iMerit startet Plattform, die KI-Entwickler mit Fachexperten verbindet
Der Datenservice-Anbieter iMerit, der im Juli von ExlService Holdings übernommen wurde, hat eine Plattform gestartet. Damit sollen KI-Entwickler spezialisierte Fachexperten rekrutieren und verwalten, die Modelle bewerten, per Fine-tuning verbessern und validieren.
SHARE UPDATED 08:00 EDT / OCTOBER 26 2026
IMerit Technology Service Pvt Ltd., a provider of data annotation and artificial intelligence training services that was acquired by ExlService Holdings Inc. in July, today launched a platform to help AI developers recruit and manage specialized experts who can evaluate, fine-tune and validate models for use in demanding enterprise applications.
The iMerit Scholars platform is designed to manage a global network that the company expects to grow to hundreds of thousands of experts. Its existing community includes more than 30,000 vetted professionals across 118 countries, representing 106 languages and disciplines including medicine, mathematics, agriculture and autonomous systems.
The company said the launch reflects a shift in AI development from building increasingly capable foundation models to adapting those models for specific applications, particularly in regulated industries such as healthcare, banking and insurance.
“It’s like having a product and saying, ‘What are we going to do with it?’” said Radha Basu, executive vice president and head of iMerit at EXL. She said specialized human expertise is becoming increasingly important as enterprises deploy AI in applications where errors can have serious consequences.
The platform provides centralized tools for recruiting, onboarding, evaluating, assigning and compensating experts. Built around iMerit’s proprietary Ango Hub data annotation, workflow automation and model fine-tuning platform, it supports collaboration on multimodal training data, model evaluation and feedback workflows. Payment processing and compliance capabilities are provided through integration with Trolley Technologies Inc.
Unlike conventional data labeling services, iMerit emphasizes the ability of specialists to challenge AI models and identify mistakes that automated evaluations might miss.
“We call it tormenting models,” Basu said. “Question the model, question the model to make sure it’s working, actually try to break it.”
The company uses a three-tier assessment process to qualify participants. Initial screening evaluates general AI capabilities and cognitive skills. Subsequent assessments examine domain-specific qualifications and the ability to work effectively with AI models. The most specialized evaluations involve peer reviews conducted by other experts.
The company also manages customer projects directly rather than simply matching clients with independent contractors. Its responsibilities include project management, quality assurance and auditing. Experts are compensated for individual engagements but remain part of the broader Scholars community.
Maintaining long-term relationships matters because specialists build knowledge about models across successive evaluation and training cycles, Basu said.
The company offers several pricing arrangements, including hourly rates based on expertise, contracts tied to specified deliverables and engagements linked to measurable outcomes. Basu cited agricultural models that identify early crop failures as an example of work that could be evaluated against performance targets.
The company said it can also accommodate dramatically fluctuating demand. Basu recalled one customer requesting approximately 400 additional workers within three or four days to address an urgent requirement.
The EXL acquisition gives iMerit access to additional healthcare and insurance expertise, expanding its ability to support enterprise-specific AI applications.
Basu said the company particularly wants to broaden its coverage of specialized disciplines and languages. “Our biggest need right now is to go into new expertise, scale into newer languages and into newer levels of the same expertise,” she said.
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