AI/ML Dataset Support
AI/ML Dataset Support in AI Annotation refers to the ability of annotation platforms or tools to manage, process, and facilitate the labeling of datasets used for training and evaluating artificial intelligence (AI) and machine learning (ML) models.
Dataset Design & Planning
At Jeenish AI Solutions, we offer dataset design and planning services to help AI teams build high-quality, purpose-driven training datasets from the ground up. This includes defining annotation types, sampling strategies, class distributions, and edge case inclusion.
Whether you're building a computer vision model or a multilingual chatbot, we ensure your dataset captures real-world diversity and aligns with your use case goals. For example, designing a balanced dataset of pedestrian actions across varied weather conditions for autonomous vehicles.
Our expert team collaborates closely with you to develop scalable, domain-specific data strategies that minimize bias and maximize model performance.
Request a DemoData Augmentation & Cleanup
At Jeenish AI Solutions, we provide data augmentation and cleanup services to enhance dataset diversity and improve AI model robustness. Augmentation involves synthetically expanding your dataset using techniques like image rotation, noise addition, or text paraphrasing—without collecting new data.
Cleanup ensures that existing datasets are free of errors, inconsistencies, and duplicates, making them more reliable for training. For instance, we remove mislabeled images in vision datasets or correct formatting issues in multilingual text.
These processes are essential for reducing bias, improving generalization, and accelerating AI development across industries like autonomous driving, retail, and NLP.
Reinforcement Learning from Human Feedback (RLHF)
At Jeenish AI Solutions, we support Reinforcement Learning from Human Feedback (RLHF), where human evaluators score or rank AI-generated outputs to guide model improvement. This helps align large language models (LLMs) with human preferences, safety standards, and contextual appropriateness.
For example, given multiple chatbot replies to a query, our trained reviewers rank the responses based on relevance, helpfulness, and tone. These rankings then inform reward models used in reinforcement learning loops.
RLHF is vital for fine-tuning models like chatbots, content assistants, and generative AI tools—ensuring more trustworthy, useful, and aligned AI behavior in real-world applications.
Request a DemoLabel Validation & Continuous QA
At Jeenish AI Solutions, we offer label validation and continuous quality assurance (QA) to ensure your annotated data remains accurate, consistent, and model-ready. This involves a second layer of review where expert validators audit annotations for errors, inconsistencies, or ambiguity.
We use QA sampling, inter-annotator agreement checks, and feedback loops to maintain high-quality standards across datasets—whether image, text, audio, or video. For instance, we validate bounding boxes in autonomous vehicle datasets or correct mislabeled sentiment tags in multilingual reviews.
This continuous QA process helps prevent model drift, reduce rework, and build high-performing AI systems with reliable, clean data.
Model Fine-Tuning Assistance
At Jeenish AI Solutions, we provide model fine-tuning assistance to help you optimize pre-trained AI models with custom, domain-specific data. This includes preparing curated datasets, formatting inputs, and offering human-in-the-loop feedback to guide the fine-tuning process.
Whether you're adapting an LLM for legal document summarization or a vision model for medical imaging, we ensure your data is aligned, labeled, and QA-verified for optimal training outcomes.
Our team works closely with your ML engineers to iterate quickly, troubleshoot edge cases, and deliver tuned models that perform reliably in real-world applications.
Request a Demo