Faster AI adoption & AI-assisted Decision Making for Continuous Value
Continuously Harvest Value with Data, Model and Business KPI Observability
Value Realization Journey
An AI Observability Suite for Continuous Value (CV) Delivery
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Data Observability
The quality of data used plays a very important role in the success of AI projects. The right data combined with the right model produces the highest business value.
Data Observability
Acts as a gatekeeper for data quality for your AI models.
Custom and Automated Data Quality Rules
Create data quality rules specific to a use case or leverage the intelligent AI data quality rule generator for making sure that the data is fit to run training/inference.
Trigger training/inference only if data passes all the quality checks.
Save time and effort by making sure the ML model training and/or inferences run only if the data is of expected quality.
Automated DQ Remediation Suggestions
Reduce time to data quality remediation by leveraging our proprietary ADQR.
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Model Observability
How is the model performing? Is there a drift? Do I need to re-train/tune the model? All this and more can be found out and remediated with the help of our model observability suite.
Model Observability
The model monitoring and remediation stack helps organizations analyze performance metrics of AI models, compare them with benchmarks, and log their lineage.
Model Benchmarking
Benchmark the performance of your AI model against heuristic rules and/or the logic of your domain expert and make sure they perform better.
Model Monitoring and Remediation
Continuously monitor the performance and behavior of your AI models and tune if the model performance deteriorates.
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Compliance Observability
A machine learning model in production which is biased against customers of a particular race, gender, age group etc. will do more harm than good. Learn more about how we can ensure the models are responsible.
Compliance Observability
Understand what are the factors based on which predictions are made by your AI models through simple business explanations. Also, make sure your models are fair and do not contain any inherent bias either in data or the model.
Business Explanations
Make the models self-explainable by leveraging Cetas's state of the art explainability module which will help in higher business adoption.
Bias Tracker
Look into your models predictions from any bias point of view to understand the root cause of any bias so you can take the necessary actions to improve fairness.
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Business Value Observability
A machine learning model in production which is biased against customers of a particular race, gender, age group etc. will do more harm than good. Learn more about how we can ensure the models are responsible.
Business Value Observability (Value Meter)
Continuously track the business value generated by your AI models and optimize them to get the most of your AI investments.
Use Case Templates
Leverage Cetas' years of experience in solving complex business problems through AI by quickly on-boarding the value meter for your specific use case through the template registry.
Decision Intelligence
An AI model without feedback tends to go stale quickly. Use the decision intelligence engine to take action from predictions. Get business feedback and improve the model as per the business KPI in real time. Track the financial value generated by your AI models.
Product Highlights
Alerts and Automation
Take advantage of our easy email and Slack integration.
Improve Business Value Every Day
Get continuous feedback from users and model tuning based on business KPI.
Investigate the Root Cause
Go beyond model monitoring and get faster remediation to stop value erosion.
Drive Business Value from Adoption
Get simplified and explainable outputs to enhance adoption. Guide business users in taking actions from AI outcomes, segment level analysis & action and "what if" analysis.
Generate Value & Enhance ROI
Get clear visibility into your ML models and monitor what matters most: Business KPIs
Any Tech, Any Model, Value Delivered
Our product is model & platform-agnostic, runs on-premise or in the cloud, and seamlessly integrates with all your data science and ML tools.
Track Business Value Across the AI-Lifecycle with the Built-in Framework
Enhance business capability by defining and setting up custom KPIs to track impact of data and model quality on business outcomes.