Exclusive: Kilimo helps farmers save water and get paid for it

Farmers walk through a field being irrigated.

Image Credits: ridvan_celik / Getty Images

When people think about the water they use, they tend to think about drinking water out of the tap or maybe their daily shower. But about 70% of the water we use goes toward growing the crops that feed us, a number that swells to as high as 90% in low-income countries. Finding water for other uses can be a tough row to hoe.

In many areas, though, farmers are incentivized to use as much water as they think they need, even in excess to ensure a successful crop. “Governments want to produce their own food. They don’t want water to be expensive,” Jairo Trad, co-founder and CEO of Kilimo, told TechCrunch.

“But if farmers under-irrigate, there’s a huge risk of losing production and losing money and losing more food,” he added. “There’s an imbalance in the risk.”

Cheap irrigation has transformed many regions around the world into breadbaskets, but it also means that there can be little left for other uses.

For companies, water shortages can be an existential threat. “If you have a million $200 million bottling plant and you don’t have water next week, there’s a lot of money at risk,” Trad said. “So we started talking with people and trying to put a value on water.”

What Trad and his colleagues at Kilimo devised can best be thought of as a risk management tool. So far, the company has taken around 100,000 soil samples across 45 different crop types in a number of different countries, mostly in South America. From there, it uses those samples to connect soil moisture to satellite imagery of farm fields, which is far easier to acquire. 

“You have to sit close to the ground to understand how things behave in that specific soil in that specific country,” Trad said.

Kilimo can then remotely monitor farm fields and advise farmers on their water use. It charges farmers a fee for the service, and if they’re able to successfully cut their water use, Kilimo can sell the surplus water to a company that needs it in the same watershed, sharing a portion of the proceeds with the farmer. In the end, farmers that trim their water use end up netting 20% to 40% more than they paid Kilimo. Everything is verified by third parties following the Volumetric Water Benefit Accounting standard.

Though the startup has been around for about a decade, it’s working to expand its operations as water scarcity rises to the top of executives’ lists of concerns. It currently works throughout South America, including in Argentina, where it is based, and Mexico. Next up is the Southwestern United States and Europe. To support the growth, Kilimo recently raised a $7.5 million Series A, the company exclusively shared with TechCrunch. The round was led by Emerald Technology Ventures with participation from iThink VC, Kamay Ventures, Salkantay Ventures and The Yield Lab Latam.

Kilimo is working with Microsoft, Intel and Coca-Cola, all of which have announced water pledges. (Data centers are large water consumers, as are beverages.) Trad hopes to sign more. “Each company alone is not going to make a difference. But if you can leverage corporations plus government plus development bank entities, there you start making a difference,” he said.

TechCrunch Early Stage in Boston, MA on April 25, 2024

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African American young developer in eyeglasses concentrating on his online work on computer sitting at workplace

p0 uses LLMs to save enterprises from code catastrophes

African American young developer in eyeglasses concentrating on his online work on computer sitting at workplace

Image Credits: AnnaStills (opens in a new window) / Getty Images

Startup p0 is named after catastrophic events that can cause a platform to crash, leading to potential security breaches and loss of customer trust in businesses. Those are the problems that p0 was created to solve, using large language models (LLMs) to help developers catch serious issues in code before it is shipped. The startup announced today it has raised $6.5 million from Lightspeed Venture Partners, with participation from Alchemy Ventures.

p0 uses LLMs to identify safety and security issues in software before it is run in a production environment, and it does not need user configuration. Software issues it addresses include data integrity, validation failures, speed and timeouts. Developers use it by connecting their Git code repositories to p0. One of p0’s main customers is a large food service company with millions of live users on its system. They use p0 to find issues that can compromise the security and reliability of their platform. For example, p0 showed them that their sign-up sheets could’t handle emojis.

The startup was founded in 2022 by Prakash Sanker, who previously worked at companies like Palantir, and Kunal Agarwal, a founder of SoftBank-funded working capital startup C2FO. The company was started to “fundamentally change the manner in which code quality assurance is done,” Sanker told TechCrunch.

“While building software at our previous companies, we always felt that getting something into production was painful, typically involving a really boring and time-consuming bug bash process,” he says. “Our developers were always balancing the demands of shipping product or spending time writing tests.”

