, veterinarians want to home in on one company they trust, and Mixlab fit that description for many because they were getting medication out faster, Levy said.
“What Mixlab is doing isn’t completely unique, but they are doing it better,” he added. “When we looked at their customer service metrics, we saw they had a good reputation and were relentlessly focused on providing a better experience.”
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ConverseNow is targeting restaurant drive-thrus with new $15M round
One year after voice-based AI technology company ConverseNow raised a $3.3 million seed round, the company is back with a cash infusion of $15 million in Series A funding in a round led by Craft Ventures.
The Austin-based company’s AI voice ordering assistants George and Becky work inside quick-serve restaurants to take orders via phone, chat, drive-thru and self-service kiosks, freeing up staff to concentrate on food preparation and customer service.
Joining Craft in the Series A round were LiveOak Venture Partners, Tensility Venture Partners, Knoll Ventures, Bala Investments, 2048 Ventures, Bridge Investments, Moneta Ventures and angel investors Federico Castellucci and Ashish Gupta. This new investment brings ConverseNow’s total funding to $18.3 million, Vinay Shukla, co-founder and CEO of ConverseNow, told TechCrunch.
As part of the investment, Bryan Rosenblatt, partner at Craft Ventures, is joining the company’s board of directors, and said in a written statement that “post-pandemic, quick-service restaurants are primed for digital transformation, and we see a unique opportunity for ConverseNow to become a driving force in the space.”
At the time when ConverseNow raised its seed funding in 2020, it was piloting its technology in just a handful of stores. Today, it is live in over 750 stores and grew seven times in revenue and five times in headcount.
Restaurants were some of the hardest-hit industries during the pandemic, and as they reopen, Shukla said their two main problems will be labor and supply chain, and “that is where our technology intersects.”
The AI assistants are able to step in during peak times when workers are busy to help take orders so that customers are not waiting to place their orders, or calls get dropped or abandoned, something Shukla said happens often.
It can also drive more business. ConverseNow said it is shown to increase average orders by 23% and revenue by 20%, while adding up to 12 hours of extra deployable labor time per store per week.
Company co-founder Rahul Aggarwal said more people prefer to order remotely, which has led to an increase in volume. However, the more workers have to multitask, the less focus they have on any one job.
“If you step into restaurants with ConverseNow, you see them reimagined,” Aggarwal said. “You find workers focusing on the job they like to do, which is preparing food. It is also driving better work balance, while on the customer side, you don’t have to wait in the queue. Operators have more time to churn orders, and service time comes down.”
ConverseNow is one of the startups within the global restaurant management software market that is forecasted to reach $6.94 billion by 2025, according to Grand View Research. Over the past year, startups in the space attracted both investors and acquirers. For example, point-of-sale software company Lightspeed acquired Upserve in December for $430 million. Earlier this year, Sunday raised $24 million for its checkout technology.
The new funding will enable ConverseNow to continue developing its line-busting technology and invest in marketing, sales and product innovation. It will also be working on building a database from every conversation and onboarding new customers quicker, which involves inputting the initial menu.
By leveraging artificial intelligence, the company will be able to course-correct any inconsistencies, like background noise on a call, and better predict what a customer might be saying. It will also correct missing words and translate the order better. In the future, Shukla and Aggarwal also want the platform to be able to tell what is going on around the restaurant — what traffic is like, the weather and any menu promotions to drive upsell.
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Financial firms should leverage machine learning to make anomaly detection easier
Anomaly detection is one of the more difficult and underserved operational areas in the asset-servicing sector of financial institutions. Broadly speaking, a true anomaly is one that deviates from the norm of the expected or the familiar. Anomalies can be the result of incompetence, maliciousness, system errors, accidents or the product of shifts in the underlying structure of day-to-day processes.
For the financial services industry, detecting anomalies is critical, as they may be indicative of illegal activities such as fraud, identity theft, network intrusion, account takeover or money laundering, which may result in undesired outcomes for both the institution and the individual.
There are different ways to address the challenge of anomaly detection, including supervised and unsupervised learning.
Detecting outlier data, or anomalies according to historic data patterns and trends can enrich a financial institution’s operational team by increasing their understanding and preparedness.
The challenge of detecting anomalies
Anomaly detection presents a unique challenge for a variety of reasons. First and foremost, the financial services industry has seen an increase in the volume and complexity of data in recent years. In addition, a large emphasis has been placed on the quality of data, turning it into a way to measure the health of an institution.
To make matters more complicated, anomaly detection requires the prediction of something that has not been seen before or prepared for. The increase in data and the fact that it is constantly changing exacerbates the challenge further.
Leveraging machine learning
There are different ways to address the challenge of anomaly detection, including supervised and unsupervised learning.
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