As
teams across industries
adopt AI to solve the most pressing business problems, WhyLabs enables them to operate with certainty by providing model monitoring, preventing costly model failures, and facilitating cross-functional collaboration.
E-commerce
- Recommendations
- Personalization
- Inventory management
Logistics
- Warehouse optimization
- Supply chain planning
- Peak hours prediction
Cybersecurity
- Network traffic classification
- Network anomaly detection
- User/machine behavioral analysis
Real Estate
- Personalization
- Property Market Value forecasting
- Advanced Property Analysis
Healthcare
- Discharge planning
- Capacity planning
- Medical record extraction
Telecommunications
- Customer churn prediction
- Personalization
- Fraud detection
A single platform for any use case
Power AI operations for structured and unstructured data
Ranking and Recommendations
- Ensure that the model makes recommendations accurately and consistently
- Prevent model decay caused by concept drift and data quality issues
- Increase ROI for marketing and advertising spend
Forecasting
- Equip your team with tools that alert about forecasting bias
- Prevent forecasting bias caused by data quality and seasonal drifts
- Increase ROI by continuously improving model performance across edge cases
Document understanding
- Ensure consistent performance across information extraction pipelines
- Prevent pipeline failures caused by concept drift and data quality
- Maintain trust in model accuracy with customers and end users
Image understanding
- Prevent bias and failures caused by data drift and data quality
- Ensure consistent performance across all data segments and hardware types
- Maintain trust in model accuracy with customers and end users
Running AI is a Team Sport
Surface the right insights to the right team members
Data Scientists & ML Engineers
- Spend less time building tools, spend more time building and improving AI applications
- Speed up the model development process by automating training data validation with WhyLogs
- Equip production pipelines with state-of-the-art data logging and monitoring techniques
- Prevent model failures by catching data quality, data drift and concept drift problems proactively
Product Managers & Team Leads
- Gain customer trust by catching model failures before they affect user experience
- Reduce technical debt and maintenance costs by streamlining model operations
- Connect model performance metrics to core business KPIs and track everything through a unified interface
Executives & Senior Leadership
- Gain and retain trust with stakeholders by eliminating failures and undesirable AI predictions
- Realize ROI from AI investment by reducing model development cycles and technical debt
- Gain transparency into data and model health through a unified, intuitive interface
