Jetify, the company formerly known as Jetpack.io, is launching its first AI agent product Tuesday. Dubbed Testpilot, the company’s first AI agent is meant to make the majority of the routine test creation process a matter of simply letting Jetify create a test plan, execute it, and report back, all while the agent directly interacts with the user interface.
Right now, Testpilot works for web-based applications, but the team is also planning to launch mobile testing soon.
The company’s original focus was mostly on creating dev environments in the cloud. While it will still offer those (though maybe with a stronger focus on making those API-driven), Jetify CEO and co-founder Daniel Loreto describes Tuesday’s launch as a bit of a pivot and as the first in a series of AI agent launches, all of which will focus on improving the software development lifecycle.
“We feel, and I’m sure a lot of people feel this way, that AI is truly one of those once-every-30-year technology waves that comes along. It’s like the invention of the Internet or mobile, right? So having the opportunity to be part of that wave and to really participate in it convinced us that, yes, let’s take the risk. Let’s shift,” he told me.
The team decided that the best way to get started with AI agents was to work on something that solved a clear pain point for developers. Nobody likes writing tests — and when it comes to UI testing, existing tools like Selenium can quickly become brittle with even the slightest UI changes. That’s also exactly what AI tools are good at handling: They won’t break, even when a button moves a few pixels to the left.
Loreto also noted that QA teams are already used to using third-party tools for testing their applications, making Testpilot an easier sell. “It’s a well-defined motion that companies say, ‘Yeah, we need this end-to-end testing, and we’re willing to hire a third party to help us with that,’” he said.
He also stressed that this kind of end-to-end testing is easy to integrate into the development process since all the company needs from the developer is access to log in to the application that the service is meant to test. Since Testpilot looks at the actual application, not the underlying code, it doesn’t need access to GitHub or any other code repository, for example.
However, even though Testpilot today can cover 60% to 70% of tests that humans would likely come up with, there is still a gap. That’s something Jetify acknowledges, but as Loreto noted, he thinks that this is still “a huge value-add.”
As of now, potential users still have to go through an onboarding flow with Jetify. Soon, the team hopes to launch a self-service version as well (with mobile support following after that). Jetify is still experimenting with how best to price the service, which is, in part, why the company hasn’t launched a self-service version yet.
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Atomicwork, a SaaS startup led by Indian founders, has raised $25 million in a fresh investment round backed by Khosla Ventures, as it plans to scale and deploy AI agents to simplify communication and service delivery between employees and their enterprises.
Amid growing digital adoption, enterprises struggle to natively provide quick support and easy access to information for their employees. The IT service management market offers tools to solve this problem to some extent. However, deploying these solutions requires time and specialized resources. The emergence of AI has brought some relief. Yet, enabling a chatbot-like experience often demands an incumbent enterprise system behind the scenes. It could be ServiceNow, BMC Remedy, or Jira Service Management.
Atomicwork replaces the incumbent’s presence with a modern AI-driven experience, enabling enterprises to offer automated service workflows. Employees can use these workflows to find work-related answers or access services from departments including HR, IT, or finance through integrations with apps such as Microsoft Teams, Slack, Intune, Okta, Notion, Salesforce, and GitHub, among others.
Vijay Rayapati, co-founder and CEO, defines Atomicwork as a “full-stack AI ServiceNow,” targeting global companies with around $1 billion in revenue and at least 1,000 people.
“The difference [between ServiceNow and Atomicwork] is architectural, what was built 25-30 years ago, versus how we build today,” Rayapati said in an exclusive interview.
Unlike deploying ServiceNow or any of its competitive IT service management platforms, which involves multi-year implementation cycles, Atomicwork’s platform could be implemented in no more than a couple of weeks, according to him. The startup also provides a universal agent to assist enterprises in setting up automated workflows.
Founded in 2022 by Rayapati, who previously founded the cloud management platform Minjar, which was acquired by Nutanix in 2018, along with Kiran Darisi and Parsuram Vijayasankar, both part of Freshworks’ founding team, the startup initially started with an AI assistant to automate enterprise workflows.
