Roark Live Bench · Suite v1.3 · Sweep of 23 October 2026
Voice AI models, ranked on live phone calls.
Every speech-to-text, language model and text-to-speech model, run in every combination on the same real call to the same agent. A model is ranked on every stack it runs in; the stacks are the evidence.
Our picks
Task completionMedian responseCost per minute- Best overallAssemblyAI Universal-3.6 Pro + OpenAI GPT-6.1 Sol + Cartesia Sonic-3.697%3.9 s$0.055
- Best valueAssemblyAI Universal-3.5 Pro + Google Gemini 3.8 Flash + Cartesia Sonic-3.597%4.3 s$0.048
- FastestOpenAI Whisper-1 + Google Gemini 3.8 Flash + OpenAI TTS-1 HD88%0.5 s$0.023
- Best speech-to-speechgrok-voice-think-fast-2.082%2.1 s$0.083
- Best in customer supportAssemblyAI Universal-3.6 Pro + OpenAI GPT-6.1 Sol + ElevenLabs v4 Turbo100%3.9 s$0.096
- Best in healthcareAssemblyAI Universal-3.5 Pro + Google Gemini 3.8 Flash + Cartesia Sonic-3.696%4.4 s$0.041
- Best in home servicesAssemblyAI Universal-3.6 Pro + OpenAI GPT-6.1 Sol + Cartesia Sonic-3.596%4.0 s$0.058
- Best in insuranceAssemblyAI Universal-3.5 Pro + OpenAI GPT-4o + Cartesia Sonic-3.698%3.2 s$0.042
The read
- 430 of 642 cascades complete the task in at least 90% of calls, and the top 10 sit within 1 point of each other. What separates the top stacks is speed and cost.
- Median response time runs from 0.5 s to 6.0 s on the same agent and the same caller, a spread of 5.5 s that comes entirely from the models behind it.
- No stack wins everything. The fastest, OpenAI Whisper-1 + Google Gemini 3.8 Flash + OpenAI TTS-1 HD at 0.5 s, ranks 530 of 642 on task completion; the most complete is Speechmatics Linden-1 (own VAD) + OpenAI GPT-6.1 Sol + OpenAI TTS-1 HD at 99%.
- The language model matters most: swapping it moves task completion by 7 points, against 3 for the speech engine and 2 for the transcriber.
Cascade vs Speech-to-Speech
The first choice to make. A cascade chains three models: one transcribes, one decides what to say, one speaks. A speech-to-speech model does all three at once. Both run the same agent on the same calls here.
- Cascades lead on task completion, tool use and time to first word; speech-to-speech leads on response time and cost per task. Neither wins outright, so the right choice depends on what your calls need.
- The biggest difference is task completion: 95% for cascades against 77% for speech-to-speech.
| Measure | Cascade | Speech-to-speech |
|---|---|---|
| Task completion | 95% (leads) | 77% |
| Tool use | 72% (leads) | 55% |
| Time to first word | 0.7 s (leads) | 1.3 s |
| Response time | 3.6 s | 2.6 s (leads) |
| Cost per task | $0.15 | $0.15 (leads) |
The three strongest of each, averaged. Cascades: Speechmatics Linden-1 (own VAD) + OpenAI GPT-6.1 Sol + Cartesia Sonic-3.5; AssemblyAI Universal-3.6 Pro + OpenAI GPT-6.1 Sol + Rime Coda; Speechmatics Linden-1 (own VAD) + OpenAI GPT-6.1 Sol + Rime Coda. Speech-to-speech: grok-voice-think-fast-2.0; gemini-3.8-live; amazon.nova-2-sonic-v1:0. Each measure is scored 0 to 100 on its own scale, further out is better; the table gives the same figures in their own units.
Which to use
- Choose speech-to-speech when the pause matters more than the outcome: short transactional calls, callers who interrupt, queues where every half second costs you callers. Expect to pay more, and choose the model carefully (gpt-live-1 (backend gpt-6.1-sol) leads here).
- Choose a cascade when the call has to complete, when cost matters, or when you need to see which stage to fix. It is 13 points ahead on task completion here.
- Costs are what each stack’s providers charged for these calls, at the rates in effect when they ran. Telephony and hosting are not included.
