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AI NewsQodo raises $70M for code verification as AI coding scales

Qodo raises $70M for code verification as AI coding scales

9:31 PM IST · March 30, 2026

Qodo raises $70M for code verification as AI coding scales

As AI coding tools generate billions of lines of code each month, a new bottleneck is emerging: ensuring that software works as intended.Qodo, a startup building AI agents for code review, testing and governance, is betting that verification will define the next phase of software development. The New York-headquartered startup has raised a $70 million Series B round led by Qumra Capital, bringing its total funding to $120 million. Maor Ventures, Phoenix Venture Partners, S Ventures, Square Peg, Susa Ventures, TLV Partners, Vine Ventures, Peter Welender (OpenAI), and Clara Shih (Meta) also joined in the round. Qodo is aiming to serve as a layer focused on improving trust in AI-generated code as enterprises accelerate adoption of tools like OpenClaw and Claude Code. Many are discovering that faster code output doesn’t necessarily translate into reliable or secure software. While most AI review tools focus on what changed, Qodo focuses on how code changes affect entire systems, factoring in organizational standards, historical context, and risk tolerance to help companies better manage AI-generated code more confidently. Itamar Friedman, who previously co-foundedVisualeadand led the machine vision business at Alibaba (which acquired Visualead), founded Qodo in 2022. He told TechCrunch that two key moments in his career — his time at Mellanox, which was later acquired by Nvidia, and building Visualead — inspired him to start Qodo, just months before the launch of ChatGPT. At Mellanox, where he worked on automating hardware verification using machine learning, he realized that “generating systems and verifying systems require very different approaches (different tools, different thinking).” Later, at Alibaba’s Damo Academy, he saw AI evolve toward systems capable of reasoning over human language. By 2021–2022, just ahead of GPT-3.5, it became clear to him that AI would generate a large share of the world’s content—especially code—reinforcing his view that code generation and verification would require fundamentally different systems. A recent survey showsthat while 95% of developers don’t fully trust AI-generated code, only 48% consistently review it before committing, highlighting a gap between awareness and practice. “Code generation companies are largely built around LLMs. But for code quality and governance, LLMs alone aren’t enough,” Friedman said. “Quality is subjective. It depends on organizational standards, past decisions, and tribal knowledge. An LLM can’t fully understand that context. It’s like taking a great engineer from one company and asking them to review code at another — they lack the internal context.” Companies such as OpenAI and Anthropic are helping shape the broader AI narrative, including in adjacent areas like code review, but they are largely focused on building features rather than end-to-end solutions, Friedman explained. Although there are other startups in the space, many remain early stage and have yet to see widespread enterprise adoption, the CEO noted. Qodo is leaning into performance to stand out in a crowded market. The startup recently ranked No. 1 onMartian’s Code Review Bench, scoring 64.3% — more than 10 points ahead of the next competitor and 25 points ahead of Claude Code Review. The benchmark highlights its ability to catch tricky logic bugs and cross-file issues without overwhelming developers with noise. In the past month, it has launched Qodo 2.0, a multi-agent code review system now leading current benchmarks, and introduced tools that learn each organization’s definition of code quality. The company is already working with major enterprises such as NVIDIA, Walmart, Red Hat, Intuit and Texas Instruments, as well as high-growth firms like Monday.com and JFrog. “Every year has had a defining moment — from Copilot to ChatGPT to full task automation,” Friedman said. “Now we’re entering a new phase: moving from stateless AI to stateful systems — from intelligence to ‘artificial wisdom.’ That’s what Qodo is built for.”

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Omen AI’s plan to optimize data centers is all wet

Omen AI’s plan to optimize data centers is all wet

The AI-driven demand for compute power has data centers looking to squeeze more from every rack of GPUs. One consequence? Bacterial outbreaks. The liquid for liquid-cooled chips is a mixture of water and a substance that inhibits bacteria growth. To run the chips hotter, data center managers can change the mix to include more water, which absorbs heat better, but leads to nasty contamination that clogs the flow. To solve that, they flush the system, which can mean shutting down a rack for five or six hours at a potential cost of millions of dollars. Omen AIhas a solution: A tiny spectrometer that can monitor that fluid health in real time, spotting bacterial growth before it becomes a massive problem. “You’re not risking huge amounts of downtime because you have no insight into what’s going on chemically,” explains CEO and founder Zach Laberge. Today, Omen AI said it raised a $31 million Series A round, led by Nava Ventures and including participation from CRV, Vanderbilt University, Mann+Hummel, Starhill Holdings, and Hard Launch Capital, as well as personal investments from executives at Bridgestone, GM, Johnson Controls, and TensorWave. Laberge founded his first company in 2020 when he was 14, raising $3 million to install sensors on construction equipment and ultimately dropping out of high school. (His father and mother, a former Minister of Education for Ontario, were supportive of his plan to carve his own path.) After that startup shut down, Laberge started Omen in 2024, with the idea of focusing on fluid systems as the key to enabling construction machinery to be smart enough to know when it needed to be fixed. The idea was to replace the time-consuming process of extracting samples and sending them to a lab with real-time awareness. Besides bacterial growth, the device can spot pumps and pumps wearing out if it sees copper or chromium, or seals if it sees silicon. Caterpillar dealerships were a key early customer for Omen’s heavy vehicles business, but Cat is also a major supplier of gas-powered turbines and generators to provide on-premises power for data centers. It didn’t take long for Omen to see where the wind was blowing. “That was kind of the transition,” Laberge told TechCrunch. About six months ago, “a lot of the dealerships were saying, ‘Hey, we’re starting to put sensors on our turbines, can you guys do anything on the building side of things?’” Omen discovered that those buildings are full of fluid, from their HVAC systems to their chip cooling. Spotting a new, fast-growing group of potential customers, Omen began to focus on data centers. “It’s rare to see such a young founder who has the respect of established, large corporations in a space that moves a bit more slowly,” said Cory Rellas, a partner at Nava Ventures who sits on Omen’s board. “For Omen in particular, much of our diligence came through our introductions with large customers which quickly validated their approach.” Omen, which has raised $40 million since its founding in 2024, is working with a dozen data center customers as they build out their offering, including TensorWave, a company building an AI compute cloud on AMD chips. “The fluid running through these massive systems is a critical variable that most of the industry is flying blind on,” Piotr Tomasik, TensorWave’s president, said in a statement. “Omen [sees] the future of infrastructure exactly the way we do, better monitoring to optimally support compute customers.” While many organizations rely on mailing fluid samples to labs for insight, Omen isn’t alone in developing on-premises analytics — Pyxis, an established water-monitoring firm, rolled out its data center coolantmonitoring productearlier this month. The key tech advances that unlocked this approach are recent improvements in both optical technologies and signal processing software. “Hardware is just cheap enough that it makes sense to play at scale, and then signal processing lets us make more sense out of the noise,” Laberge said.

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