WhyLabs AI Control Center (also known as the WhyLabs Platform) is now an open source project!
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
Safeguard LLMs
- Control which prompts and responses are appropriate in real-time
- Validate how your LLM responds to known prompts both continually as well as ad-hoc
- Observe prompts and responses at scale by extracting key telemetry data and compare against smart baselines over time
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
