Understand the 'why' behind AI decisions. XeeAI ensures clarity and transparency by explaining every AI decision in a way you can trust.
See how our AI works—get answers, and understand the ‘why' behind them.
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Summarize this text: The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism.
Type your input. Our goal isn't just to answer it, but to help you understand why the AI responded the way it did.
Summarize this text: The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism.
XeeAI
The AI will responds to your input whilst providing an explanation of its decision.
LIME Explanation
A bar graph where you can see the most influential words that you used in your input.
How do neural networks make decisions?
Neural networks make decisions through a process of weighted connections between artificial neurons. They learn by adjusting these weights during training, allowing them to recognize patterns in data. When presented with new inputs, they process this information through multiple layers, with each neuron applying an activation function to determine its output.
Token Weight Analysis
Token weights showing influence of each word on the response:
The tokens "neural" (1.79), "networks" (1.68), and "decisions" (1.62) have the highest weights, indicating they most strongly influenced the model's response.
See which parts of your input influenced the AI's response most.
Visualize how the model interprets your queries with local explanations.
Modern AI systems—especially large language models—can generate highly convincing outputs, yet often operate as black boxes, leaving users unsure how or why certain answers are produced. This lack of transparency raises serious concerns around trust, accountability, and fairness.
Explainable AI (XAI) addresses this challenge by making model decisions understandable to humans. By highlighting which parts of an input most influenced the output, users can better assess the reliability, bias, and rationale behind AI-generated responses.
Our project integrates XAI techniques like LIME (Local Interpretable Model-agnostic Explanations) and token-weight analysis to make the inner workings of our chatbot transparent and interpretable. We believe this is essential for building ethical, responsible AI systems—especially in contexts like education, law, healthcare, and research.
"Explanations are necessary for trust. Without them, users are left to guess or blindly follow AI decisions."
— Ribeiro et al., "Why Should I Trust You?": Explaining the Predictions of Any Classifier (2016)
By prioritizing explainability, we move toward AI systems that are not just powerful, but also accountable, fair, and human-aligned.
Our explainable AI platform sets new standards for transparency, trust, and usability in artificial intelligence.
Get powerful language model responses with explanations anyone can grasp—no technical deep dive required.
By surfacing how inputs affect decisions, our AI helps identify and reduce unintended bias.
Interactive charts and highlights make it easy to understand model behavior at a glance.
See why the AI responds the way it does, with clear token-weighted visualizations powered by LIME.
XeeAI let's you see under hood of AI.
Meet the creators of XeeAI!
AI Engineer && Full-Stack Developer
Full-Stack Developer && UI/UX Designer
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Full-stack Developer
