Radha Basu, Founder and CEO of iMerit has built her profession at HP, spending 20 years with the tech giant and eventually heading its Enterprise Solutions group. She then took Support.com public as its CEO. Radha began Anudip Foundation in 2007 with Dipak Basu after which founded iMerit in 2012. She is taken into account a number one tech entrepreneur and mentor, and a pioneer within the software business.
iMerit delivers multimodal AI data solutions by combining automation, expert human annotation, and advanced analytics to support high-quality data labeling and model fine-tuning at scale.
You’ve had a remarkable journey—from constructing HP’s operations in India to founding iMerit with a mission to uplift marginalized youth in Bhutan, India, and Recent Orleans. What inspired you to start out iMerit, and what challenges did you face in creating an inclusive, global workforce from the bottom up?
Before founding iMerit, I used to be Chairman and CEO of SupportSoft, where I led the corporate through its initial and secondary public offerings, establishing it as a world leader in support automation software. That have showed me the facility of mixing people and technology from day one.
While India’s tech boom created latest opportunities, I noticed many talented young people in underserved areas were left behind. I believed of their potential and drive to learn. Once they saw how software could power advanced technologies like AI, they eagerly embraced these careers.
We launched iMerit with a small, diverse team, half of whom are women, and have grown rapidly ever since. Our team’s adaptability and coachability have been key, especially as data-centric AI has increased long-term demand for expert specialists.
Today, iMerit is a world provider of AI data solutions for mission-critical sectors like autonomous vehicles, medical AI, and technology. Our work ensures customers’ AI models are built on high-quality, reliable data, which is important in high-stakes environments.
Ultimately, our strength lies in strong technology underpinnings and a team of well-trained, motivated employees who thrive in a supportive, learning-driven culture. This approach has fueled our growth, kept us money positive, and earned us high NPS scores and dependable clients.
iMerit now works with over 200 clients, including tech giants like eBay and Johnson & Johnson. Are you able to walk us through the corporate’s growth journey—from those early days to becoming a world leader in AI data services?
We’ve had a front-row seat to our clients’ AI journeys, partnering from early experiments to large-scale production. Our work spans startups, global autonomous vehicle leaders, and major enterprises. By training their models from the bottom up, we’ve gained unparalleled insight into what it truly takes to scale AI in the true world.
The sector has evolved continually and rapidly. I even have rarely seen a technology advance so dramatically in such a short while. We’ve transformed from a knowledge annotation provider right into a full-stack AI data company, delivering specialized solutions across the whole human-in-the-loop (HITL) lifecycle: annotation, validation, audit, and red-teaming. Handling edge cases and exceptions is significant for real-world deployment, requiring deep expertise and nuanced judgment at every step.
Our largest vertical is autonomous mobility, where we manage the total perception stack, including sensor fusion across 15 sensors for passenger, delivery, trucking, and agricultural vehicles. In healthcare, we drive clinical imaging AI. In high-tech, we’re on the forefront of GenAI tuning and validation, demanding greater sophistication in our workflows and talent.
Success in these domains isn’t nearly having experts- it’s about cultivating expertise: the cognitive ability to challenge, coach, and contextualize AI models. That is what sets our teams apart.
Our growth is fueled by long-term partnerships, and most of our top ten clients have been with us for over five years. As their needs grow more complex, we continually elevate our domain knowledge, tooling, training, and solutions. Each our tech stack and our people must continually evolve.
The fusion of software, automation, annotation, and analytics, creates the rubric for very flexible, rapid, highly precise, human-in-the-loop interventions. 70% of recent logos are on our own tech stack, which requires an enormous internal transformation. Again, our culture ensures the teams are hungry to learn and wish to grow continually.
What have been probably the most pivotal moments in iMerit’s history—whether technological milestones or strategic decisions—that helped shape the corporate’s trajectory?
At a time when AI data work was seen as crowd-based gig work, we took an early bet that this could grow as a profession and would require complexity and enterprise focus. By constructing in-house teams dedicated to advanced use cases, we enabled our clients to scale rapidly, culminating in our first $1M MRR deal in autonomous vehicles, a milestone that validated our approach.
The COVID-19 lockdown tested our agility: we transitioned from fully in-office to totally distant almost overnight, investing heavily in infrastructure, security, and culture. Inside weeks, client operations rebounded, and we grew each revenue and headcount that 12 months. Today, with 70% of our team back on-site, we proceed to leverage distant talent, launching Scholars, our global network of material experts for GenAI tuning and validation. Whether it’s a cardiologist or a Spanish mathematician, our high-touch culture attracts and motivates top talent, directly elevating the standard and consistency of our solutions.