As the automation demand has grown and AI capabilities have advanced, Atomicwork introduced its agentic service management platform late last year, which brings context-aware AI agents that analyze multiple data sources to perform tasks across enterprise apps, such as resetting their work email password, relaying questions in a prospect sales call, or accessing their design catalog on Figma using Microsoft Teams or Slack.
ServiceNow — and others in this domain — also allows enterprises to develop and deploy AI agents based on their requirements. Nonetheless, Rayapati told TechCrunch that the startup, with a futuristic approach, has built its software for humans and “non-humans” (read AI agents).
“When humans need help within a business, they raise a ticket in ServiceNow, BMC, or Jira Service Management … we are basically enabling an architecture where [agents] can actually ping a message to get help — just like human employees,” he asserted.
Atomicwork utilizes existing LLM models from OpenAI, Anthropic, Cohere, and Meta, along with its in-house small models, which it uses for intent detection, routing, and slot filing, to enable workflow automation.
As AI deployment brings the question of data security and safety, Rayapati said the startup addresses this by offering enterprises the option to own the encryption keys for their data or bring their own model endpoints into Atomicwork. He also stated that the startup has signed agreements with cloud service providers AWS and Azure to restrict data retention and training and has multiple compliance certifications.
The all-equity Series A round, co-led by Khosla Ventures and Z47 (previously called Matrix Partners India), also saw participation from Battery Ventures, Blume Ventures, and Peak XV Partners. It was followed by a $3.3 million round involving over 40 CIOs, CTOs, and industry experts in September last year and a seed round of $11 million in 2023. So far, the startup has raised over $38 million.
Currently, Atomicwork has eight customers, most of whom are in the U.S., including Ammex Corporation, Zuora, and Pepper Money.
It plans to utilize the new investment to attract more customers by “doubling down” its AI R&D to bolster its platform engineering, strengthen its product and technology, and invest in building more strategic partnerships to expand integrations with companies including Oracle and SAP.
Rayapati did not disclose the exact valuation but said it was close to 5x the total capital it raised in the new round.
The startup has a team of more than 60 people, including over 50 in India.
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DeepSeek, a Chinese AI startup, became the talk of the proverbial AI town when it released its R1 model on Friday. R1’s functionality and accuracy compared to its U.S. counterparts, despite using fewer resources and less compute power, seems like a win for the overall AI industry. But it isn’t necessarily good news for everyone.
Semiconductor giant Nvidia finds itself in the headwinds of DeepSeek’s recent achievement. The chip giant saw its stock plummet 16.9% from the close of Friday’s public markets to the close of public markets on Monday, according to Yahoo Finance data. Nvidia lost nearly $600 billion off of its market cap. Nvidia’s stock closed at $142.62 a share on Friday afternoon. On Monday, it closed at $118.58.
There is speculation that the reason why DeepSeek’s model release would impact Nvidia’s stock is that R1 provides a clear example that AI models don’t necessarily need expensive, high-end chips or hardware to build an impressive model, which isn’t exactly great news for a chipmaker like Nvidia.
“DeepSeek is an excellent AI advancement and a perfect example of Test Time Scaling,” an Nvidia spokesperson told TechCrunch over email. “DeepSeek’s work illustrates how new models can be created using that technique, leveraging widely-available models and compute that is fully export control compliant. Inference requires significant numbers of Nvidia GPUs and high-performance networking. We now have three scaling laws: pre-training and post-training, which continue, and new test-time scaling.”
The timing of all of this is interesting because this comes one week after former President Joe Biden signed an executive order that made further restrictions on the export of U.S.-produced advanced AI chips to certain countries, with near-blanket restrictions on sending chips to countries like China where DeepSeek is based.
At the time, Nvidia said that the executive order was “unprecedented and misguided” and that it would “derail” innovation and economic growth worldwide.
President Donald Trump has since reversed Biden’s executive order and has signed a different executive order to create the Stargate Project, an infrastructure program that will invest up to $500 billion into AI data centers.
The release of DeepSeek shows that if the U.S. wants dominance over the global AI market, it may need to pay attention to more than just chips and AI hardware.
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