25-40% reduction in ingestion latency
Poor data labeling leads to low model accuracy and high costs:
30%
of ML models underperform due to inconsistent training data.
80%
of the time is spent on training data cleansing, delaying model deployment
40%
AI/ML budgets is spent on data remediation driving up struggle with data drift and labeling inefficiencies.
Agivant INTELLI-TAG: Smarter ML Tagging
INTELLI-TAG ensures scalable, precise, and automated labeling for machine learning models.
How INTELLI-TAG Solves These Challenges:
AI-Augmented Tagging
Auto-labeling improves precision
Adaptive Taxonomies
Reduces bias and enhances consistency
Reinforcement Learning Integration
Improves accuracy over time
Scalable Data Structuring
High-throughput processing for AI pipelines
High-quality training data at scale with minimal human effort.
Business Outcomes
3-5x faster annotation cycles
20-50% improvement in model performance
30% cost reduction in data labeling
Solution Offerings – Explore Agivant INTELLI-TAG
AI-Driven Auto-Labeling Faster, smarter data annotation
Bias-Reduced Adaptive Taxonomies Ensures model consistency
Integrated Reinforcement Learning Continuous accuracy improvement