In 2023, we acquired Ango.ai, an AI-powered data labeling and workflow automation platform, to drive the subsequent generation of AI data tools. This pivotal move merged iMerit’s domain expertise with Ango’s advanced tooling, expanding our capabilities in radiology, sensor fusion, and GenAI fine-tuning. We still work with customer tools as well, but many latest clients at the moment are onboarded on to Ango Hub, drawn by its user-friendly workflows and robust security, that are essential requirements in our industry.
Enterprises consistently tell us they’re searching for one of the best of each worlds: expert human insight to make sure quality, combined with a secure, scalable platform that delivers automation and analytics. Combining forces with Ango delivers exactly that, uniquely positioning us to fulfill the complex demands of today’s most ambitious AI projects and scale with confidence.
iMerit is deeply involved in advanced domains like autonomous vehicles, medical AI, and GenAI. What are a number of the unique data challenges you face in these sectors, and the way do you address them?
Data-related tasks typically account for nearly 80% of the time spent on AI projects, making them a critical component of the pipeline. The info-centric a part of AI will be time-consuming and expensive if not handled appropriately and scalably.
Data quality, and particularly the avoidance of egregious errors, is important in mission critical sectors that we operate in. Whether it’s a perception algorithm or a tumor detector, clean data is important within the training-to-validation loop.
Exception handling is disproportionately precious. Human insight into why something is outside the norm or why a scenario broke the model creates massive value in making the model more complete and robust.
As well as, context windows have gotten larger. We’re summarizing clinical notes of a complete doctor-patient consultation and analyzing anomalies in MRIs based not only on the image but in addition on the patient’s medical context. Material experts need to arrange rubrics to research the info accurately and ensure quality.
Safety, privacy, and confidentiality are hot button topics. Our Chief Security Officer has to safeguard against unauthorized access, deletion, and storage of information. Infosec protocols like SOC2, HIPAA and TISAX, have been major areas of investment for us.
Finally, our engineers and solution architects are continually working on custom integrations and reports in order that unique customer needs are reflected within the last mile. A one-size-fits-all approach doesn’t work in AI.
You’ve spoken about combining robotics and human intelligence as a safer path for AI. Are you able to expand on what that workflow looks like in practice—and why you suspect it’s higher than attempting to eliminate AI’s creative divergence?
AI provides scale, meaning that corporations are developing tools to automate lengthy processes traditionally carried out by humans. But humans provide the last mile of flexibility, certainty and resilience. As software-delivered services proceed to proliferate in AI, probably the most successful corporations will effectively mix robotics with Human-in-the-Loop practices (HITL).
We see HITL as a consistent layer in every phase of the AI development and deployment lifecycle, and in addition as a pillar of trust and safety. Consequently, human intelligence shall be essential to course correct if the models fail. These critical applications will need the human mind to find out what changes will should be made. That is where HITL services will change into much more significant as we integrate AI into production and field operations.
Your Ango Hub platform blends automation with human-in-the-loop expertise. How does this hybrid model improve data quality and model performance in production AI systems?
AI and automation provide scale and speed, while humans provide nuance, insight and oversight. HITL ensures human involvement at critical junctures within the AI lifecycle – ensuring high-quality inputs, validating outputs, identifying edge cases, fine-tuning models for domains, and providing contextual judgment. Humans help ensure accuracy by reviewing and verifying outputs, catching hallucinations or logic errors before they cause harm. In addition they provide oversight in ethically sensitive or high-risk contexts where LLMs shouldn’t make final calls. More importantly, human feedback fuels continuous learning, helping AI systems align more closely with user goals over time.
HITL takes many forms. Human experts engage in targeted annotation, apply complex reasoning to edge cases, and review AI-generated content using structured QA interfaces. Quite than evaluating every decision, contextual escalation systems are sometimes implemented. These systems route only low-confidence outputs or flagged anomalies to human reviewers, balancing oversight with efficiency.
One other critical use of HITL is fine-tuning AI agents via Reinforcement Learning from Human Feedback (RLHF). Human reviewers rank, rewrite, or provide feedback on agent responses, which is particularly necessary in sensitive domains like healthcare, legal services, or customer support. In tandem, scenario-based testing and red teaming allow human evaluators to check agents under adversarial or unusual conditions to discover and patch vulnerabilities pre-deployment.