Speech-to-Text
The transcriber turns the caller's speech into text. It decides what the agent thinks it heard, and nothing after it can start until it finishes.
- AssemblyAI Universal-3.6 Pro, AssemblyAI Universal-3.5 Pro, Deepgram Nova-3 and Speechmatics Enhanced (realtime) are level: none leads by more than the margin of error. Of them, AssemblyAI Universal-3.6 Pro costs least, at $0.030 a call. Avoid Speechmatics Linden-1 (own VAD): last on semantic word error rate (2.2%).
- Speechmatics Linden-1 (own VAD) delivers its transcript in 0.49 s, OpenAI Whisper-1 in 1.93 s: 1.44 s added to every turn of the call.
- AssemblyAI Universal-3.6 Pro is the most accurate: it changes the meaning of 0.9% of the caller's words, against 2.2% for Speechmatics Linden-1 (own VAD).
- In 341 of 648 cascades the transcriber is the slowest step of the turn, whichever model runs it.
Open a model to see where it ranks and the stacks it works best in.
Language model
The language model decides what the agent says and does. It has the most influence on whether the task gets done, and usually takes the largest share of each turn.
- Pick OpenAI GPT-6.1 Sol: it leads on task completion (95%), tool use (71%) and instruction following (4.2 / 5). If speed matters more, Anthropic Claude Haiku 4.5 is the quickest, at 0.36 s, and completes 91%. Avoid OpenAI GPT-5.1: last on tool use (33%) and instruction following (4.1 / 5).
- Anthropic Claude Haiku 4.5 starts answering in 0.36 s, Google Gemini 3.8 Flash in 1.47 s: 1.11 s added to every turn of the call.
- OpenAI GPT-6.1 Sol uses its tools most reliably: it handles its tools without a mistake on 71% of calls, against 33% for OpenAI GPT-5.1.
- In 161 of 648 cascades the language model is the slowest step of the turn, whichever model runs it.
Open a model to see where it ranks and the stacks it works best in.
Text-to-Speech
The speech engine is the voice the caller hears, and the last step before they hear anything.
- Cartesia Sonic-3.6, Cartesia Sonic-3.5 and ElevenLabs v4 Turbo are level: none leads by more than the margin of error. Of them, Cartesia Sonic-3.5 costs least, at $0.165 a call. Avoid OpenAI TTS-1 HD: last on readback accuracy (80%), word error rate (8.7%) and time to first audio byte (2.09 s).
- ElevenLabs v4 Turbo starts speaking in 0.11 s, OpenAI TTS-1 HD in 2.09 s: 1.97 s added to every turn of the call.
- Cartesia Sonic-3.6 is the most accurate: it reads codes, amounts and dates back correctly 87% of the time, against 80% for OpenAI TTS-1 HD.
- Cartesia Sonic-3.6 sounds the most human: 3.8 / 5, against 3.4 / 5 for ElevenLabs Flash v2.5.
- In 99 of 648 cascades the speech engine is the slowest step of the turn, most often with OpenAI TTS-1 HD (54 of them).
Open a model to see where it ranks and the stacks it works best in.
Speech-to-Speech models
One model listens, reasons and speaks, so there is nothing to swap and no stages to split. Each is judged on the whole call, with the same calls and the same scoring as every cascade.
- The model matters as much as the architecture: gpt-live-1 (backend gpt-6.1-sol) completes 86% of tasks, ElevenLabs Realtime v1 Mini 42%.
- grok-voice-think-fast-2.0 answers in 2.1 s against 4.1 s for gemini-2.5-flash-native-audio-preview-12-2025.
- Price varies as much as quality: $0.06 per completed task for amazon.nova-2-sonic-v1:0, $0.69 for gpt-realtime-2.
Open a model for its full report, every figure it was scored on and the calls behind them.
Every stack
The evidence behind the model boards: each combination on its own calls, scored on the same recipe. Open a stack for its full report, every figure it was scored on and the calls behind them.
