aVenture is in Alpha: aVenture recently launched early public access to our research product. It's intended to illustrate capabilities and gather feedback from users. While in Alpha, you should expect the research data to be limited and may not yet meet our exacting standards. We've made the decision to temporarily present this information to showcase the product's potential, but you should not yet rely upon it for your investment decisions.
aVenture is in Alpha: aVenture recently launched early public access to our research product. It's intended to illustrate capabilities and gather feedback from users. While in Alpha, you should expect the research data to be limited and may not yet meet our exacting standards. We've made the decision to temporarily present this information to showcase the product's potential, but you should not yet rely upon it for your investment decisions.
© aVenture Investment Company, 2025. All rights reserved.
44 Tehama St, San Francisco, CA 94105
Privacy Policy
aVenture Investment Company ("aVenture") is an independent venture capital research platform providing detailed analysis and data on startups, venture capital investments, and key industry individuals.
While we strive to provide valuable insights with objectivity and professional diligence, we cannot guarantee the accuracy of the information provided on our platform. Before making any investment decisions, you should verify the accuracy of all pertinent details for your decision.
aVenture does not offer investment advisory services and is not registered as an investment adviser. The data provided by aVenture does not constitute recommendations or advice, whether by methodology or a statement written by a staff member of aVenture.
Links to external websites do not imply endorsement or affiliation with aVenture. References or links to providers offering the ability to invest in a primary or secondary transaction in a company are for convenience purposes only. They are not solicitations or offers to buy or sell an investment. Remember that past performance does not guarantee future results, and venture capital and private assets should be a contributory part of a diversified portfolio.
From TechCrunch
By Julie Bort
April 24, 2024
It is a universal truth of human nature that the developers who build the code should not be the ones to test it. First of all, most of them pretty much detest that task. Second, like any good auditing protocol, those who do the work should not be the ones who verify it.
Not surprisingly, then, code testing in all its forms — usability, language- or task-specific tests, end-to-end testing — has been a focus of a growing cadre of generative AI startups. Every week, TechCrunch covers another one like Antithesis (raised $47 million), CodiumAI (raised $11 million) and QA Wolf (raised $20 million). And new ones are emerging all the time, like new Y Combinator graduate Momentic.
Another is year-old startup Nova AI, an Unusual Academy accelerator grad that’s raised a $1 million pre-seed round. It is attempting to best its competitors with its end-to-end testing tools by breaking many of the Silicon Valley rules of how startups should operate, founder/CEO Zach Smith tells TechCrunch.
Whereas the standard Y Combinator approach is to start small, Nova AI is aiming at mid-size to large enterprises with complex code-bases and a burning need now. Smith declined to name any customers using or testing its product except to describe them as mostly late-stage (Series C or beyond) venture-backed startups in e-commerce, fintech or consumer products, and “heavy user experiences. Downtime for these features is costly.”
Nova AI’s tech sifts through its customers’ code to build tests automatically using GenAI. It is particularly geared toward continuous integration and continuous delivery/deployment (CI/CD) environments where engineers are constantly shipping bits and pieces into their production code.
The idea for Nova AI came from the experiences Smith and his co-founder Jeffrey Shih had when they were engineers working for big tech companies. Smith is a former Googler who worked on cloud-related teams that helped customers use a lot of automation technology. Shih previously worked at Meta (also at Unity and Microsoft before that) with a rare AI specialty involving synthetic data. They’ve since added a third co-founder, AI data scientist Henry Li.
Another rule Nova AI is not following: While boatloads of AI startups are building on top of OpenAI’s industry-leading GPT, Nova AI is using OpenAI’s Chat GPT-4 as little as possible. No customer data is being fed to OpenAI.
While OpenAI promises that the data of those on a paid business plan is not being used to train its models, enterprises still do not trust OpenAI, Smith tells us. “When we’re talking to large enterprises, they’re like, ‘We don’t want our data going into OpenAI,” Smith said.
The engineering teams of large companies are not the only ones that feel this way. OpenAI is fending off a number of lawsuits from those who don’t want it to use their work for model training, or believe their work wound up, unauthorized and unpaid for, in its outputs.
Nova AI is instead heavily relying on open source models like Llama developed by Meta and StarCoder (from the BigCoder community, which was developed by ServiceNow and Hugging Face), as well as building its own models. They aren’t yet using Google’s Gemma with customers, but have tested it and “seen good results,” Smith says.
For instance, he explains that OpenAI offers models for vector embeddings. Vector embeddings translate chunks of text into numbers so the LLM can perform various operations, such as clustering them with other chunks of similar text. Nova AI doesn’t use OpenAI’s embeddings and instead uses open source for this on the customer’s source code. It uses OpenAI tools only to help it generate some code and to do some labeling tasks, and is going through lengths not to send any customer data into OpenAI.
“In this case, instead of using OpenAI’s embedding models, we deploy our own open source embedding models so that when we need to run through every file, we aren’t just sending it to OpenAI,” Smith explained.
While not sending customer data to OpenAI appeases nervous enterprises, open source AI models are also cheaper and more than sufficient for doing targeted specific tasks, Smith has found. In this case, they work well for writing tests.
“The open LLM industry is really proving that they can beat GPT 4 and these big domain providers, when you go really narrow,” he said. “We don’t have to provide some massive model that can tell you what your grandma wants for her birthday. Right? We need to write a test. And that’s it. So our models are fine-tuned specifically for that.”
Open source models are also progressing quickly. For instance, Meta recently introduced a new version of Llama that’s earning accolades in technology circles and that may convince more AI startups to look at OpenAI alternatives.
Share:
xAI’s “Colossus” supercomputer raises health questions in Memphis
Elon Musk’s AI startup xAI plans to continue using 15 gas turbines to power its “Colossus” supercomputer in Memphis, Tennessee, according to an operating permit with the Shelby County Health Department for non-stop turbine use from June 2025 to June 2030. Why does it matter? The Commercial Appeal, a news outlet that obtained the documents, observes that environmental concerns have emerged, as the 20-year-old turbines emit hazardous air pollutants (HAP), including formaldehyde, at levels exceedi
Feb 15, 2025
Perplexity launches its own freemium ‘deep research’ product
Perplexity has become the latest AI company to release an in-depth research tool, with a new feature announced Friday. Google unveiled a similar feature for its Gemini AI platform in December. Then OpenAI launched its own research agent earlier this month. All three companies even have given the feature the same name: Deep Research. The goal is to provide more in-depth answers with real citations for more professional use cases, compared to what you’d get from a consumer chatbot. In a blog post
Feb 15, 2025
Marc Andreessen dreams of making a16z a lasting company, beyond partnerships
Many venture industry observers have wondered whether Andreessen Horowitz, a firm that manages $45 billion, has its sights on eventually becoming a publicly traded company. Co-founder Marc Andreessen said he isn’t “chomping at the bit to take the firm public,” on this week’s Invest Like the Best podcast. But he discussed his goal of building a16z into an enduring company, drawing inspiration from JP Morgan and publicly traded private equity firms. Historically, venture capital firms have been p
Feb 15, 2025
Don't miss our latest news and updates. Subscribe to the newsletter