AI’s full potential is realized only when humans remain within the loop, guiding, validating, and improving each step. Whether it’s refining agent outputs, training evaluation loops, or curating reliable data pipelines, human oversight adds the structure and accountability AI must be trusted and effective.
With Generative AI tools evolving rapidly, how is iMerit staying ahead in providing evaluation, RLHF, and fine-tuning services?
We recently launched the Ango Hub Deep Reasoning Lab (DRL), a unified platform for Generative AI tuning and interactive development of chain-of-thought reasoning with AI teachers. Our DRL enables real-time, turn-by-turn processes and evaluation based on human preferences, resulting in more coherent and accurate model responses to complex problems.
Advances in GenAI models and application development highlight the worth of unpolluted, expert-created, validated data. With the Ango Hub DRL, experts can test models, discover weaknesses, and generate clean data using chain-of-thought reasoning. They interact with the models in real-time and send prompts and corrections back step-by-step in a single interface.
Leveraging iMerit Scholars, the Ango Hub DRL refines model reasoning processes. It leverages iMerit’s extensive experience with HITL workflows. Experts design multi-step scenarios for complex tasks, similar to creating chain-of-thought prompts for advanced math problems. iMerit Scholars review outputs, correct errors, and capture interactions seamlessly. The magic shouldn’t be in onboarding large numbers indiscriminately. The perfect Mathematicians aren’t necessarily one of the best teachers. One also shouldn’t treat a cardiologist like a gig employee. The fitment and training of subject experts to think within the ways in which profit the model training process probably the most, in addition to the engagement, make the difference.
What does “expert-in-the-loop” mean within the context of fine-tuning generative AI? Are you able to share examples where this human expertise significantly improved model outputs?
Expert-in-the-Loop combines human intelligence with robotic intelligence to advance AI into production. It involves human experts who validate, refine, and enhance the outputs of automated systems.
Specifically, expert-led data annotation ensures that training data is accurately labeled with domain-specific knowledge, thereby improving the precision and reliability of predictive AI models. By reducing biases and misclassifications, expert-driven annotation enhances the model’s ability to generalize effectively across real-world scenarios. This leads to AI systems which are more trustworthy, interpretable, and aligned with industry-specific needs.
For instance, after acquiring a big corpus of medical data, an American multinational technology company needed to guage the info to be used in its consumer-facing medical chatbot to make sure protected and accurate medical advice for users. Turning to iMerit, they leveraged our extensive network of US-based healthcare experts and assembled a team of nurses to work in a consensus workflow with escalations and arbitration provided by a US Board Certified physician. The nurses began by evaluating the knowledge base featuring definitions to evaluate accuracy and risk.
Through edge case discussion and guideline revision, the nurses could reach consensus in 99% of cases. This allowed the team to revise the project design to a single-vote structure with a ten% audit, thereby reducing project costs by over 72%. Working with iMerit has enabled this company to repeatedly discover ways to scale medical data annotation ethically and efficiently.
With over 8,000 full-time experts worldwide, how do you maintain quality, performance, and worker development at scale?
The definition of quality is at all times tailored to every client’s specific use case. Our teams collaborate closely with clients to define and calibrate quality standards, employing custom processes that ensure every annotation is rapidly validated by material experts. Consistency is vital to the event of high-quality AI. That is supported by high worker retention (90%) and a robust deal with production analytics, a key differentiator within the design of Ango Hub, shaped by every day user input from our team.
We continually put money into automation, optimization, and knowledge management, underpinned by our proprietary iMerit One training platform. This commitment to learning and development not only drives operational excellence but in addition supports long-term profession progression for our employees, fostering a culture of experience and growth.
What advice would you give to aspiring AI entrepreneurs who need to construct something meaningful—each in technology and in social impact?
AI is moving dizzyingly fast. Transcend the tech stack and hearken to your customers to know what matters to their business. Understand their appetite for speed, change and risk. Early customers can try things out. Greater customers have to know that you just are here to remain and that you’re going to proceed to prioritize them. Set them comfortable along with your proactive approach towards transparency, safety and accountability.
Moreover, rigorously select your investors and board members to make sure alignment on shared values and concerns. At iMerit, we experienced significant support from our board and investors during difficult times similar to COVID-19, which we credit to this alignment.
The important thing qualities that contribute to an entrepreneur’s success within the tech industry transcend taking risks; they involve constructing a profitable, inclusive company.