Sanker and Agarwal decided to build a one-click tool that could identify p0s before they affect customers, while shortening software delivery cycles. Sanker says the quality assurance tools currently used by developers, which typically focus on static analysis, security analysis, test writing or test execution, are less precise and require a lot of engagement and ingenuity to discover p0s.

p0’s founders say it is able to be part of the development process without slowing it down because it revolves around LLMs.

Agarwal explains that enterprises traditionally do security testing with a black box approach, which means external white hat hackers or security systems try to attack their systems without a deep knowledge of the system. Or internal developers who are very familiar with the system try to attack it. “Typically, it’s been very hard to know the internals of systems just by looking at code externally,” he said.

p0 uses LLMs to understand its customer’s codebases and create contextual challenges that have the potential to exploit vulnerabilities. For example, it can detect an API vulnerability that might give away private information when hit with a specific data payload.

“Without LLMs, it would be impossible to create a contextually relevant challenge,” Agarwal said. “This is critical because understanding context powers the system with intelligence and mounting a relevant challenge enables us to reduce noise.”

The company’s engine is currently powered by open source LLMs, including Llama and Mistral. p0 extracts the relevant parts of a customer’s codebase and embeds it with the right context and query for its LLM engine to respond to, Agarwal explained. Then it examines those responses and makes them readable by humans. As p0 develops, it plans to refine its model weights. For enterprise customers, LLMs are hosted within their environment for information security reasons.

Agarwal says hallucinations aren’t a challenge for the startup, because it doesn’t write code. Instead, it mounts challenges and it can detect challenges created by hallucinations.

p0 has launched from stealth and is revenue generating thanks to its first customer (the global food service provider). Sanker says it has 50 customers in its pipeline who will be onboarded in 2024 and monetizes through a SaaS model. In the future, it wants to include staging environments as an offering.

Other plans include expanding p0’s capability for finding different types of critical issues and supporting more languages. The founders also want to get rid of the need for a customer-hosted staging environment and turn p0 into an end-to-end solution.

In an investor statement, Lightspeed partner Hemant Mohapatra said, “p0’s cutting-edge approach to code and API security is unique and amongst the first ever truly LLM-native ways of solving this age-old and ever-evolving problem. We are excited to have incubated and backed them from when this was just an idea on paper.”

Threads tests the ability to save posts as it continues to compete with X

Image Credits: Instagram Threads

Instagram Threads is getting a highly requested feature that puts it in closer competition with X/Twitter. The text-based social networking app is experimenting with the ability to save posts, allowing users to bookmark favorite posts to revisit them later.

Instagram head Adam Mosseri announced the feature in a Threads post on Wednesday, noting that the company just started the limited test.

Users with the test can find the new save feature under the three-dot menu in the top-right corner of a post. We’re not sure why Threads decided to hide the feature in the “More Options” menu since Instagram has its bookmarks icon easily accessible next to the like, comment, and share buttons. X recently moved its bookmark button on iOS to make it easier to find. Threads will likely continue testing the most optimal location for its new save feature.

Instagram has had a bookmarking capability since 2016, so it makes sense that Threads would also adopt the feature. The ability to save content for later is helpful when you want to look at a post — especially if it includes a link to a longer article — but don’t have time to read it now.

Since the debut of Threads last year, the app has continuously rolled out new features to appeal to users and take on rivals like X, Bluesky, Mastodon, Nostr, Post and Spill. Last month, the platform confirmed it was working on a “Trends” feature to surface trending topics.

During Meta’s fourth-quarter earnings report, Threads revealed that it has over 130 million monthly active users.

Threads now reaches more than 130 million monthly users, says Meta, up 30M from Q3

AI investor survey

AI is going to save software companies' dreams of growth

AI investor survey

Image Credits: Bryce Durbin / TechCrunch

It appears emerging price points for AI-powered software products will boost the total addressable market (TAM) for technology products and help reaccelerate growth at tech companies big and small.

In late 2023, Battery Ventures noted that the pullback of revenue growth at software startups had reached its nadir, and growth levels were starting to stabilize in the fourth quarter. Around the same time, Scale Venture Partners reported that after several years of deceleration, early-stage software companies were expected to renew momentum in 2024. Taken together, it seemed that tech companies were all but out of the woods.

