Dream Labs raises $90 million to develop artificial intelligence for domestic robots

Mueon Corporation has successfully raised $65 million in a Series A funding round to develop its modular computing platform. The round was led by Maverick Silicon, with participation from Intel Capital and other investors.

Chief Executive Officer and co-founder Wilfred Gomes stated that the funding will enable the company to bring integrated computing and memory systems to market. Additional investors included Geodesic Alliances Fund, Oregon Venture Fund, M-Ventures, Wellington Capital, IQT, and Defined.

As a result, Mueon's total capital raised now exceeds $80 million. Previously, the company secured $15.5 million in seed funding in 2025.

Mueon develops semiconductor systems designed for high-performance computing and artificial intelligence. The company's technology aims to meet the growing infrastructure needs of data centers. The new funds will be used to expand engineering and operations teams.

The financing will also help advance the Cubelets technology from a platform development stage to a broader validation system. The company plans to prepare this technology for production while deepening customer engagement. Gomes added that Mueon's architecture is designed to overcome existing rack-level limitations.

Mueon's core technology, known as Cubelets, consists of modular building blocks for three-dimensional computing and memory. This architecture integrates compute power, memory, interconnects, power delivery, and thermal management. Unlike traditional data center systems that treat these components as separate design tasks, Mueon designs them as parts of a unified system.

This approach is intended to reduce data transmission distances and lower energy consumption. The company aims to achieve a tenfold improvement in energy efficiency and cost within a single rack. Furthermore, the system architecture is designed to scale to megawatt-level density if required by applications, although these figures are company goals and have not yet undergone independent verification.

Key aspects of the work include silicon architecture and design development, advanced integration, and software orchestration and ecosystem validation. These elements cover hardware architecture, thermal design, power optimization, and electrical parameters, as well as compatibility definition with existing AI frameworks.

The investment community also gained representation on Mueon's board of directors. Manish Muthal, Senior Managing Director at Maverick Silicon, will join the board. Srini Ananth, Managing Director at Intel Capital, will also serve as a director. Both investors emphasized the company's systemic approach to data center design.

Industry partners have also shown interest in the platform's potential. d-Matrix sees opportunities in combining modular infrastructure with in-memory computing. Ampere Computing, now part of SoftBank, is also collaborating with Mueon, focusing on mitigating technological risks as the platform moves toward production. Mueon is also working with technology and manufacturing partners.

The increasing demand for AI workloads continues to strain the power and cooling capacity of data centers. Operators require infrastructure capable of scaling with expanding workloads without proportionally increasing overhead. Mueon's methodology seeks to solve this problem through modular system integration.

The next phase of funding will focus on growing engineering capabilities, engagement, and production readiness. It is necessary to demonstrate that Cubelets can achieve the expected efficiency gains at scale. Successful validation could help establish Mueon's architecture as an alternative for future AI infrastructure.

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.

Albert has successfully raised $55 million in a Series B funding round, with the funds designated for advancing artificial intelligence in scientific research. The leadership of this round was secured by TCV, and new investors joined, including Delta-v Capital, Socium Ventures, High Sage Ventures, Coral Capital, and Legion VC.

Truffle Capital led the financing, with participation from existing investors such as Coatue, J.P. Morgan Growth Equity Partners, Index Ventures, and Applied Ventures. The company plans to use the raised capital to further refine its chemistry-based platform.

Located in Oakland, California, Albert develops an operating system for materials science and chemistry. This platform integrates specialized AI with corporate security, management, and integration features, such as no data storage, granular access control, and audit trails of reasoning.

Albert provides services to organizations with complex research challenges facing evolving regulations and disrupted supply chains. Among the company's clients are BASF, Henkel, Clorox, Kenvue, and Akzo Nobel. Albert's goal is to help these companies' scientists faster transform experimental data into innovative products.

Albert's model, named Valence, is chemically specific and demonstrates an accuracy 45 times higher than general AI when answering scientific questions. This accuracy metric was achieved in a new study accompanying the funding announcement. Valence was trained on text processing and composition and function identification.

The method mimics the ability of an experienced chemist to distinguish between similar compositions and materials, as well as understand how minor changes can significantly impact an experiment. In chemistry, for instance, the slightest deviations in ingredients, procedures, or experimental conditions can lead to different results. Albert handles this through domain-specific intelligence and its Form Bench benchmark for formal chemistry.

Albert asserts that this benchmark will set a new standard for evaluation in this highly specialized field. The Albert platform combines scientific knowledge, AI tools, integrations, and regulatory control into a single environment. It allows scientists to interact with research data using natural language. By querying Albert, users coordinate tools, agents, and models to support complex scientific work.

The system helps teams find institutional knowledge and avoid repeating past experiments. It is capable of suggesting formula candidates, assisting in experiment design, and analyzing large datasets. These capabilities aim to eliminate bottlenecks in research and improve decision-making processes.

Furthermore, Albert supports enterprise requirements regarding data security and controlled access. Its chemistry-specific approach enables organizations to link information across various research systems, facilitating the discovery and reuse of valuable experimental knowledge.

Over the past year, Albert has demonstrated significant platform growth. Project creation increased 26-fold as Albert OS was implemented in organizational research functions. The number of scientists using the platform grew sixfold across 44 countries. These achievements reflect growing interest in specialized AI for corporate research.

Workflow creation also increased fourfold as more teams adopted various research processes on the platform. Ask Albert transformed from an AI search assistant into a broader R&D partner in just six months. Client feedback highlights the value of connecting disparate scientific information. Kenvue noted that the partnership with Albert improved connections within the overall data platform, while AkzoNobel used Ask Albert to query information covering raw materials and lab journal entries. Albert intends to use the new funding to expand its capabilities and accelerate product development.