EXL Completes Acquisition of iMerit Accelerating Enterprise AI Leadership

• iMerit founder and CEO Radha Ramaswami Basu joins EXL executive committee

ExlService Holdings, Inc. (NASDAQ: EXLS), a global data and AI company, announced it has completed the acquisition of iMerit, a recognized leader in AI model training, evaluation and reinforcement learning. Together, EXL’s enterprise data and AI leadership and iMerit’s capabilities and foundation model relationships will help clients build AI systems that are trusted, accountable and built to perform in the enterprise.
The completion of the acquisition establishes an end-to-end AI platform for enterprises, uniting EXL’s deep data, context and AI expertise with iMerit's technology, expert-led solutions and generative AI experience helping them accelerate the transition from pilot to production-scale AI.

As part of the transaction, iMerit founder and CEO Radha Ramaswami Basu joins EXL as Executive Vice President, Head of iMerit, and becomes a member of the company’s executive committee.

“This acquisition is a transformational pivot for EXL, deepening our vertically specialized AI capabilities and expanding our reach into high-growth AI technology sectors,” said Rohit Kapoor, chairman and chief executive officer of EXL. “By combining iMerit’s capabilities with EXL’s domain expertise and AI platforms, we are well positioned to help clients build, fine-tune and operationalize AI that performs reliably in production. This is especially critical in regulated industries where domain knowledge, context and compliance are non-negotiable. I am delighted to welcome Radha to EXL's executive committee; her vision, leadership, and deep expertise at the intersection of human intelligence and AI will help shape the next chapter of EXL's growth and innovation.”

“The next generation of enterprise AI will be defined not by the models organizations choose, but by how effectively they can deploy them in real-world business environments,” said Basu. “What excites me most about joining EXL is the opportunity to combine iMerit’s pioneering work in AI data, evaluation and human intelligence with EXL’s extraordinary depth in data, AI and enterprise transformation. Together, we can help clients bridge the gap between innovation and execution, turning AI potential into measurable business results. I am energized by what lies ahead and honored to join EXL’s executive committee as we help clients unlock the full value in enterprise AI integration.”

With iMerit now part of EXL, the combination extends the data and AI-led strategy EXL has been executing on for several years.

About EXL

EXL (NASDAQ: EXLS) is a global data and AI company that offers services and solutions to reinvent client business models, drive better outcomes and unlock growth with speed. EXL harnesses the power of data, AI, and deep industry knowledge to transform businesses, including the world's leading corporations in industries including insurance, healthcare, banking and capital markets, retail, communications and media, and energy and infrastructure, among others. EXL was founded in 1999 with the core values of innovation, collaboration, excellence, integrity and respect. We are headquartered in New York and have approximately 68,000 employees spanning six continents. For more information, visit www.exlservice.com.

About iMerit

iMerit is a leader in AI fine tuning, evaluation, and reinforcement learning. iMerit helps frontier AI labs and enterprises build more accurate, reliable, and domain-aware models. iMerit delivers high-quality data across industries such as high-tech, autonomous mobility, healthcare AI, and robotics. Scholars, its global network of specialists, includes physicians, scientists, engineers, linguists, and other subject matter experts who power high-quality data creation, reasoning evaluation, model alignment, and human feedback workflows for next-generation AI systems. Its proprietary Ango Hub platform allows customers and experts to collaborate on complex multimodal data to generate highly curated and validated training artifacts for high-stakes models. Learn more at imerit.ai.

EXL Acquires iMerit to Strengthen AI Training and Enterprise‑Scale Platforms

• Positions EXL to accelerate AI innovation in the enterprise with iMerit’s direct relationships with foundation model builders. 
• Deepens EXL’s vertically specialized end-to-end AI capabilities with iMerit’s model training, evaluation and reinforcement learning. 
• Expands EXL’s total addressable market across high-growth AI tech sectors, and multiplies the impact of iMerit on a broader enterprise audience

ExlService Holdings, Inc. (NASDAQ: EXLS), a global data and AI company, today announced a definitive agreement to acquire iMerit, a recognized leader in AI model training, evaluation and reinforcement learning. iMerit is focused on helping its clients train large language and multimodal models to improve accuracy, precision, and effectiveness. The acquisition, valued at up to $310 million in upfront and future consideration, is expected to close in the third quarter of 2026, subject to customary closing conditions. The move strengthens EXL’s ability to help enterprises achieve measurable outcomes from AI, builds partnerships with leading foundation model builders and expands EXL’s reach into high-growth AI tech sectors.

