Collective raises $100 million to expand AI-based financial services

The Israeli cybersecurity startup announced the successful raising of $34 million in funding. This round was a Series A of $28 million, led by the fund Red Dot Capital Partners. Representatives from Lumir Growth Partners also participated in the investment.

The company plans for its solutions to enable security teams to respond faster as cyber threats become more complex. Previously, Mave had raised $6 million in seed funding from Dell Technologies Capital and Emerge Ventures.

The funds will be directed towards developing products for the corporate sector and conducting artificial intelligence research, as well as implementing expansion plans. Mave's AI-based tools are designed to reduce the time analysts spend gathering evidence and analyzing alerts.

Mave was founded in 2025 by CEO Raziel Oren and CTO Guy Eshel, who co-founded the company. Both founders previously served in Israel's intelligence unit 'Unit 8200', providing the company with deep expertise in corporate security operations.

Large organizations often use dozens of different security tools. Incident information can be scattered across identity systems, endpoints, and cloud environments. Analysts have to consolidate these disparate data sources before making decisions on further actions.

Traditional Security Information and Event Management (SIEM) systems provide a unified view of collected data, but they may not contain all the necessary information to understand an active incident. This forces analysts to manually check threats across multiple platforms.

Mave's AI agents integrate directly with existing security systems. They query data from each tool, collect relevant information, and consolidate the results within a single investigation. The platform is also designed to filter false positives and detect activity related to potential attacks.

The system is capable of executing approved response actions according to rules set by security teams. These actions can include isolating affected systems, disabling compromised accounts, and blocking malicious activity. Human oversight remains critical when approval or judgment is required.

According to Mave, its platform is already used by 50 companies and global managed security service providers. These implementations help protect hundreds of clients, including organizations operating in regulated financial and medical sectors.

The company reports that its annual recurring revenue has grown by over 400% in the last year and is closing seven-figure deals. These metrics attract commercial attention, even though the absolute revenue volume is not disclosed.

The fundraising comes amid a growing number of AI-driven attacks against organizations. AI-based cyberattacks can develop significantly faster than security teams can conduct manual investigations. Mave believes that agentic systems will help organizations cope with this increasing volume of threats.

The new funding will be used to develop the research platform and strengthen the enterprise platform, as well as for international scaling, as interest in automated security operations continues to grow. The company currently has about 30 employees and plans to double its staff over the next three years, adding specialists in go-to-market and AI research.

The Israeli development center of the company will play a key role in this growth. Like other firms creating AI-based security devices, Mave operates in a competitive environment. The success of Mave's enterprise segment depends on the reliability of investigations, openness of choice, and restraint in response measures.

Dream Labs has raised $90 million in seed funding to create artificial intelligence intended for use in everyday robots. The San Francisco-based startup was founded in 2026 by former leaders of NVIDIA's GR00T research group.

The funding round was led by B Capital and Kindred Ventures. Other investors included A. Capital, Unusual Ventures, SV Angel, Wing VC, Scribble VC, and Adrian Aoun. The company believes that robots often face a problem similar to humans: they are trained for one specific task but cannot apply the acquired skills in other areas.

Dream Labs aims to create more flexible robots using a unified AI model. Such a system would allow it to perform new tasks or work with new equipment while requiring less expertise. CEO Joel Zhang noted that there is sufficient capable hardware in the industry, but there is a lack of intelligence that could function across different robots.

In addition to raising funds, Dream Labs introduced Dream API. This service converts video streams from a robot's camera and instructions given in simple English into motor commands. Developers can use this service to program supported robots without needing to build their own AI.

Furthermore, the company launched Fine-tuning as a Service. Developers and robotics manufacturers can upload demonstration data to receive a custom-tuned model for unequipped or new functions. The technology can also be tested via Robot Playground.

Any user with a browser can send commands to a physical robot at Dream Labs' office in San Francisco and observe the reaction in real time. These products aim to make robotic AI more accessible to developers and manufacturers, and also give potential users the opportunity to test the technology before large-scale implementation.

The company's flagship model, Dream-1, is trained on videos of people performing routine actions. It uses visual information to predict subsequent events and determine the robot's action. Dream-1 is designed to transfer capabilities between different hardware.