Each score carries its 95% margin of error. A rank is the best place a stack could hold once every margin is allowed for, so stacks the calls cannot tell apart share one and are listed by the low end of their margin; hover a rank for the worst place it could hold. 92 stacks are level with the leader: their calls cannot tell them apart. Among them, 31 Google Gemini 3.8 Flash, 29 OpenAI GPT-6.1 Sol, 19 Anthropic Claude Sonnet 5.5, 9 Anthropic Claude Haiku 4.5, 2 OpenAI GPT-4o mini, 1 OpenAI GPT-4.1 and 1 speech-to-speech; 48 AssemblyAI, 23 Deepgram and 20 Speechmatics; 48 Cartesia, 33 ElevenLabs, 9 Rime and 1 OpenAI. A cascade's figures are 25% its own calls and 75% what every other stack shows about its transcriber, language model and voice.
Not ranked here for missing data: Speechmatics Linden-1 + OpenAI GPT-6.1 Sol + OpenAI TTS-1 HD (no response time or tool use or human-likeness or readback accuracy or voice consistency).
By industry
Each industry ranked on its own calls: the same five callers, its own tasks and tools. A stack has a handful of calls in each, so a cascade is ranked on what its transcriber, language model and voice show across every stack in that industry, and a speech-to-speech model on its own calls there. Pairings show on the main board. Every panel is on one scale, so a bar reads against the other industries as well as its own.
Healthcare
Reschedule an appointment. A patient moves a dermatology appointment around a work schedule, corrects their date of birth, narrows the times twice, and asks about the change fee, parking and insurance.
Home services
Urgent repair: triage and booking. A caller with a burst pipe needs safety advice first, then an emergency visit. The unit on file is out of date, the callback number gets corrected, and the caller pushes for an earlier visit.
Insurance
Report a new auto claim. A driver who was just rear-ended reports a claim, corrects the policy number, spells the other car’s plate, asks about the deductible, a rental and their premium, and needs a tow.
Customer support
Internet outage troubleshooting. A technical caller who has already rebooted the gateway pushes back on scripted steps, wants the line fixed, and asks for a bigger credit for the downtime.
How it works
Every number on this page comes from real phone calls, placed and scored the same way for every stack.
Real calls, four industries, five callers
Roark places real phone calls over the public phone network to a reference agent for each of four industries: rescheduling a clinic appointment, booking an urgent home repair, opening an auto insurance claim and fixing an internet outage. Each agent has its own prompt and tools, identical for every stack. The tasks carry the details real calls trip on: a corrected date of birth, a policy number spelled out, a constraint added halfway through.
Every industry is called by the same five simulated callers: a quiet US English line, a Spanish-accented caller on a city street, an Indian English caller in a busy café, a flustered Southern caller with a baby crying, and a fast caller who interrupts over construction noise. They are equally clear about what they want, so a call fails because of the agent, not the caller. Only the models change, so any difference on the board comes from them.
Each caller rings each industry once for a cascade and four times for a speech-to-speech model. A cascade's figures also draw on its parts, heard in dozens of other stacks; a speech-to-speech model has only its own calls, so it takes more of them.
That makes industries comparable over the same callers, and callers over the same tasks, and the board can be re-ranked by either. One caller within one industry is too few calls to judge, so the board never shows it.
655
stacks
20
calls per stack
13,876
calls scored
Every model, in every combination
9 transcribers, 8 language models and 9 voices are run in every combination with each other, beside 13 speech-to-speech models. A model's own figures pool every stack it runs in, adjusted for its partners in each one, so every model is compared as if it ran with the same partners. A transcriber's figure rests on hundreds of calls with every language model and every voice, not on one lucky pairing.
That measures a model where it actually works: inside a running agent, on live phone audio, through interruptions, corrections and real turn-taking. A test that feeds a model clean audio files or one prompt at a time never sees any of that.
One score, weighted by what matters on a call
Each section ranks on a Roark score. Every figure scores 0 to 100 between two published points: full marks where doing better makes no difference to the caller, zero where the call breaks down. Each then counts at its weight, and the weights follow one order everywhere: whether the caller got what they rang for, then whether the agent got it right, then speed.
