Emotion sensing
Audio prosody weighted with text sentiment so frustration, hesitation, and distress are caught from how callers speak, not only from what they say.
Voice AI that
holds the
conversation.
Most voice agents fail in the runtime, not in the model. A production voice agent is the model plus the execution layer around it.
What we build
Four capabilities branching from a single voice pipeline.
Persona mirroring
Tone, pace, formality, and address adapt per caller. Direct or detailed, formal or casual, brisk or warm.
Adaptive visual UI
Voice interactions paired with dynamic UI components. Cards, lists, forms, and structured interfaces generated in real time alongside the conversation.
Dual-graph memory
Per-user graph plus an agent-global graph. Short-term, long-term, episodic, semantic. The voice remembers across sessions, days, and weeks.
How we build voice
Voice ships a different set of problems than chat. Turn boundaries, silence behaviour, barge-in, audio-domain emotion, and TTS playback timing shape the architecture as much as the STT, LLM, and TTS providers.
Gather & Discover
Map the voice tax before any code. Turn boundaries, silence, barge-in, audio emotion, TTS timing. Latency budget and human-handoff set per channel.
Conversation map and trust boundariesArchitect & Build
Turn-taking, silence behaviour, barge-in, streaming response timing, and emotional context designed into the runtime itself. Lifecycle keyed on TTS-stopped, not LLM-end, so the interaction feels continuous, not transactional.
Production voice pipeline, end to endHarden & Launch
Calibrate per channel: VAD, silence ladders, TTS pacing, barge-in latency. Persona validated under real callers. Latency held end to end until the seams vanish.
Production-ready voice systemInside the voice runtime
One turn end to end. Critical stages run in parallel, not in sequence. By the time the user stops speaking, most of the prompt is assembled, memory is fetched, and emotion is read from prosody. Latency is not infrastructure. Latency is behaviour.
Memory speculative-fetches against partial transcripts. Affect computes prosody and text sentiment in parallel.
Persona stays the same. The six other layers recompose around it. Tool surface is scoped to the current phase.
LLM is fired against the latest partial transcript with version tagging. Discard rate on revision: 3 to 5%.
Surface chosen per turn. UI elements stream into the page progressively, in step with speech, not as a block.
What the
voice layer
changes
Voice that lands as conversation, not as a phone tree. Four shifts every Nester voice engagement is built to deliver.
Reduce contact-center overhead
Voice agents absorb repetitive intake, triage, scheduling, and routing work that traditionally moves across queues, shifts, and disconnected teams.
Accelerate caller resolution
Calls that used to need transfers, callbacks, or hold time resolve in a single turn. Sub-second response keeps the conversation moving, not the caller waiting.
Trust built into the conversation
Emotion sensing, persona consistency, escalation paths, and compliant retention are designed in from the first call, not added once production issues appear.
Scale caller capacity
Inbound voice volume handled without proportional growth in headcount, shift coverage, training cycles, or vendor overhead.
Built on the
voice stack you already use
STT, TTS, LLM, transport, telephony. Pluggable across every layer. Whatever your team has chosen, we work with it.
OpenAI
Claude
Gemini
DeepSeek
Grok
OpenAI
Claude
Gemini
DeepSeek
Grok
Deepgram
ElevenLabs
Whisper
Cartesia
Resemble
LiveKit
Pipecat
Twilio
Deepgram
ElevenLabs
Whisper
Cartesia
LiveKit
Twilio
Zep
Graphiti
Neo4j
Mem0
Postgres
MongoDB
Zep
Graphiti
Neo4j
Mem0
Postgres
MongoDB
Operating questions
What teams ask before voice goes into production.
Do you replace our existing voice stack?
No.
We work across the infrastructure and providers companies already use. STT, TTS, telephony, orchestration, observability, and runtime systems.
How quickly can systems reach production?
Initial systems typically reach live environments within weeks, then evolve through pressure-testing, calibration, and production hardening under real caller conditions.
How do you handle reliability in production?
Voice systems are evaluated against latency, interruption handling, escalation logic, emotional variance, silence behaviour, and operational edge cases before wider rollout.
Can human teams stay in the loop?
Yes.
Escalation paths, human handoff, supervision, and trust boundaries are designed into the system from the beginning.
Do you rely on proprietary infrastructure?
Voice engagements build on internal runtime, orchestration, memory, observability, and reliability infrastructure developed across previous production systems.
That allows faster iteration around latency, turn-taking, escalation, emotional adaptation, and production hardening without forcing companies into proprietary stacks.
Do you work with regulated environments?
Yes.
We have worked on systems requiring HIPAA-aware workflows, auditability, retention controls, and governed escalation behaviour.
What happens after launch?
Most engagements continue through calibration, optimization, reliability tuning, and expansion into adjacent workflows as usage patterns emerge.
The voice is no longer the interface.
It becomes part of the operation itself.