Voice Agents

Voice agents that resolve, not just respond.

auto-AGENTS™ are voice-native AI built on frontier LLMs. They reason and plan in real time, act across your stack over MCP to finish the job, and recursively improve themselves (RSI) in production, so they resolve the whole call, and get measurably better at it every week.

Book a demo Try a live call

Live Sub-500ms · SOC 2 Type II · HIPAA · 155+ locales · deploys to your telephony

Autonomous resolution

Inference latency

Interactions evaluated

Inference cost ↓

Neural voices

Locales

The hard part, live

One call. Five systems. No humans.

A patient calls to reschedule an appointment and refill a prescription. Watch a single auto-AGENT™ verify identity, read the chart, book with the provider, route the refill and confirm, orchestrating across Salesforce, Epic, scheduling, WhatsApp and ServiceNow over MCP, in real time.

auto-AGENTS™ · Live call · Lakeside HealthSimulated demo

Try the hardest call

A multi-system healthcare call, resolved end to end. Watch the connected systems light up as the agent works.

Connected systems · MCP 0 / 5 active

Salesforce

Epic · EHR

Scheduling

ServiceNow

Twilio · voice

Ready

A scripted demonstration, no real patient data. In production every action runs inside your policies, audited and reversible.

Hear it · real voice

Press call. Hear it resolve.

A quick one with real audio: an auto-AGENT™ handles a billing dispute end to end, reasoning with the caller, acting over MCP, and closing the loop. Turn your sound on.

auto-AGENTS™ · Live callReal audio

Connecting to Northwind Energy…

Try a live call

Press call, an auto-AGENT™ resolves a billing dispute end to end. Turn your sound on.

Call time

00:00

Sentiment

Detected

Actions · MCP

–

latency

–

autoQA

Ready

A demonstration call, no real customer data. Audio generated with ElevenLabs.

The difference

Scripted bots deflect. auto-AGENTS™ resolve.

Most "AI" voice tools are still decision trees with a nicer voice. They follow a script, miss the real intent, and drop the caller into a queue the moment the conversation goes off-path. auto-AGENTS™ reason about what the caller actually needs, act on your systems to finish it, and learn from every call.

Yesterday's voice bots

Contain the caller

Fixed decision trees you hand-build and maintain forever
Deflect and contain, then escalate to a human queue
Break on accents, interruptions and topic changes
Static, the same mistakes, call after call
Sampled QA on ~2% of conversations

auto-AGENTS™

Resolve the conversation

Reason about intent in real time, no flowchart to draw
Resolve end to end by acting on your stack over MCP
Handle barge-in, code-switching and ambiguity gracefully
Recursively self-improve every week (RSI)
autoQA scores 100% of interactions, automatically

What teams automate

Billing & refundsSchedulingOrder statusTier-1 triageOutbound remindersIdentity & verificationCollectionsMember & patient intake

Agent Studio

From intent to a self-improving agent, autonomously.

Give it intent. auto-AGENT synthesizes the agent, flow, prompts, knowledge grounding and guardrails, then proves it out against an adversarial eval suite before a single live interaction. No flowcharts to hand-draw.

1Specify intent 2Synthesize 3Evaluate 4Self-improve · RSI

"We described our hardest queue on a Monday and were resolving live calls by Thursday, no flowcharts, no professional-services project. And it's measurably better every week since."

Director, Contact Center

National insurer · 900+ agents

Declare intent. Choose a frontier voice. Ship.

01 · Specify intent

Set the runtime once, voice or text, your telephony, language, and a neural voice from Azure, OpenAI or ElevenLabs. Drop in a brief or spec and the model drafts the prompts from it. Model-agnostic underneath: route to GPT-5.x, Gemini, Claude or on-prem Llama, with deep settings when you want them.

Create task templateruntime

Type

TextVoice

Voice provider

11ElevenLabs▾

Language

🇺🇸 English (United States) · en-US▾

Final voice code

en-US-Ava : DragonHDLatestNeural

auto-AGENT drafts the welcome & system prompts from your brief.

The agent synthesizes its own reasoning graph.

