Sponsor: iMerit

Great AI products need to solve the data woes of enterprises reliably and consistently. Sudeep George, VP of Engineering at iMerit, deep dives into the importance of high-quality data in building effective machine learning models on the I M Possible podcast with Eddie Avil.

Watch iMerit’s VP of Engineering, Sudeep George, discuss:

• How to ensure that data is high-quality
• The role of ML DataOps and how it helps with AI model performance
• How enterprises can accelerate their AI development and deployment
• How AI can be made more accessible
• Cutting-edge AI innovation & its social impact

To support a cutting-edge demo in autonomous mobility, a leading AI company partnered with iMerit to power its Vision-Language-Action (VLM) model with high-quality, expertly curated training data. iMerit’s team of domain specialists rapidly annotated real and synthetic driving scenarios, improving model accuracy, explainability, and safety—while delivering a 50% boost in task efficiency.

This case study highlights how iMerit’s tailored data annotation and VLM expertise enabled the client to surpass demo goals, enhance model transparency, and accelerate innovation in autonomous vehicle technology.

To ensure accurate and safe generative outputs from its healthcare chatbot, a leading U.S. tech company partnered with iMerit to evaluate and refine a new medical dataset using Retrieval-Augmented Generation (RAG). Leveraging a team of nurses and a board-certified physician, iMerit developed a scalable, expert-driven workflow to audit medical definitions for accuracy, safety, and usefulness—achieving 99% consensus and over 72% in project cost savings.

This case study highlights how iMerit's domain-specific RLHF and human-in-the-loop evaluation helped the client reduce risk, improve quality, and scale responsibly in a high-stakes healthcare environment.

A leading professional social networking platform partnered with iMerit to enhance its AI-powered co-pilot through Reinforcement Learning from Human Feedback (RLHF). The goal was to ensure that the co-pilot delivers accurate, coherent, and responsible responses tailored to users' job-related queries. iMerit provided expert annotation and response evaluation services, helping align the assistant’s output with the platform’s values and professional tone.

By leveraging a skilled team and customizable tools, iMerit improved the assistant’s conversational quality, boosted user satisfaction, and upheld ethical standards. The collaboration led to measurable gains in responsiveness, relevance, and user engagement—making the AI co-pilot a more effective and trusted digital assistant.

In the evolving world of AI development, ensuring alignment between machine behavior and human intent is critical. This blog explores how consensus logic and escalation workflows can significantly enhance the effectiveness of Reinforcement Learning from Human Feedback (RLHF). By aggregating multiple human judgments and escalating ambiguous cases to expert reviewers, these strategies reduce bias, increase reliability, and improve the overall trustworthiness of AI systems.

As vehicles advance toward autonomy, in-cabin monitoring systems are becoming essential for enhancing safety, comfort, and compliance. These AI-driven systems track driver alertness, passenger behaviour, and environmental factors—transforming the driving experience and meeting growing regulatory demands. With the market projected to soar past $3.2B by 2031, the need for smart, reliable monitoring has never been greater.

iMerit supports this evolution with high-quality data annotation via its Ango Hub platform, combining predictive AI with expert oversight. This ensures automotive companies can train accurate, bias-free models for use cases ranging from driver fatigue detection to child safety—driving innovation in next-generation vehicle intelligence.

Generative AI chatbots are reshaping the healthcare industry by streamlining operations, enhancing patient care, and offering round-the-clock support. From symptom checking and appointment scheduling to chronic disease management and mental health assistance, these AI-powered assistants provide scalable, accessible solutions for patients and professionals alike. As technology evolves, so does their potential—integrating with wearables, delivering personalised care, and supporting complex diagnoses with impressive accuracy.

Despite the promise, challenges like data privacy, regulatory compliance, and the need for expertly annotated medical datasets remain. iMerit addresses these issues head-on with secure, HIPAA-compliant data solutions, expert-driven annotation services, and domain-specific reinforcement learning support. With its proven track record and deep expertise in healthcare AI, iMerit is empowering organisations to build accurate, ethical, and scalable AI chatbots that are shaping the future of patient-centred care.

In today’s fast-evolving AI landscape, enterprises face challenges in scaling machine learning workflows from development to production. This blog explores how the integration of Snowflake and iMerit's Ango Hub streamlines AI data pipelines—combining robust cloud data warehousing with advanced multimodal data annotation. Learn how this powerful synergy accelerates model training, enhances data quality, and enables seamless MLOps with automation and human-in-the-loop precision.

Discover how Snowflake and Ango Hub transform raw data into AI insights through seamless integration of cloud storage, automated annotation, and expert validation—empowering scalable, high-quality AI development.