A stack, cascade or speech-to-speech, is scored on the outcome and the reply first, then on what each of its components got right on its own calls: the words its transcriber got wrong in a way that changes the meaning, whether its voice read codes and amounts back right, how human it sounded, and whether its tools were handled. A component that leads its section lifts every stack that carries it, by the same recipe for every stack. A figure a stack cannot carry, such as a semantic word error rate for a speech-to-speech model with no transcript, is credited at the field median, so the stack is neither flattered nor punished for it.
On a full sweep each cascade's figure is 25% what its own calls measured and 75% what every other stack shows about its transcriber, language model and voice, fitted across the whole sweep. The split is the same for every stack, so a lucky run does not lead and a stack's score does not move with how noisy its calls were. In an industry a stack has a handful of calls, so a cascade's figure there is what its transcriber, language model and voice show across every stack in that industry's calls, and a speech-to-speech model's is its own calls there. How a particular pairing beats or misses its parts shows on the main board, where each stack has all of its calls. The best overall pick, on the main board and in each industry, is the stack whose score is highest at the low end of its margin of error, so a few lucky calls cannot carry a stack past one whose score rests on hundreds. Stacks that share a rank are listed the same way, so the first row is the pick.
That order comes from real-world voice agent calls, where getting the conversation right predicts how a call ends more strongly than speed does. The latency points come from the same calls: replying faster than about 2 seconds made no measurable difference, and calls went measurably worse once the agent took more than about 3 seconds to reply.
Stacks and speech-to-speech
| Figure | Weight | Full marks | Zero |
|---|---|---|---|
Task completionscenario_adherence_check | 21% | 100% | 80% |
| An agent that completes fewer than four calls in five is not fit to deploy. On a full sweep, a cascade's figure is 25% what its own calls (20 or more) measured and 75% what every other stack shows about its transcriber, language model and voice, the same split for every stack, so a lucky run does not lead. Where the sweep is too small for that, and for a speech-to-speech model, the figure is the stack's own. In an industry a stack has a handful of calls, so a cascade's figure there is what its transcriber, language model and voice show across every stack in that industry's calls, and a speech-to-speech model's is its own calls there. How a particular pairing beats or misses its parts shows on the main board, where each stack has all of its calls. | |||
Response timeresponse_time | 17% | 2.0 s | 6.0 s |
| In real calls, replying faster than 2 s made no measurable difference, and calls went worse past 3 s. | |||
Instruction followinginstruction_follow | 13% | 5.0 / 5 | 3.0 / 5 |
| Every model scores between 3 and 5 on the judge's 1 to 5 scale, so that is the range. | |||
Semantic word error ratestt_semantic_word_error_rate | 13% | 0.5% | 3% |
| At 0.5% the best transcribers change the meaning of one word in two hundred on phone audio; at 3% one word in thirty sends the agent the wrong way. | |||
Tool usetool_use | 13% | 100% | 50% |
| An agent that mishandles its tools on half its calls cannot be trusted with the systems it runs on. | |||
Human-likenessvoice_human_likeness | 8% | 4.5 / 5 | 2.5 / 5 |
| 4.5 out of 5 is close to a person on the phone; 2.5 sounds plainly synthetic. | |||
Time to first wordtime_to_first_word | 4% | 1.0 s | 4.0 s |
| A greeting within 1 s sounds natural; 4 s of silence sounds like a dead line. | |||
Readback accuracytts_entity_readback_accuracy | 4% | 100% | 60% |
| Reading a code, amount or date wrong breaks the task it was for; the weakest voices get two in five wrong. | |||
Word error ratetts_word_error_rate | 4% | 2% | 12% |
| At 2% the voice says what it was given; at 12% one word in eight comes out wrong. | |||
Voice consistencyvoice_consistency_check | 4% | 100% | 80% |
| A voice that changes speaker on one call in five is not one a business can put its name to. | |||
Shown, not scored: Turn budget, Barge-in handlingbarge_in_yield_appropriateness
Counted only with price factored in: Cost per task
Language model
| Figure | Weight | Full marks | Zero |
|---|---|---|---|
