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Empower Your Gen AI Models for Real-World Success with Enhanced Data Feeding.
The lack of robustness in Generative AI, particularly LLMs, causes reliability issues.
Generative AI models are challenged with reliability and accuracy, potentially leading to unpredictable outcomes and biases.
Uncertainties of AI models cause negative impact for brand values .
The models' outputs can be unreliable and potentially generate inaccurate information, biased outputs, or undesirable results. These uncertainties and risks can damage a company's reputation and public trust.
Model Evaluation for LLMs' Continuous Learning
Co-one's Model Evaluation Solutions for Generative AI provide a framework for assessing uncertainty in black box models, integrating feedback ranking via Reinforcement Learning, and offering customizable APIs to facilitate continuous learning.
How Co-one Evaluate and Improve Generative AI Models
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Fallback Evaluation
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Automated Human Feedback for continuous model improvement.
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Continuous Learning
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Connect your AI model.
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Define accuracy metrics
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Define harmful topics
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Onboarding to Evaluation Platform
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Detect uncertainty for LLMs.
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Evaluate model weaknesses at a domain level.
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Uncertainty Measurement
This project involves a study conducted by Co-one for Türkiye İş Bank's chatbot, Maxi, focusing on the chatbot's model evaluation.
Co-one collaborated with Türkiye İş Bank to enhance the performance and accuracy of Maxi, the bank’s AI-powered chatbot, in understanding and responding to customer queries. Through comprehensive text annotation and intent classification, we aimed to optimize Maxi’s natural language processing capabilities, ensuring a seamless customer experience and efficient query resolution.
Chatbot Model Evaluation and Intent Generation
Use Cases & Solutions
We provide a model evaluation services with an uncertainty measurement framework to ensure trustable AI.
A Glimpse into Our Growth
3K
TRUSTED CUSTOMERS
1M
REPORTS GENERATED
32K
TOKEN ACCESS
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SUPPORTED LANGUAGES