The agent can be configured to support multiple languages. Setting up the right model for a specific language typically takes 2–3 hours.
Accuracy depends on the model used and the complexity of queries. With advanced LLMs like GPT-4.0, the system achieves highly reliable responses and can be fine-tuned with domain-specific data for even greater accuracy.
Running multiple languages (e.g., English and Hindi together) may introduce slightly higher latency, but the system is optimized to minimize delays in real-time interactions.
The agent does not perform tone analysis out of the box, but sentiment analysis and tone detection can be added as needed for specific use cases.
The system works with models comparable to Google or Microsoft voice engines and can switch between them based on requirements.
The agent uses WebRTC frameworks like LiveKit for seamless real-time communication. A custom WebRTC infrastructure can also be implemented, though it may take more setup time.
Yes. The agent supports voice-to-voice communication and can generate responses in natural, human-like voices.
By default, no recordings or voice information are stored. However, data storage can be enabled if your use case requires it.
Strong guardrails ensure that only safe, relevant, and compliant information is shared. Data and prompts are managed securely with enterprise-grade safeguards.
Speaker recognition is not fully supported yet, but the system can be extended to handle it. Voice changes and voice cloning are also possible.
Absolutely. The same stack supports standard telephony, enabling the AI to handle live calls as well as web-based voice interactions.
Yes. The system includes analyzers that can process both spoken voice inputs and text-based inputs, ensuring consistent and intelligent responses across channels.