Mach9 raises $34 million to develop AI-based software for civil engineering

Blacksmith, a software infrastructure provider, successfully closed a Series C funding round, raising $66 million. The leadership of this round secured Sequoia Capital at a company valuation of $1 billion. Premji Invest also participated in the round, along with existing investors including Peak XV Partners, GV, and Y Combinator.

This is the ninth funding round for Blacksmith. With the new capital, the total investment in the company has reached $125 million. The funds are planned to be used for expanding the team and increasing computing power. The Blacksmith platform is used for continuous integration (CI), and the company claims that 10,000 companies use its platform.

Continuous Integration (CI) automatically builds and tests code whenever a developer makes changes to the code repository. This allows teams to detect issues early before product release. The growth in development volume creates additional load on traditional infrastructure.

Blacksmith was founded in 2024 by individuals such as Aditya Jayaprakash, Aayush Shah, and Aditya Maru. The founders met while studying at the University of Waterloo. Their main goal is to accelerate and reduce software testing costs.

AI-based coding tools are transforming the software development process. Although AI tools can quickly generate code, this code still requires verification, which puts increased strain on systems responsible for building and testing applications. Traditional cloud infrastructure often struggles to scale when CI workloads change, as demand can jump sharply from near zero to tens of thousands of CPUs.

Blacksmith developed its infrastructure specifically to support CI workloads. Clients can make just one change to the workflow configuration, and Blacksmith handles all the work of providing and maintaining runners without client involvement in the setup process. Among the company's clients are Ashby, Granola, Handshake, and LangChain. Blacksmith's infrastructure covers data centers in the US and Europe, and the company manages capacity approaching 10 megawatts. It plans to increase this base by at least five times by 2027.

This expansion is intended to support the growing demand from engineering teams performing a higher volume of automated tests. Weekly CI jobs on the platform have increased from five million in January to over 90 million. Furthermore, Blacksmith reports a 17-fold increase in its annual recurring revenue over the same period. Infrastructure engineer Joaquin Borggio reported that LangChain benefited operationally from the service, as Blacksmith allowed them to reduce runner costs by approximately 40% and save engineering time by reducing infrastructure management.

The new capital will be used by Blacksmith to build its infrastructure to accommodate changing workload types it can handle in software development. The team also plans to scale up capacity and increase staff to serve a wider customer base. While AI-based coding agents can accelerate software production, quality assurance is necessary. Accelerated testing can become a bottleneck in the development pipeline, but it facilitates more frequent releases. Blacksmith will face competition from major cloud providers and developer tools.

The company's future development will depend on performance, infrastructure cost control, and customer demand. This latest funding demonstrates that investors remain optimistic about infrastructure supporting AI-driven software development.

Hunar.AI has successfully raised 40 crore rupees in a Series A funding round led by Blume Ventures. Hyderabad Angels Fund, Together Fund, Seedstars, AVNM Ventures, as well as co-founders Prateek Maheshwari and Alakha Pandey of PhysicsWallah, and co-founder Aditya Wuchi of MediaMint also participated in this round.

Hunar.AI plans to use the raised funds to develop artificial intelligence infrastructure, improve products, and expand its market presence. The company focuses on creating specialized AI technologies for frontline industries where operational complexities arise from the need for mass hiring, geographically distributed teams, and linguistic diversity—challenges that traditional HR systems cannot effectively solve.

Krishna Handelwal, co-founder and CEO of Hunar.AI, noted that managing the workforce on the frontline is one of the largest yet least satisfied opportunities in corporate AI. He emphasized that solving this problem requires a deep understanding of industry workflows, multilingual capabilities, and technology capable of reliably executing tasks at scale, rather than simply applying generic models to existing processes.

Hunar was founded in 2022 by Krishna Handelwal and Shantanu Bhattacharya. The Hunar platform includes Hunar Voice and Hunar Connect, which utilize conversational AI agents to manage personnel workflows via voice messages, WhatsApp, and chats. These workflows cover candidate sourcing and selection, interview scheduling, onboarding, training, employee engagement, and retention. By automating high-volume interactions, the platform helps enterprises manage frontline operations while maintaining connectivity with candidates and employees across different locations.

Arpit Agrawal, Partner at Blume Ventures, stated that as the world turns to India for lessons in managing these new types of businesses at scale, Hunar is building the AI layer that makes it possible. He added that due to its focus on outcomes rather than infrastructure, Hunar has the potential to create a category in India that can scale globally.

According to the company's statement, in September 2026, Hunar AI agents processed over 15 million conversations, with the volume of these conversations doubling quarterly. Hunar's clients include Swiggy, Delhivery, Instamart, Pronto, Blue Dart, Flipkart, Aditya Birla Capital, and Tata Croma. Furthermore, NITI Aayog predicts that India's gig workforce will grow from approximately 10 million workers in 2024–25 to 23.5 million by 2029–30. As businesses expand in cities and regions, effective hiring, onboarding, and retention have become an important operational priority, yet many processes still rely on manual calls, follow-ups, and coordination.

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.