02 · Synthesis

auto-AGENT reasons over your brief and synthesizes the conversation graph, opening, intake, knowledge-grounded assessment, record lookup, clean endings, each node with a goal, the state it needs, and a recovery policy for ambiguous input. LLM reasoning, deterministic execution.

Inside the engine →

Flow · auto-built4 nodes

auto-AGENT generated 4 nodes · 8 caller values · 1 tool

It runs adversarial evals on itself before shipping.

03 · Evals

Every build is graded against generated adversarial scenarios, the CQB eval suite, scoring intent accuracy, inference latency, hallucination rate and policy adherence. The agent fixes its own regressions and saves them back, gated by human approval.

eval scenarios / build

2.0s

avg inference reply

EvaluationsStandard · v1.0

12/12

scenarios

2.0s

avg reply

11s

slowest

✓Refund under thresholdpassed

✓Escalate over $100passed

✓Unclear answer · recoversfix saved

✓Caller ends earlyhuman approved

RSI: a reinforcement loop that compounds.

04 · Recursive self-improvement

In production, a closed loop runs continuously, resolve, measure, learn, optimize. autoQA scores 100% of interactions, winning policies get promoted, and prompts, routing and TokenTrim™ inference-compression retune themselves. Quality compounds; cost per resolution decays. Every change gated and reversible.

RSI loop · productionlast 12 weeks

Resolution rate

↑ 76%

First-call resolution

↑ 82%

Cost / resolution

↓ 90%

Escalations

↓

Multi-agent orchestration · MCP

One calm voice. A whole team of agents behind it.

While it reasons with the customer, the agent dispatches specialist sub-agents and invokes your systems over the Model Context Protocol, retrieving, deciding, acting. After the interaction, background agents summarize, sync the CRM, score it, and trigger follow-up. 170+ tools, every action audited and reversible.

Why it matters

The business case writes itself.

Autonomous resolution

End-to-end resolution on real conversations, deflections become resolutions, so your team handles only what truly needs them.

Lower cost to serve

TokenTrim™ inference-compression cuts tokens ~10× per interaction, so you scale to 24/7 coverage without scaling compute spend.

Continuous evaluation

autoQA scores 100% of interactions, not a 2% sample, turning compliance and quality into a live, learnable signal.

ROI snapshot

What a hard queue looks like after 90 days.

Representative results from deploying on a high-volume tier-1 queue. As the RSI loop tunes itself, resolution climbs while cost per contact falls, the two curves that usually move against each other.

Illustrative figures based on typical enterprise deployments. Your numbers depend on volume, mix and systems, we'll model them with you.

Autonomous resolution31%→76%

Cost per contact$6.40→$0.68

Avg. speed to answer4m 12s→0s

QA coverage~2%→100%

CSAT3.9→4.6

Enterprise runtime & trust

The model is the easy part.

Getting a voice agent into a real contact center is the hard part, the telephony, the handoffs, the controls and the guarantees. auto-AGENTS™ ships with all of it, so you connect in days, not quarters.

Connect any telephony

Native CCaaS integrations (Genesys, NICE, Five9, Amazon Connect, Twilio, Webex…), your own media stream, or raw SIP/PSTN carriers. No rip-and-replace, and billing-optimized so you're never double-charged on the AI leg.

How connectivity works →

Graceful human handoff

When a call needs a person, the agent warm-transfers over native or SIP (REFER / re-INVITE) with a full summary, transcript and context attached, so the customer never repeats themselves and the agent picks up mid-thought.

Two runtime modes

Direct-audio streaming (audio→LLM) for the lowest latency, or transcribed (audio→text→LLM) for tighter audit and cost control, switchable per flow. Model-agnostic across GPT-5.x, Gemini, Claude and on-prem Llama.

Secure & deployable anywhere

SOC 2 Type I & II, HIPAA, ISO 27001, PCI, GDPR and EU AI Act alignment, with field-level encryption, tenant-scoped KMS, PII redaction and an immutable audit trail. Run in our cloud, your VPC, on-prem or pinned to a region; ~10,000 concurrent sessions.