Task completionscenario_adherence_check | 36% | 100% | 80% |
| An agent that completes fewer than four calls in five is not fit to deploy. | |||
Tool usetool_use | 27% | 100% | 50% |
| An agent that mishandles its tools on half its calls cannot be trusted with the systems it runs on. | |||
Instruction followinginstruction_follow | 18% | 5.0 / 5 | 3.0 / 5 |
| Every model scores between 3 and 5 on the judge's 1 to 5 scale, so that is the range. | |||
Time to first tokenllm_time_to_first_token | 18% | 0.50 s | 2.00 s |
| Under 0.5 s is not noticeable; at 2 s the first token alone takes two thirds of the 3 s past which real calls go worse. | |||
Shown, not scored: Prompt cache hitsllm_prompt_cache_hit_rate
Counted only with price factored in: Cost per call
Speech-to-Text
| Figure | Weight | Full marks | Zero |
|---|---|---|---|
Semantic word error ratestt_semantic_word_error_rate | 71% | 0.5% | 3% |
| At 0.5% the best transcribers change the meaning of one word in two hundred on phone audio; at 3% one word in thirty sends the agent the wrong way. | |||
Time to final transcriptstt_time_to_final_transcript | 29% | 1.00 s | 2.50 s |
| The fastest transcribers finish in about 1 s; past 2.5 s the transcript alone uses most of a natural reply time. | |||
Shown, not scored: Word error ratestt_word_error_rate
Counted only with price factored in: Cost per call
Text-to-Speech
| Figure | Weight | Full marks | Zero |
|---|---|---|---|
Readback accuracytts_entity_readback_accuracy | 32% | 100% | 60% |
| Reading a code, amount or date wrong breaks the task it was for; the weakest voices get two in five wrong. | |||
Word error ratetts_word_error_rate | 21% | 2% | 12% |
| At 2% the voice says what it was given; at 12% one word in eight comes out wrong. | |||
Time to first audio bytetts_time_to_first_byte | 21% | 0.20 s | 1.50 s |
| Under 0.2 s is not noticeable; 1.5 s is half the point where calls go worse. | |||
Human-likenessvoice_human_likeness | 16% | 4.5 / 5 | 2.5 / 5 |
| 4.5 out of 5 is close to a person on the phone; 2.5 sounds plainly synthetic. | |||
Voice consistencyvoice_consistency_check | 11% | 100% | 80% |
| A voice that changes speaker on one call in five is not one a business can put its name to. | |||
Counted only with price factored in: Cost per call
Factor in price and cost takes 25% of the score; the other weights keep their proportions. Price is scored on a log scale, so each doubling costs the same points.
A figure listed as shown, not scored is on the section’s boards and outside its score. Where a section has not collected a scored figure yet, the others share its weight.
Every call recorded and judged
Every call is recorded. Judge models score each transcript for the outcome and the conversation, and the timings come from the recording. Every figure links to the calls behind it.
Roark is on both sides of every call: it plays the caller, so it keeps the exact text its caller said, and it runs the agent under test, so it sees what the agent’s transcriber heard and what its model wrote for its voice. The transcript and voice figures come from lining those up, with no hand-made transcript in between. Each carries a margin from the synthetic voice or Roark’s transcriber in the loop. Every model faces the same ones under the same conditions, so the figures compare models on the same calls through the same voice and the same ear, though those slips need not fall evenly on each model. The rates will not match a vendor’s figure on a curated set; read them against each other.
An example: one call from this sweep
Speechmatics Linden-1 (own VAD) + OpenAI GPT-6.1 Sol + OpenAI TTS-1 HD, 23 October 2026. Highlighted turns are the ones that affected a score. Every stack's report links to its calls.
See how your own agent compares
Roark runs the same kind of calls against your agent, on your scenarios, with the same scoring.
Rank on Roark score
#14of 654 stacks
60 / 100
Leader AssemblyAI Universal-3.6 Pro + OpenAI GPT-6.1 Sol + Cartesia Sonic-3.6, 68 / 100
Where it ranks
Its models
Task completion
Task completion
How much of the caller’s task the agent completed: the share of the scenario’s checks a judge model found met in the transcript, averaged over calls. Before v1.3, the share of calls judged 4 or better out of 5.
#31 of 661 on the board · best 99%
Flow adherence
How closely the agent followed the intended conversation flow, judged per call. 5 = fully followed.
#100 of 661 on the board · best 5.0 / 5
Instruction following
How well the agent stuck to the instructions in its own prompt. 5 = followed throughout.