"As organizations reimagine their businesses with AI, success requires industry-specific data, rigorous evaluation and reinforcement learning to deliver reliable results in business-critical workflows,” said Rohit Kapoor, chairman and chief executive officer of EXL. “The acquisition of iMerit strengthens EXL’s AI strategy and ability to help clients move from experimentation to production. By combining iMerit’s capabilities with EXL’s domain expertise and AI platforms, we are setting the standard for AI that is trusted, accountable and built to perform in the enterprise.”

EXL will now be at the center of how next-gen AI is built, leveraging iMerit’s client relationships with leading foundation model companies. EXL and its clients will benefit from early insight into how models are trained, fine-tuned and improved. This also positions EXL to help enterprises build fit-for-purpose small language models tailored to their data and workflows.

iMerit enhances EXL’s platform and human intelligence capabilities through its Ango platform and Scholars network. Ango powers sophisticated data interactions with GenAI models, enabling chain-of-thought reasoning, red teaming and multimodal evaluations. Scholars expands EXL’s domain expertise through iMerit’s global network of specialists, including physicians, scientists, engineers, linguists and other subject matter experts who support human intelligence-driven feedback workflows for reinforcement learning.
EXL will integrate Ango with its agentic platforms — including EXLerate.ai, EXLdata.ai, and EXLdecision.ai — to combine expert human judgment, model evaluation and enterprise-scale execution. Together, these capabilities create an end-to-end AI platform that helps enterprises accelerate the transition from pilot to production-scale AI.

We see EXL as an ideal leader in this defining moment for AI. We can build on our work with AI innovators and bring those insights to companies seeking to unlock their proprietary data,” said Radha Ramaswami Basu, chief executive officer and founder of iMerit. “Both companies share a belief that specialized high-quality data is the foundation of AI success. We are excited to multiply our impact through EXL’s industry expertise, complementary technology and trusted enterprise relationships.

These offerings strengthen EXL’s vertically integrated AI stack and its ability to build and fine-tune domain-specific language models. This is particularly critical for regulated industries such as healthcare, insurance, banking and capital markets where EXL is already a highly trusted data and AI partner.

This acquisition also expands EXL into high-growth AI sectors, including high tech, mobility, autonomous systems and physical AI. iMerit’s expertise across text, image, video, voice and LiDAR data creates a strong foundation for AI solutions powering robotics, autonomous vehicles and intelligent real-world environments.

Transaction Details

The $310 million acquisition involves an upfront consideration of $170 million, with an additional $140 million in incentives and earnouts over two years contingent on meeting specified milestones. The transaction is expected to close in the third quarter of this year, subject to customary closing conditions, including expiration or termination of the waiting period for applicable antitrust regulations.

About EXL

EXL (NASDAQ: EXLS) is a global data and AI company that offers services and solutions to reinvent client business models, drive better outcomes and unlock growth with speed. EXL harnesses the power of data, AI and deep industry knowledge to transform businesses, including the world’s leading corporations in industries including insurance, healthcare and life sciences, banking and capital markets, retail, communications and media and energy and infrastructure, among others. EXL was founded in 1999 with the core values of innovation, collaboration, excellence, integrity and respect. We are headquartered in New York and have over 67,000 employees spanning six continents. For more information, visit www.exlservice.com.

About iMerit

iMerit is a leader in AI fine tuning, evaluation, and reinforcement learning. iMerit helps frontier AI labs and enterprises build more accurate, reliable, and domain-aware models. iMerit delivers high-quality data across industries such as high-tech, autonomous mobility, healthcare AI, and robotics. Scholars, its global network of specialists, includes physicians, scientists, engineers, linguists, and other subject matter experts who power high-quality data creation, reasoning evaluation, model alignment, and human feedback workflows for next-generation AI systems. Its proprietary Ango Hub platform allows customers and experts to collaborate on complex multimodal data to generate highly curated and validated training artifacts for high-stakes models. iMerit is backed by Khosla Ventures, Omidyar Network, Dell Foundation and British International Investment (BII). Learn more at imerit.ai.

Uber India Pilots In-App Digital Tasks for Drivers, Expanding Income Streams Beyond Rides

Uber has introduced a new pilot program in India that allows its driver partners to earn money by completing digital tasks directly within the Uber app. Piloted across 12 cities, including Delhi, Mumbai, Bengaluru, and Hyderabad, as well as smaller markets like Pune, Jaipur, Bhopal, and Vizag, the initiative aims to expand drivers’ earning options beyond ridesharing.

Drivers can now earn extra by completing micro tasks on the Uber app, like tagging objects in photos, for the company’s data-labelling platform, Uber AI Solutions, in addition to tasks like classifying short text, counting objects, and digitizing receipts, all key components of building artificial intelligence models.

