01Live support
Keep the intelligence leg out of the caller’s way.
Place the model near the media region and customer systems it depends on, reducing an avoidable network hop inside a latency-sensitive conversation.
Industry: distributed model runtime for voice agentsSee the inference path
Voice-agent infrastructure
Place voice intelligence closer to every conversation.
Keep your call engine, STT, TTS, tools, and agent experience. NYR places your SLMs and LLMs near the media region and customer systems they serve, with a local profile for every market.
For voice AI platforms and enterprise voice products running support, scheduling, sales, triage, routing, and multilingual workflows.
01Shorter model pathRemove an avoidable inference hop
02Context in boundsProcess approved data closer to source
03Local behaviorTune language, policy, and tools by market
04Serving resiliencePlace across approved healthy capacity
Where the model path changes the call
One voice product.
A better fit for every caller.
Voice platforms already orchestrate the conversation. NYR gives the intelligence underneath a distributed runtime, so the model can be closer, locally tuned, and placed on capacity approved for that customer.
02Multilingual agents
Make every market sound like it belongs there.
Give each location its own language, vocabulary, prompt, voice workflow, and model version while operating one product and rollout pattern.
03Sensitive workflows
Keep private conversation context in bounds.
Run inference in an approved customer or regional zone so support, healthcare, financial, and identity context does not need to travel to one distant model endpoint.
04Production resilience
Give live calls an approved serving path.
Use health-aware placement and failover policies across approved capacity, while your voice platform keeps control of telephony, turn-taking, STT, TTS, and handoffs.
HAVE A LIVE CALL PATH?Bring one conversation that feels too slow.
Map one live call path We’ll map the media region, model, tools, customer data, and fallback path.
The voice intelligence layer
Your platform runs the call.
NYR runs your models underneath.
Add a distributed inference path without rebuilding telephony or conversation logic. The voice platform sends a model turn to NYR; NYR selects the approved local profile and healthy runtime, then streams the result back into the existing pipeline.
Keep your call engine
Your platform continues to own telephony, media, turn-taking, transcription, speech, tools, and the agent experience.
Bring your intelligence
Deploy your signed SLM, LLM, adapter, or approved model endpoint through one NYR-managed runtime pattern.
Profile every market
Attach local language, vocabulary, tools, policy, data boundaries, and capacity rules without forking the product.
Route each model turn
NYR selects an approved healthy runtime close to the conversation and returns the model stream to your existing voice pipeline.
Why distributed voice inference
Every model hop is part
of the caller’s wait.
DISTANT MODEL ENDPOINT
One global runtime has to fit every call.
- PATHModel turns take a longer network route
- CONTEXTConversation and tool context travels farther
- FITGlobal defaults miss language and domain nuance
- FAILUREOne serving region carries the model experience
The conversation is real-time. The model path is not.
WITH NYRLOCAL MODEL PATH
Place intelligence near the conversation.
- PATHInference runs closer to the media region and tools
- CONTEXTApproved data can stay inside its operating zone
- FITEach market gets its own model and behavior profile
- FAILUREHealth-aware placement uses approved capacity
A shorter path. A more local agent. One product to operate.
One rollout, many voices
Personalize the runtime.
Do not fork the product.
A support line in New York, a booking agent in Madrid, and a regulated workflow in London can share one product while using different models, vocabulary, tools, and data rules.
ONE PRODUCTNYRversioned rollout
Start with one measurable call path
Prove the model placement
before the full rollout.
Choose one call flow
Bring a production workflow where model delay, context movement, localization, or serving resilience is visible to the product team.
Map the full path
Trace the media region, STT, model, tools, TTS, customer data, policy boundary, and current failure modes before changing placement.
Place and compare
Run the same approved model through a NYR-managed local profile and compare the full conversational outcome, not an isolated benchmark.
START WITH ONE FLOWChoose the first measurable voice rollout.
Plan the voice rollout Bring one production call flow and the markets it serves. We’ll define the first placement and profile.
Voice-agent infrastructure FAQ
What product teams ask before moving the model path.
01Does NYR replace our voice-agent platform?
No. NYR is the distributed model runtime underneath your product. Your platform keeps telephony, STT, TTS, turn-taking, tools, handoffs, observability, and the customer-facing experience.
02Can we run our own SLM or LLM?
Yes. NYR is designed to place and operate your approved models across distributed capacity. Each deployment can select a model version, local adapter, prompt profile, and operating policy appropriate for that customer or market.
03Does local placement guarantee a specific voice latency?
No single number describes the full voice path. Turn detection, transcription, model inference, tools, speech generation, networks, and prompt size all contribute. NYR targets the model-serving network leg by placing inference closer to the media region and systems it serves.
04Can the same voice product behave differently by location?
Yes. A shared rollout can carry separate profiles for language, domain vocabulary, customer policy, integrations, model choice, data boundaries, and approved capacity in each location.
05Where can voice models run?
NYR is built for approved distributed capacity, including customer-controlled cloud environments, regional zones, and local infrastructure, with placement and failover policies defined for the workload.
Bring a real voice workload
Where does your model sit in the caller’s path?
Bring the voice pipeline, model, tools, customer locations, privacy requirements, and current serving path. We will map the first distributed deployment around a real conversation.
Map my live call path