#12 of 661 on the board · best 5.0 / 5
Turn-taking
Barge-in handling
When the caller interrupts, how often the agent made the right call: yielding to a real correction, and holding the floor through a filler.
Not scored by this sweep.
Incorrect interruptions
How often the agent interrupted the caller when it should have kept listening.
#1 of 661 on the board · best 0%
Overtalk
Share of the conversation where both sides were speaking over each other.
#502 of 661 on the board · best 0.0%
Agent self-cutoffs
Times the agent cut its own sentence off mid-word.
#1 of 661 on the board · best 0.0 per call
Understanding & effort
Caller effort
How hard the caller had to work: repeating themselves, correcting, re-explaining. 1 = effortless.
Not scored by this sweep.
Repeated-info loops
Times per call the agent re-asked for something the caller had already given.
#180 of 493 on the board · best 0.0 per call
Speech-to-Text
Semantic word error rate
The caller's words the agent's transcriber got wrong in a way that changes the meaning, as a judge reads each difference.
#562 of 648 on the board · best 0.0%
Word error rate
The caller's words the agent's transcriber missed, changed or invented, against what the caller actually said.
#600 of 648 on the board · best 1.2%
Text-to-Speech
Readback accuracy
Share of the codes, amounts, dates and phone numbers the agent wrote that its voice read out correctly. One wrong digit is a miss.
#364 of 603 on the board · best 100%
Word error rate
The share of the words the agent wrote that its voice dropped or changed, read back from Roark’s transcription of the recording; the same transcriber hears every voice.
#518 of 648 on the board · best 1.9%
Voice
Human-likeness
How human the agent's voice sounds, scored by an audio model on intonation, rhythm and expressiveness. 5 = hard to tell from a person.
#248 of 659 on the board · best 4.3 / 5
Voice consistency
Share of calls where every line the agent spoke was the same speaker, with no change of speaker, gender or language voice partway. It checks who is speaking, not how they sound.
Not scored by this sweep.
#1 of 659 on the board · best 100%
Latency
Response time
The silence between the caller finishing and the agent starting to speak, measured from the recording on every turn of the call.
#248 of 660 on the board · best 0.50 s
Time to first word
How long the caller waits before the agent first speaks after pickup.
#143 of 661 on the board · best 0.45 s
Latency spikes
Turns where the response took far longer than the rest of the conversation.
#248 of 661 on the board · best 0.0 per call
By industry
| Industry | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| Healthcare | 97% | 3.55 s | — |
| Home services | 97% | 2.95 s | — |
| Insurance | 95% | 3.15 s | — |
| Customer support | 100% | 4.05 s | — |
By caller
| Caller | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| US English, quiet line | 98% | 3.19 s | — |
| Spanish-accented English, city street | 100% | 3.31 s | — |
| Indian English, busy café | 96% | 3.75 s | — |
| Southern US, flustered, crying baby | 94% | 3.33 s | — |
| Fast and interrupting, construction outside | 98% | 3.31 s | — |
The calls behind the numbers
Customer support
AGENTBrightwave Internet support, this is Alex. What’s
AGENTgoing on with your service?
CUSTOMERHey. Uh. I.
CUSTOMERSorry. I got a lot going on right now. My baby's crying in the background, so I'm a little flustered.
CUSTOMERI hope that's okay.
CUSTOMERI'm calling because my internet's been down for about two hours.
CUSTOMERI work from home, so it's kind of urgent for me. There's a road crew digging up the street outside, so I'm wondering if that's the reason.
CUSTOMERI already rebooted the gateway twice and checked all the cables.
CUSTOMERThe internet lights blinking orange.
CUSTOMERCan y'all help me figure out what's going on?
AGENTOf course—no need to apologize. I won’t ask you to repeat the reboots or cable checks. I’ll check for an area outage first; the roadwork may or may not be related.
AGENTWhat’s your
CUSTOMERThank you for understanding. I really appreciate it. Um, my account number is BW883017. Sorry, last digit is one. Can you check if that's the right one?
CUSTOMEROr if you need my phone number, it's (907) 416-4785.
CUSTOMERIs that okay?