Until now, such work was usually outsourced to independent contractors outside the Uber ecosystem. By bringing it into the driver app, Uber is offering drivers an additional stream of income, especially during downtimes when ride demand is low.

“This pilot is about giving drivers more choice, flexibility, and ways to earn,” said Megha Yethadka, Global Head, Uber AI Solutions. “Drivers can face downtime at certain times of the day, and digital tasks offer a way to make that time productive. Early engagement has been strong, with many thousands of tasks already completed.”

Uber AI Solutions works with global companies to deliver services like data labelling, localisation, and product testing which are the building blocks of high-performing AI systems. If successful, the India pilot may become a template for similar initiatives worldwide, giving Uber’s vast network of drivers and delivery partners a role in the fast-growing AI economy.

OpenAI Argues Public Data Isn't Commercial Use in Delhi HC Dispute

OpenAI recently argued before the Delhi High Court that using publicly available data to train ChatGPT does not constitute a commercial activity in itself. The case stems from a lawsuit filed by ANI Media, which alleges that OpenAI used its content without permission to train its AI models.

OpenAI's legal team contended that training a large language model (LLM) is a neutral activity that can be used for both commercial and non-commercial purposes. They emphasized that ChatGPT drives traffic to ANI's website, meaning no commercial harm is being caused to the news agency. Additionally, OpenAI argued that copyright law protects the expression of content, not the discovery of ideas and facts.

The court has scheduled the next hearing for May 16, where rejoinder arguments from ANI and other parties will be considered.

This case could have significant implications for AI training and copyright law in India.

What does copyright law say about using public data for AI training?

Copyright law varies across jurisdictions, but generally, it protects the expression of ideas rather than the ideas themselves. When it comes to AI training, the key legal questions revolve around whether scraping publicly available data constitutes copyright infringement and whether AI-generated outputs violate existing protections.

Key Considerations:

• Fair Use & Exceptions: Some countries, like the United States, allow limited use of copyrighted material under fair use, which considers factors like purpose, amount used, and market impact. However, this is often debated in AI contexts.
• Text & Data Mining (TDM) Exemptions: The European Union has introduced opt-out mechanisms for copyright holders, allowing AI developers to use publicly available data unless explicitly restricted.
• India’s Copyright Act: Section 52(1)(c) of India's Copyright Act provides exemptions for transient or incidental storage of copyrighted works, which some argue could apply to AI training..
• Legal Challenges: OpenAI is currently facing lawsuits, including one in the Delhi High Court, where ANI Media claims its content was used without permission. Courts are still determining whether AI training qualifies as a commercial activity or falls under research exemptions.

The debate is ongoing, and legal frameworks are evolving to address AI’s impact on copyright.

Next-Level AI Training: Quantum Computing Fine-Tunes Billion-Parameter Model

Chinese researchers have achieved a global first by using a real quantum computer to fine-tune an Al model with one billion parameters. The experiment was conducted on Origin Wukong, China's third-generation superconducting quantum computer with 72 qubits.

Workers calibrate and install the China's independently developed third-generation superconducting quantum computer. Photo:Courtesy: Anhui Quantum Computing Engineering Research Center

This breakthrough led to an 8.4% improvement in training performance while reducing the number of parameters by 76%. The Al model also showed better results in specific tasks-when trained on mental health conversation data, it made 15% fewer mistakes, and in a math problem-solving test, its accuracy jumped from 68% to 82%.

The fine-tuning process traditionally requires high computing power, but quantum computing offers unique Chinese researchers have achieved a global first by using a real quantum computer to fine-tune an Al model with one billion parameters. The experiment was conducted on Origin Wukong, China's third-generation superconducting quantum computer with 72 qubits.

This breakthrough led to an 8.4% improvement in training performance while reducing the number of parameters by 76%. The Al model also showed better results in specific tasks-when trained on mental health conversation data, it made 15% fewer mistakes, and in a math problem-solving test, its accuracy jumped from 68% to 82%.

The fine-tuning process traditionally requires high computing power, but quantum computing offers unique advantages. By leveraging superposition and entanglement, quantum computers can explore vast combinations of parameters simultaneously, making Al training faster and more efficient.

This development could be a game-changer for Al training, reducing computational costs and improving model efficiency.

Experts have reacted with cautious optimism to China's breakthrough in using a quantum computer to fine-tune a billion-parameter AI model.

Some experts remain skeptical, pointing out that while the results are promising, the research is still in the demonstration phase and lacks peer-reviewed validation.