EXL Buys AI Training Firm iMerit to Close the Gap Between AI Demos and Real Business Results
The New York services giant spent this month completing its purchase of iMerit, a company that prepares the data and human expertise AI models need before they can work reliably in hospitals, factories, and financial firms.
Key points
- EXL Service Holdings, a New York company with roughly 68,000 employees, completed its acquisition of iMerit Technology in July 2025.
- iMerit, founded in 2012 and based in San Jose, California, specialises in labelling and checking the data used to train AI models for robotics, healthcare, and autonomous vehicles.
- The combined company plans to offer enterprises a single path from raw data to a finished, tested AI system ready for production use.
- EXL CEO Rohit Kapoor told The Robot Report that competitive advantage in AI will come from proprietary data and continuous evaluation, not from picking the most powerful general model.
- iMerit founder Radha Ramaswami Basu joins EXL as executive vice president and head of iMerit.
Most AI projects fail not because the underlying model is weak, but because nobody prepared it properly for the messy, specific conditions of a real job. EXL Service Holdings wants to fix that.
EXL, which sells technology-driven services to insurers, banks, healthcare firms, and retailers, completed its acquisition of iMerit Technology this month. The price was not disclosed publicly.
What does iMerit actually do?
iMerit trains and checks AI models. The company employs subject-matter experts who label raw data, the images, text, audio, and sensor readings that an AI model learns from, and then stress-test the model to find the places where it gets things wrong.
Think of it this way: before a self-driving car can recognise a child running into the road, thousands of human reviewers must first watch video footage and mark every child, every kerb, every unexpected obstacle in every frame. iMerit is one of the companies that does that work at scale. It also built Ango Hub, a software platform that lets clients and domain experts collaborate on sorting and validating complex data sets together.
One concrete example: iMerit partnered with Carbon Robotics to process millions of plant photographs so a robot could learn to weed farmland without pulling up crops.
Why does this deal matter for businesses using AI?
It matters because most companies are stuck between a promising AI pilot and a production system they can actually trust. That gap costs money and time.
Kapoor told The Robot Report that the market is shifting. Value is moving away from building the raw AI model toward making AI usable, trustworthy, and consistently accurate inside a specific business workflow. The combined EXL-iMerit operation aims to cover the full chain: gathering proprietary company data, fine-tuning a model on that data, evaluating how it behaves under pressure, and then running it inside a live business process.
| Capability | Who brings it |
|---|---|
| Expert data labelling and annotation | iMerit |
| Ango Hub collaboration platform | iMerit |
| Global domain expert network | iMerit |
| Enterprise industry data (insurance, healthcare, banking) | EXL |
| Business process integration | EXL |
| Trace analysis for model error investigation | EXL |
Basu, iMerit's founder, put the core problem plainly. A model can score well on a standard benchmark test, the kind of exam used to rank AI systems, and still fall apart when it meets the unusual or high-stakes situations that real healthcare or financial workflows throw at it regularly. Human experts who can challenge the model and identify its failure points are the fix, not an optional extra.
What about robots and self-driving vehicles?
Physical AI, meaning AI that controls a body in the real world, whether a surgical robot or a lorry, has even less room for error than a chatbot giving bad advice.
Basu explained that autonomous vehicles are trained on layered sensor data: camera vision, lidar (a laser-based distance-measuring system), and audio. Human reviewers then evaluate not just what the vehicle detected, but whether its decision and its explanation of that decision would have been safe in a real collision scenario. That review process is where iMerit's experts operate.
Safety and compliance, Basu said, cannot be a final checkbox before launch. They have to be baked into every stage: data collection, training, evaluation, and ongoing monitoring.
What is the honest takeaway here?
The acquisition is a reminder that the expensive part of enterprise AI is rarely the model itself. Preparation, quality control, and continuous human oversight are where projects succeed or fail, and where most of the spending ends up going.
If your company is considering deploying AI in a regulated or high-stakes setting, the most useful question to ask any vendor is not "which model do you use?" but "who checks whether it is actually right, and how often?"