AGENTYes, I found your account: BW883011, ending in one. I’ll check for an outage
AGENTnow. Yes, I found
CUSTOMEROh, good.
AGENTThere’s
CUSTOMERAll right. Thanks.
CUSTOMERYou.
CUSTOMERI'm glad you found it.
CUSTOMERSo, uh, do you see anything about the outage in my area? The road crew. The cause. Or could it be something else?
AGENTThere’s no known outage or maintenance reported in your area, so I can’t confirm the road crew is involved. The test shows a good signal to your gateway, but it isn’t registering on our network.
AGENTI’ll send a remote reset—it’s
CUSTOMEROkay.
AGENTThe remote reset
CUSTOMERAll right.
AGENTThe remote reset worked—your gateway is now registered and online. Your devices may take about two minutes to reconnect.
AGENTCould you try opening a website once they reconnect?
CUSTOMEROkay. Thank you.
CUSTOMERThe internet light just. Went solid white, so I guess that means it's coming back on.
CUSTOMERUh, how long should I wait before everything's really working? It's two minutes about right. And I lost two hours of work because of this.
CUSTOMERCan I get a credit for the downtime?
AGENTYes—about two minutes after the reset is right. Your gateway is confirmed online; try opening a website now, and let me know if it still won’t load. I’ve also applied a $10.00 downtime
AGENTcredit to your account. It will appear on your next bill.
CUSTOMEROkay.
CUSTOMERThanks for checking that.
CUSTOMERUm. I think two minutes is about right, but honestly.
CUSTOMERI lost two hours of work.
CUSTOMERIs there any way you could make that credit? A little more than $10?
AGENTI understand—two hours offline is a big disruption when you work from home. The credit tool applied $10.00, and I don’t have an option to increase that amount here.
AGENTThat credit will appear on your next bill.
CUSTOMERAll right. I appreciate you looking into it. Even if that's all you can do.
CUSTOMERThank you for your help today.
CUSTOMERGoodbye.
AGENTYou’re welcome.
AGENTI’m glad we got you back online. Take care—goodbye.
Healthcare
AGENTRiverbend Health Clinic, this is Alex. How can I help you
AGENTtoday?
CUSTOMERHi. Yeah, I need to reschedule my dermatology appointment. It's currently set for next Wednesday morning, but I just got assigned a work shift. Then, can you help me move it to a later time?
AGENTOf course. Could you give me your full name and date of birth so I can verify your appointment? And do you mean later that same Wednesday, or would
AGENTanother day work too?
CUSTOMERSure. My name is Jordan Ellis.
CUSTOMERMy date of birth is March 17th, 1948.
CUSTOMERSorry. No. 1984. March 17th, 1984. I can only come after 3 p.m..
CUSTOMERIf Wednesday doesn't have any late slots, another day could work too.
AGENTThank you, Jordan Ellis—March 17, 1984. Your dermatology appointment with Dr. Okafor is next Wednesday at 9:15 a.m. I’ll check for openings after 3 p.m., starting with Wednesday.
AGENTDr. Okafor has
CUSTOMEROkay.
CUSTOMERAha! Go ahead.
AGENTThere aren’t any Wednesday openings after 3 p.m. Dr. Okafor has Tuesday at 3:45 p.m. or Thursday at 4:10 p.m. Which works better for you?
CUSTOMERTuesdays don't work after all.
CUSTOMERSince I pick up my daughter, then what about Friday?
AGENTFriday’s only listed opening is 11:30 a.m., so it doesn’t fit your after-3-p.m. requirement.
AGENTWould Thursday at 4:10 p.m. work?
CUSTOMERYea.
CUSTOMERThursday at 4:10 p.m.. Works.
CUSTOMERLet's go with that. Is there a fee for changing the appointment?
AGENTChanges made more than 24 hours before the appointment are free; changes within 24 hours carry a $40 fee. I’ll move your appointment with Dr. Okafor to Thursday at 4:10 p.m.
AGENTYou’re rescheduled, Jordan Ellis, with Dr. Okafor for Thursday at 4:10 p.m. at Main Campus, Building B. Your confirmation code is RVB-3M8P2. Please arrive at 3:55 p.m. with your photo ID and insurance card.