The company claims it can control compatible robots equipped with grippers without additional task-specific training. Dream Labs currently collaborates with over 20 hardware partners. The model is capable of performing desk tasks such as picking up items, placing objects, and folding laundry.

Support for wheeled mobile robots and more dexterous robotic arms is planned. General Partner Daisy Kai of B Capital stated that the reduction in equipment costs opens up opportunities for the development of robotic intelligence. She believes that accessible software will help developers create more useful applications on existing machines.

Dream Labs plans to direct most of the new capital toward developing computational infrastructure. These investments will support more users and expand the range of tasks and types of robots that Dream-1 can handle. Although the potential is significant, the researchers' approach still requires validation in real-world conditions.

Hundreds of millions of industrial robots operate worldwide, but most of them are limited to only a few tasks in specific environments. If the model works reliably with all machines, it could simplify production automation and other processes for companies. Furthermore, it could reduce the number of systems firms need to develop to accommodate various robots. However, Dream Labs' technologies must demonstrate their functionality in diverse conditions, as well as with different types of equipment and more complex applications.

North.cloud has successfully raised $60 million in a Series B funding round. These funds will be used to develop the company's financial operating system, which covers cloud services, artificial intelligence, and data handling.

Investors participating in this round include Peak Span Capital, Liquidity Ventures, Companyon Ventures, Alpaca, Uncommon Denominator, Gaingels, and Mana Ventures. The secured capital will allow for increased investment in product development, as well as ensuring a higher degree of automation and adding new integrations.

North enables enterprises to reduce infrastructure costs across all leading cloud service providers and modern data platforms, including Microsoft Azure, Google Cloud, and AWS. The company aims to replace fragmented processes related to various pricing models, usage requirements, and reporting tools inherent in each system (whether it is cloud infrastructure, a data model, or a service).

Thanks to the North platform, clients have saved over $400 million against total managed cloud spending, which approaches two billion dollars. Among the company's clients are AssemblyAI, Atlan, Tines, SnapLogic, Emplifi, and Imply. The platform addresses a growing problem for finance and engineering departments.

Traditionally, teams often use spreadsheets and separate dashboards to understand infrastructure costs, which consumes significant time without automatically eliminating costly inefficiencies. North combines cost analytics with automated optimization, helping businesses identify waste, manage cloud commitments, and increase computing value. This approach provides finance and engineering teams with a unified view of infrastructure economics.

The latest funding followed the release of North v3, which expanded support to Microsoft Azure, thus completing support for three major hyperscalers. Furthermore, native integrations with OpenAI, Anthropic, and Snowflake were implemented, and the Autobot automatic commitment scaling mechanism was enhanced.

Artificial intelligence is creating new challenges in business financial management. The increasing use of models adds an extra layer of complexity to existing cloud service and data expenditures. North is expanding its platform to help companies manage these interconnected costs.

TokenFlow's offering integrates model usage, attribution, budgeting, and financial control into a single system. Businesses can use these features alongside existing cloud infrastructure management, reflecting the growing overlap between AI and traditional infrastructure costs.

North believes that effective cost management requires more than just dashboards for companies. Automated systems can help teams detect changes, improve resource allocation, and leverage optimization capabilities, which can reduce manual workload as AI adoption accelerates.

AssemblyAI reported an effective savings rate of about 50% through its work with North. The company noted that its coverage expanded in parallel with infrastructure growth, allowing the team to focus on customer-centric development.

North plans to reinvest the Series B funds into increasing the number of integrations and strengthening autonomous optimization. Funds will also be invested in additional AI cost management tools and new personnel hiring. These investments will help North realize its vision of fully automating the infrastructure cost lifecycle.

The company demonstrates a customer retention rate of 98.8% and reports over $400 million saved by its clients. North's current clientele includes several large technology companies, and the company believes its results can be leveraged to target other cost categories. As AI plays an increasingly vital role in software development, infrastructure costs are becoming less separate from the product and the business it supports. North is building a platform designed to help such enterprises manage these expenses without stifling innovation.