Today, we’re seeing early indications that those optimistic takes were in tune with how 2024 would at least start to unfold. Companies are reporting their Q4 2023 results, and Big Tech companies have posted better-than-expected revenue and profit so far. Microsoft did well, Meta blew the doors off, and Amazon had a great quarter as well. We’re still waiting on a host of smaller SaaS companies to report, but it does appear that 2023 ended on a better note than earlier in the year.

Building a viable pricing model for generative AI features could be challenging

There’s good reason to expect more of the same in 2024. It appears that the market is willing to accept that software imbued with new AI capabilities will cost more. So, yes, software companies of all sizes will have something new to upsell existing customers and potentially land new accounts, and it means that the TAM of software companies is widening.

A business can grow faster for longer in a larger market than it can in a smaller market. AI is therefore serving as a near-term growth boost for tech companies while raising the ceiling for how big they can become over time.

The price of AI

Charging for AI software products is not merely a way to squeeze customers; it’s also a great way to defend margins. AI is not cheap, and recently we’ve seen people once again debating whether AI startups have worse gross margins than traditional SaaS businesses.

If you are going to run lots of LLM cycles, you have to charge for it. Otherwise, you are going to take large bales of cash and set them on fire. So it should not be entirely surprising that we’re seeing AI services coming at a stiff price point. Microsoft’s Copilot Pro will run you $20 per month, as will Google’s new level of consumer service that includes its latest AI model. GitHub Copilot costs $10 per month, minimum. Box is charging for AI as well, to put an enterprise spin on the point. The list goes on.

The cost of enterprise-level access to AI services can be partially graded on the success of Microsoft 365’s AI tools. They seem to be doing well. And OpenAI’s revenue growth is a useful proxy for demand for its services, given how popular ChatGPT is. Both seem to indicate that we’re seeing real demand for AI-powered software, which, in turn, should convert to additive growth for tech companies.

Looping back to where we started: This dynamic is why I expect software growth to be solid this year.

There’s another way to consider the impact of AI-related software services on tech companies’ top lines. Observe the following chart from Altimeter investor Jamin Ball:

Image Credits: Clouded Judgement.

This chart parses how much growth AI is driving for Microsoft’s cloud platform. If we presume that all hyperscale cloud infrastructure providers are seeing similar boosts to compute demand, we can infer that lots of tech companies are ramping up their usage of AI models inside their products. As no company wants to see their gross margins erode as they grow, the above implied spend that Ball charted must be converted into higher prices. That means growth.

It seems that the market isn’t hesitating to pay for AI-related software services. And given that demand for AI-related tooling is high, the combination of the two factors should not only boost tech growth today, but also the total tech market itself over time. How about that for a good foot to start the year on?

Rad AI founders

Rad AI, a startup that helps radiologists save time on report generation, raises $50M Series B from Khosla Ventures

Rad AI founders

Image Credits: Rad AI

In 2017, Vinod Khosla told CNBC that the job “of the radiologist will be obsolete in five years.” While the founder of Khosla Ventures later revised that timeline to as long as 15 years, he maintained that AI image recognition could soon diagnose disease on scans better than human doctors. 

Seven years later, radiologists are still required to interpret most scans (even if AI software helps them); the more immediate challenge is the shortage of these doctors in the United States and around the world. 

While Khosla Ventures has backed several imaging startups, including Vista.ai and Q Bio, the firm’s latest bet is on a company that makes radiologists’ workload easier by reducing the time spent on report documentation, instead of trying to replace the physician with a machine.

On Tuesday, Khosla led a $50 million Series B into Rad AI, which developed a tool that can generate reports for radiologists. Other participants in the round included World Innovation Lab and returning investors ARTIS Ventures, OCV Partners, Kickstart Fund and Gradient Ventures (Google’s AI-focused fund). The financing brought the company’s total capital raised to over $80 million.

Rad AI was founded in 2018 by Dr. Jeff Chang, who completed his medical training as a radiologist when he was 16 and later received an MBA from UCLA, and serial entrepreneur Doktor Gurson.

Since Chang knew from his own experience as a practicing doctor that the majority of radiologists’ time is spent documenting findings rather than analyzing images, the pair decided to develop a proprietary LLM trained on radiology report datasets for automating doctors’ findings and impressions documentation. 

While tech companies didn’t widely use generative AI until OpenAI’s ChatGPT burst onto the scene in 2022, Rad AI takes pride in being an early adopter of this technology. “I’m confident we’re the first company in radiology to start using LLMs,” Gurson, Rad AI’s CEO told TechCrunch. “We started doing that work in 2018, around the same time that open AI was creating their [first] models.” 

