Educational platform Arivihan raises $10 million to expand AI learning

The corporate startup Ema, founded by former Google and Coinbase executive Surajjit Chatterjee, has successfully closed a Series B funding round, raising $77 million. The round was led by Creaegis.

Investors such as Accel, S32, and Prosus also participated in the round. According to the startup, this funding increases its valuation fourfold, although the exact valuation amount was not disclosed.

Ema plans to use the raised funds to expand its product market rollout operations and enter new geographical regions, including the Asia-Pacific (APAC) and Europe, Middle East, and Africa (EMEA). The company was founded in 2023 and has headquarters in California, along with an office in Bengaluru.

Surajjit Chatterjee, founder and CEO of Ema, noted that 'corporations don't need more software; they need results. Our latest funding round will help us drive agentic business transformation in enterprises that are still stuck in pilot purgatory.'

Ema has developed an agent platform that allows enterprises to automate workflows in functions such as Human Resources (HR) and finance. The startup claims that its product-oriented model enables companies to implement and scale AI capabilities without the need for complex service support.

The company has moved beyond the stage of pilot projects and concepts, beginning to provide AI agent-based solutions for large enterprises. Its clients include Wipro, Hitachi, and NTT Data.

For example, at Wipro, the Ema platform powers an employee assistant that supports over 240,000 employees across 65 countries. The system automates over 100 workflows and processes approximately 2.9 million employee queries annually. The company reports that response time has decreased from days to seconds, and employee satisfaction has increased by 20%.

Prakash Partasarathi, Managing Partner and CTO of Creaegis, emphasized: 'The Ema platform already supports millions of employee interactions in some of the world's largest organizations, delivering measurable results required for management. Ema has created a scalable, productive platform with exceptional cost-efficiency, rather than a service model dependent on individual implementations.'

Companies that use services like ChatGPT or Claude via API do not actually own these models because they send their data to third parties, pay for tokens, and have no access to the internal workings of the model. This situation is a serious concern for banks, hospitals, and government institutions.

For this reason, Arcee AI announced it has raised $150 million in funding to develop open models that enterprises can run directly in their own data centers.

The San Francisco-based company Arcee AI announced its Series B funding round. The leaders of this round were Vista Equity Partners, Cambium Capital, and Emergence Capital, with participation from Microsoft M12's venture division, AI10 Ventures, Hitachi, IAG, P7, and Wipro.

This funding valued Arcee at over $1 billion, making it a new unicorn; after accounting for the new capital, the post-investment valuation reached approximately $1.15 billion.

In 2025, McQuade decided to invest in the company and build advanced models from scratch. He noted in an interview that the startup had about $30 million and decided to allocate 65–70 percent of these funds to training new models.

The result of this investment was the Trinity model line. Over a period of about six months, Arcee transformed from a dense model with 4.5 billion parameters into Trinity Large—a mixture of experts (MoE) model with 400 billion parameters and 13 billion active parameters per token.

Trinity Large was released in early 2026 as a highly efficient, permissively licensed model, fully developed in the United States. It is considered the first frontier-scale open model created end-to-end in America since Meta stopped releasing Llama models.

The most interesting aspect in the industry was the development cost. Arcee reported that the entire model line for 2025, including Trinity Large, was created for approximately $20 million. This amount covers salaries, computing power, data, infrastructure, and operating expenses.

In an industry where many believe that training a new model requires billions, $20 million is an astonishingly low figure. This common view was already being challenged when DeepSeek announced the creation of a top-tier model for less than $6 million.

Tests show that the Trinity models outperform Meta's Llama 3 and match the level of Mistral and leading Chinese models. This allows Arcee to become one of the few American labs capable of competing convincingly in the open-weight segment.

The startup plans to use the raised funds to accelerate the development of the next generation of the Trinity model family. This involves creating larger models, improving reasoning capabilities, and developing more efficient architectures that can run on less powerful hardware.

Another important direction is expanding collaboration with the U.S. Department of Energy and its national laboratories. Open models that users can deploy on private infrastructure are very attractive for governmental and research tasks where data cannot leave secure environments.

The third direction is product development. Arcee intends to create a new generation of tools to help organizations fine-tune, evaluate, implement, and operate open models. This includes tools for fine-tuning, security assessment, and deployment within the Vista Equity Partners portfolio companies. Vista owns over 90 corporate software companies, all of which are potential customers for the open models they can own and manage.