Test Your Virtual Assistant
with out-of-the-box handy tools
Interact with your virtual assistant in real-time before you publish – to test recognition, performance, and flow of the conversation for various scenarios. The XO Platform’s in-built tools enable you to try different variations of user prompts to ensure the NLP interpreter is accurately processing these utterances.
UTTERANCE TESTING
Examine how VA responds
to a variety of user inputs
Testing virtual assistants with a good sample of utterances gives invaluable insights into the performance.
Understand how the Kore.ai XO Platform process the user request through its’ multi-engine. Check if it identifies the right intent and entities for a given utterance; fine-tune the training based on the expected response.
BATCH TESTING
Improve the accuracy
by testing the performance of the ML model
Delineate how well the virtual assistant understands user utterances using predefined test suites available in the builder or custom test suites. Execute a series of tests to get a detailed statistical analysis and gauge the performance of the ML model; view NLP metrics like precision, recall & F1 scores.
Use the Platform’s visualization tools, confusion matrix and K-fold testing to understand the assistant usage patterns, conversation flows and drop-off points.
Further, drill down results to intent and test case level analysis. Tag specific test case results that need follow-up actions and collaborate with your team to improve the performance.
CONVERSATION TESTING
Optimize Performance
by testing the recordings
Validate the assistant’s skills end-to-end to ensure the best experience for the customers and improve ROI.
The Conversation Testing tool allows you to simulate conversation flows. It lets you validate the virtual assistant’s interaction flows across dialog and knowledge graph intents. Create different test cases to analyze the results at a later date.
EXPLAINABLE AI
Unlock the Black Box
with a fully transparent NLP engine
The Kore.ai XO Platform provides complete transparency into intent identification and entity extraction with explanations about each query response.
The debug log gives you info on what is happening at the backend – you see the NLP, logs, session context, and variables. Unlock the “black box” of machine learning and use the data to analyze the root cause of various failure scenarios.
Health & Monitor
Goal-Driven Framework
for flawless IVA Training & Testing
A successful IVA means a thoroughly trained and tested one. In this release, you can use our ‘Goal-driven’ NLU Performance Monitoring framework to analyze batch test results and improve IVA training and testing.
Get a 360-degree view of the NLP training provided to the virtual assistant by seeing the overall Accuracy, F1 score, Precision, and Recall. With an in-depth analysis of the test coverage and data results, achieve close to an error-free IVA training. Get extra help to enhance the training for incorrect patterns, short utterances, and incorrect entity annotations.