Six years later, Rad AI’s products are used by about a third of U.S. health systems and nine of the 10 largest radiology groups in the country, Gurson said.

The fresh capital will be used to build a team that deploys Rad AI’s latest product: a standalone radiology reporting solution. 

“We have a lot of interest, but there’s only so much we can deploy at once,” Gurson said, adding that Rad AI is hiring people who can install and maintain the software.

Some incumbents have been trying to add GenAI functionality to their radiology reporting software over the past 18 months, but Rad AI doesn’t consider these companies to be true competitors yet.   

“At this point, probably 99% to 100% of the market uses our products,” he said. “If it’s any indication, we’ve not lost a single customer since we started.”

Spoor uses AI to save birds from wind turbines

spoor, AI, startups, venture capital

Image Credits: Spoor

Wind is the largest source of renewable energy in the U.S., according to the U.S. Energy Information Administration, but wind farms come with an environmental cost as wind turbines can wreak havoc on bird populations. Meet Spoor, the startup using AI to help wind farms mitigate that risk.

Spoor is a software that uses machine learning to detect birds on video while also recording their movement and predicting their flight patterns. Spoor co-founder and CEO Ask Helseth told TechCrunch that government regulations in several countries require wind farms to monitor and track their impacts on birds, especially in areas with endangered species, but prior to AI-enabled computer vision, there wasn’t a good way to do that.

“The expectations from the regulators are growing but the industry doesn’t have a great tool,” Helseth said. “A lot of people [go out] in the field with binoculars and trained dogs to find out how many birds are colliding with the turbines.”

Spoor’s system of continuously monitoring sites offers a large improvement, Helseth said. Existing wind farms can use the data to better react to bird migration patterns and can slow or even stop wind turbines when avian activity is expected to be heightened. Companies can also use the tech to monitor potential sites for wind farms and evaluate their risk to the local avian populations.

“Wind farms are quite huge, many hundred square kilometers, and trying to use computer vision to basically monitor the air is an interesting technology challenge,” Helseth said. “We needed to create a scalable technology that can detect birds. It’s kind of a novel use of computer vision and our own data pipeline.”

The Oslo, Norway-based company just raised a $4 million seed round from investors including Futurum Ventures, Nysnø and biodiversity-focused VC Superorganism. The round also included Ørsted Ventures, the venture arm of Ørsted, one of the world’s largest offshore wind farm companies.

Helseth said that they fielded inbound interest from more than 100 investors for the seed round and were very strategic with who they decided to work with. Superorganism was the only firm they reached out to. Kevin Webb, a co-founder and managing director at Superorganism, said the firm had been tracking Spoor for a while and got excited about the investment because Spoor perfectly fits Superorganism’s thesis of backing companies that help the planet get to zero emissions without harming nature or biodiversity in the process.

“We saw them very early on and in the time that we have known them they have started working with the largest wind farm developers on the planet,” Webb told TechCrunch. “Ask and his team have hired incredibly well. We were frankly blown away by the progress they had made in building out the team.”

Spoor getting its start in Norway was a helpful factor in the company’s progress as Norway has an advanced wind farm program. Plus, Europe has a stronger adoption of wind energy compared to the U.S., Helseth said. But the company has its eye set on expanding into the U.S., which should be a windfall of its own.

The U.S. government has an aggressive goal of reaching 30 gigawatts of offshore wind capacity by 2030, which offers a strong opportunity for companies like Spoor. Any company that wants to set up a wind farm in the U.S. has to comply with guidelines from the U.S. Fish and Wildlife Service and make sure that their wind farms don’t violate legislation like the Endangered Species Act or the Migratory Bird Treaty Act. Regulators are particularly strict in the U.S. about how wind turbines could impact American Bald Eagle populations. Helseth added that he’s seen wind farms get delayed or not built at all because of issues they run into related to native bird populations.

Spoor isn’t the only one using AI machine vision to solve the problem. IdentiFlight is another looking to use AI to tackle the problem.

Still, Helseth hopes Spoor can help break up some of those bottlenecks and be a growing, positive factor in moving the industry forward.

“We are still a small company, per se, but we have interest from around the world, the industry is hungry for our solutions,” Helseth said.

This piece has been updated to better reflect Spoor’s competitors.