iMerit Launches Scholars Platform to Scale Expert-Led AI Training and Evaluation

A model can produce a convincing medical explanation or a fluent answer to a mathematics problem and still make a mistake that only a specialist will recognize. That need for expert judgment sits behind iMerit’s launch of the iMerit Scholars platform, a system designed to organize the people who train, evaluate and challenge advanced AI models.

The EXL-owned company announced the launch on October 26, bringing recruitment, onboarding, expert matching, project assignments, engagement and payments into a unified platform. Its existing Scholars program includes more than 30,000 invited and vetted professionals across 118 countries and 106 languages, according to the announcement.

The platform is designed to manage hundreds of thousands of experts as demand grows. That is a statement about intended operating scale, distinct from the community’s current reported size. The immediate proposition is to make specialized human input easier to coordinate throughout the model development and deployment lifecycle.

Building the Workforce Behind Model Improvement

The Scholars community spans medicine, mathematics, agriculture, linguistics, autonomous systems and other specialized fields. These experts contribute to supervised fine-tuning, model evaluation, red-teaming and reinforcement learning from human feedback, commonly called RLHF.

Those activities need different kinds of judgment. Fine-tuning can require a carefully constructed example of the response a model should produce. Evaluation asks whether an output meets defined criteria. Red-teaming deliberately probes a system for failures, while human feedback can provide preferences and corrections that inform subsequent training.

Matching the right person to the task therefore matters as much as expanding the pool. A clinician familiar with a narrow specialty may spot a consequential error that a broadly knowledgeable reviewer misses. A linguist may recognize an ambiguity that disappears in a literal translation.

The new platform matches projects to domain specialization and cognitive skills, while providing the administrative infrastructure to keep those experts engaged. iMerit describes the goal as a continuing relationship with specialists who learn the work and participate in feedback cycles.

“We need people who are knowledgeable enough not just to teach models, but to prove them wrong and correct them,” said Radha Basu, executive vice president and head of iMerit at EXL.

Ango Hub Connects Expert Judgment With Training Data

The operational technology underneath the Scholars platform is Ango Hub, iMerit’s data annotation and model evaluation platform. Its role is to turn expert work into organized, reviewable artifacts that can enter a training or evaluation pipeline.

Ango Hub’s existing product documentation describes configurable workflows, multiple annotators on an asset, benchmark tasks and reviewer controls to accept, reject or correct labels. Project guidelines and an issue system help reviewers resolve questions, while role-based access controls govern who can work with particular data.

The platform supports several modalities, including images, video, audio, text, PDFs and medical DICOM data. That breadth is relevant because model development increasingly combines language with visual, clinical or physical-world information. The interface and review process need to fit the material an expert is examining.

The distinction between the two platforms is useful: Scholars coordinates the expert workforce and its relationship with projects, while Ango Hub provides the tools and workflows through which expert contributions become usable AI data.

From Correcting Answers to Evaluating Entire Agent Workflows

iMerit’s Deep Reasoning Lab adds a more detailed view of that process. The company describes experts designing multi-step scenarios, reviewing model outputs, categorizing errors and supplying corrections with explanations. These structured examples can help developers identify where an answer goes wrong and create targeted training material.

Such reasoning traces are curated artifacts created and reviewed for a task. They should not be assumed to reveal every internal computation that produced a model’s answer. Their practical value is in making an evaluation path and its corrections explicit enough to inspect and reuse.

The company’s agent evaluation service extends review beyond a final response to planning steps, tool calls, intermediate states and outcomes. It describes structured rubrics and expert adjudication for issues such as task completion, tool selection, grounding and policy boundaries.

That becomes important when AI can take action. An agent may produce a polished summary while using the wrong tool or leaving the underlying task incomplete. Reviewing the sequence of actions gives specialists a way to identify those failures and return structured feedback to developers.

Payments and Project Continuity Become Part of the Infrastructure

The launch also integrates Trolley for global payouts. iMerit says the integration supports secure payments and compliance workflows for its international expert community.

Payment operations may appear removed from model performance, but they affect whether a specialist can participate consistently. Recruitment, onboarding, task access and compensation all become operational dependencies when a project relies on experts across many countries.

The platform brings those functions alongside matching and engagement rather than leaving each project team to assemble them separately. For customers, the intended benefit is a more consistent workforce and review process; for experts, it is a clearer path from joining a project to completing work and receiving payment.

“You’re not just labeling data, you’re helping shape how these systems reason and behave in your field,” said Pavel Kopach, M.D., a pathology specialist at Beth Israel Deaconess Medical Center and an iMerit Scholar in healthcare AI, in the announcement.

EXL Brings the Enterprise Deployment Context

The launch follows EXL’s completion of its iMerit acquisition, announced August 3. EXL positioned the combination around joining model training and evaluation expertise with its enterprise data, operational context and AI implementation capabilities.

That creates a broader route to market for Scholars. Basu said iMerit is expanding from frontier-model development into banking, healthcare and insurance workflows, where domain customization, trust and safety affect whether AI can operate successfully in production.

The new platform does not itself establish that a model is reliable in every specialized setting. It addresses a more concrete prerequisite: organizing the people, tools and feedback needed to test a system against the demands of that setting. As AI moves deeper into professional workflows, the ability to find the right expert, capture a useful correction and bring that knowledge back into development becomes an increasingly important part of the technology stack.