Warm transferauto-AGENTS™ → live agent

AI PM

Priya M.Tier 2 · Billing & claims

context attached

CallerMaria L. · identity verified

IntentReschedule + refill

SentimentPositive

Reason for transferPrior-authorization required

Identity ✓Chart pulled ✓Availability checked ✓Refill drafted ✓

Full transcript & recording attached · 0:42, summarized for the agent

Full context travels with the call, summary, transcript, actions taken

"It resolves three-quarters of our calls without a human, and the resolution rate climbs every week. Nothing else we evaluated even came close."

VP, Customer Operations

Global financial services · 135 agents

Proof

Outcomes teams feel in weeks, not quarters.

Deploy on your hardest queue, watch autoQA and resolution climb as the RSI loop tunes itself, and reallocate your team to the conversations that actually need a person.

resolved autonomously

to measurable lift

Connected to your stack

170+ connectors. It works where you already work.

FAQ

Voice Agents, answered.

Scripted bots and most first-gen "AI agents" are decision trees, they match against a fixed script and escalate when reality goes off-path. auto-AGENTS™ reason about the caller's actual intent with a frontier LLM, act on your systems over MCP to resolve the request, and improve themselves in production via RSI. You don't hand-draw flowcharts, and the agent gets measurably better every week instead of repeating the same mistakes.

No. Describe the job, or attach a spec, and auto-AGENT generates the flow, drafts the welcome & system prompts, grounds answers in your knowledge, and wires tools over MCP. It then validates the build against the CQB scenario suite before launch. You review and edit; you don't start from a blank canvas.

Three ways, no rip-and-replace: native integrations with major CCaaS platforms (Genesys, NICE, Five9, Amazon Connect, Twilio, Webex and more), ingesting your own media stream, or connecting to raw SIP/PSTN carriers. Call control is billing-optimized so you're not double-charged on the AI leg. See Telephony & connectivity →

The agent warm-transfers to a live agent over native routing or SIP (REFER / re-INVITE), handing over a full summary, transcript and the actions it already took. The customer doesn't repeat themselves, and the human picks up mid-context. You set the escalation policies, by intent, sentiment, value threshold or explicit request.

Sub-500ms at p95 with natural turn-taking and barge-in. Two runtime modes, direct-audio streaming for the lowest latency, and transcribed (audio→text→LLM) for tighter audit and cost control, switchable per flow.

Over MCP it reads and writes to your CRM, billing, scheduling and internal tools, and coordinates background agents during and after the call. With 170+ connectors and a headless API, every action runs inside your policies, fully audited and reversible.

800+ neural voices across 155+ locales, from providers including Azure, OpenAI and ElevenLabs, with custom brand-voice cloning, SSML prosody, and real-time speech-to-speech translation that preserves tone. It detects a mid-call language change and hot-swaps models without losing context.

SOC 2 Type I & Type II, HIPAA, ISO 27001, PCI and GDPR, plus EU AI Act alignment, with field-level encryption, tenant-scoped KMS, PII redaction, and an immutable audit trail. Deploy in cloud, your VPC, on-prem or by region.

See it live

Describe your hardest call. We'll ship the agent.

Bring a real scenario. In 30 minutes we'll build it, test it, and resolve it end to end, on your systems, in your languages.

Book a demo Explore the engine

One call. Five systems. No humans.

Try the hardest call

Press call. Hear it resolve.

Try a live call

Scripted bots deflect. auto-AGENTS™ resolve.

Contain the caller

Resolve the conversation

From intent to a self-improving agent, autonomously.

01 · Specify intent

02 · Synthesis

03 · Evals

04 · Recursive self-improvement

One calm voice. A whole team of agents behind it.

The business case writes itself.

Autonomous resolution

Lower cost to serve

Continuous evaluation

What a hard queue looks like after 90 days.

The model is the easy part.

Connect any telephony

Graceful human handoff

Two runtime modes

Secure & deployable anywhere

Outcomes teams feel in weeks, not quarters.

170+ connectors. It works where you already work.

Voice Agents, answered.

Describe your hardest call. We'll ship the agent.

Voice conversations that feel human.