Token Optimization
Large language models often generate unnecessary tokens, increasing API costs and latency. The Andive Token Optimization layer filters redundant content and ensures that every token used contributes directly to the final result.
Prompt Compression
Incoming prompts are cleaned and compressed to remove redundant information before they are sent to the model.
Response Filtering
Generated responses are analyzed and trimmed to remove filler tokens that do not add value to the result.
Cost Efficiency
By reducing unnecessary tokens, the system lowers API costs while maintaining the same output quality.
Performance Gain
Fewer tokens mean faster responses and lower latency for real-time AI applications.