Optimize your AI and ML workflows with Process-Smart’s end-to-end training data services. Our experienced annotation specialists manage every step of data preparation, from collection and labeling to validation and quality assurance. By embedding domain-aware professionals into your AI/ML training workflow, Process-Smart enhances dataset accuracy, streamlines workflows, and helps your models learn faster and perform better.
Labeling data so AI understands language, sentiment, and intent. We provide text annotation services, text-NLP data annotation, and machine learning data labeling to support chatbots, search models, and LLM pipelines.
Marking objects, boundaries, and details so AI learns to recognize things. Our computer vision annotation, bounding box annotation, and image annotation services train models for object detection, segmentation, and recognition tasks.
Tracking movement and actions so AI understands what’s happening. Our video annotation services include object tracking, activity labeling, event detection, and scene mapping for motion-based AI models.
Converting and tagging voice data so AI learns to listen. We provide audio annotation services, transcription, intent labeling, and multi-speaker segmentation for speech AI, call analytics, and voice-based applications.
Creating data with question-answer pairs and instructions for better AI responses. This LLM fine-tuning process improves model performance, aligns behavior, and enhances understanding in domain-specific applications.
Ranking AI outputs so it learns what humans prefer. We support RLHF workflows to refine large language models, ensuring they respond more accurately and naturally.
Collecting and transforming web and system data so AI has the structured inputs it needs. Our data harvesting services include website scraping, automated data extraction, and AI-driven data transformations to deliver clean, structured datasets ready for analytics, model training, and system integration.
AI and ML models succeed only when their training data is accurate, clean, and reliable. Our approach focuses on:
This means your models get the training data they need to learn efficiently, reason correctly, and deliver results you can trust.
