The Human Touch in Conversational AI

Our voice AI detects emotional cues in real-time, adapts its communication style to match the user's personality, and presents dynamic visual interfaces alongside voice. Traditional voice bots use scripted responses with flat delivery and zero awareness of user feelings. We build conversational AI that truly listens -with sub-1.5 second latency, emotion sensing, and adaptive persona mirroring.

Our production-grade voice pipeline achieves sub-1.5 second end-to-end latency (STT → LLM → TTS), making conversations feel instant and natural. We use Pipecat with Deepgram Nova-3 for speech-to-text, optimized LLM routing, and ElevenLabs Turbo for text-to-speech.

Yes. Our enterprise-grade guardrails include audit logging, access controls, encryption, and compliance features for HIPAA, SOC 2, and industry-specific requirements. We've deployed voice AI for healthcare intake systems with full crisis detection and human escalation protocols.

We build voice AI solutions for healthcare (patient intake, therapy support), edtech (AI tutors and mentors), customer support (emotion-aware agents), sales (adaptive onboarding), and enterprise applications. Our emotion detection and persona mirroring work across any domain requiring human-like conversations.

Our voice analytics engine processes audio signals to detect emotional state with sub-second latency. It identifies hesitation through pauses and filler words, tracks vocal energy and pitch variation for confidence levels, measures engagement through response quality, and detects stress through voice tremor and breathing patterns.

Persona mirroring means the AI dynamically adapts its interaction style based on the user's personality and preferences. It adjusts between direct/detailed, formal/casual, and technical/simplified communication. When frustration is detected, it slows down, validates feelings, and offers support before continuing.

Yes! We've open-sourced NesterConversationalBot, a production-tested framework for building voice-first AI applications with ~1-1.5 second response times, multilingual support including Hinglish, and RAG integration. It's available on GitHub.

Our stack includes Pipecat for voice pipeline orchestration, Deepgram Nova-3 for speech-to-text, ElevenLabs Turbo for text-to-speech, Daily.co for WebRTC, GPT-4 Turbo/Gemini/Claude for LLM processing, and Zep/Graphiti for memory systems. We also use MSP-PODCAST model for emotion detection.

Yes. We've built a human-like memory architecture with four types: short-term memory (active conversation context), long-term memory (persistent user profiles), episodic memory (specific memorable moments), and semantic memory (extracted facts and entities). The AI naturally 'remembers' without being told.

Our multi-layer guardrails system includes: input guardrails to block harmful content, content moderation for query classification, crisis detection with escalation protocols, output guardrails for compliance validation, and configurable topic boundaries. This ensures safe, controlled AI interactions.

Project timelines vary based on complexity. A basic voice assistant with emotion detection can be prototyped in weeks. Enterprise solutions with full guardrails, compliance, and custom integrations typically take 2-4 months. We focus on production-ready deployments, not demos.

Absolutely. Our voice AI solutions integrate with CRMs, databases, ticketing systems, healthcare platforms, and custom APIs. The architecture is data-agnostic -it works with RAG, direct APIs, or any data source you have.