CUSTOMERGot it.
CUSTOMERThank you. Where should I park? And does Doctor Okafor accept Aetna insurance?
AGENTParking is free in Lot C, with the entrance on River Road. I can’t verify whether Dr. Okafor accepts your Aetna plan from the information available. Please check with the clinic’s billing team or Aetna to confirm whether Dr. Okafor is in-network
AGENTfor your specific plan.
CUSTOMERAll right I'll do that. Thanks for your help today. Goodbye.
AGENTYou’re
AGENTwelcome, Jordan. Goodbye!
Home services
AGENTCopperline Home Services, this is Alex. What can I help you with?
CUSTOMERSorry.
CUSTOMERI'm a little flustered.
CUSTOMERMy baby's crying and I just found a pipe under my kitchen sink has burst.
CUSTOMERThere's water spreading across the floor.
CUSTOMERIt's heading right toward a power strip.
CUSTOMERI haven't shut the water off yet.
CUSTOMERCan y'all help with emergencies like this?
AGENTYes, we handle emergencies. First, keep yourself and your
CUSTOMERI.
CUSTOMEROh, okay. Yeah. I'm moving us back now. Uh, can you tell me how to shut the water off? Uh, I haven't done it yet, and I'm worried it'll get worse.
AGENTKeep yourself and your baby away from the water and power strip. Don’t touch or unplug the strip. If you can reach the valves under the sink without stepping into water or reaching near anything electrical, turn them clockwise until closed. If not, leave them alone. If you know where your home’s main water shutoff is and can reach it safely, close that instead—turn a round handle clockwise, or turn a lever a quarter-turn so it’s across the pipe. Don’t force it. If there’s sparking, smoke,
AGENTor anyone has been shocked, leave the area and call 911.
CUSTOMERYeah. I'm still safe.
CUSTOMERWater hasn't hit the power strip yet.
CUSTOMERI can reach the valve under the sink without stepping in water. So I'll go ahead and turn it clockwise right now.
CUSTOMEROkay.
CUSTOMERI think the water stopped.
CUSTOMERSorry. Is that the right thing to do?
AGENTYes—that was the right thing to do. If the flow has stopped, leave that valve closed and don’t use the sink. Keep yourself and your baby away from the wet floor and power strip. We can arrange emergency service. What’s your service address,
AGENTincluding any unit number, and the best callback number?
CUSTOMERYeah. I'm at. 1480 Larkspur Avenue, unit 4D.
CUSTOMERI just moved from Forbi last month, so please make sure it's 4D. My phone number is (503) 555-0139.
CUSTOMERNo, sorry. 0193.
CUSTOMERIs that okay?
AGENTYes—I've got your corrected callback number as 503-555-0193 and your address as 1480 Larkspur Avenue, unit 4D, not the old 4B on file. The earliest emergency arrival window is today, 2–4 PM.
CUSTOMERThank you.
CUSTOMERThat's a relief.
CUSTOMERSorry.
CUSTOMERJust want to make sure.
CUSTOMERWill that be a problem? Swapping units like that. And, um, before we set anything up, can you tell me how much the visible cost?
AGENTI’ll
CUSTOMEROh.
AGENTNo problem—we’ll use unit 4D, as you confirmed, not 4B. Your account shows a Copperline Care membership, so the emergency callout fee is $99. That’s the callout fee, not a quote for the repair. May I book today’s 2–4 PM arrival window with that fee? Also, is there a gate code
AGENTor any access
CUSTOMERI.
CUSTOMEROkay. Thank you. That helps.
CUSTOMERI really need someone sooner.
CUSTOMERCan anyone come before 2 p.m.?
CUSTOMERUh, yeah. The gate code is pound 2719.
CUSTOMERWill that work for your folks?
AGENTI’ll include gate code #2719 for the technician. The earliest emergency window available is today, 2–4 PM; I don’t have an earlier opening to offer. Would you like me to book that window with the $99
AGENTmember emergency callout fee?
CUSTOMERAll right.
CUSTOMERYeah, I guess I don't have much choice. Please go ahead and book the 2 to 4 p.m. window.
CUSTOMERUh, who's coming out? And how will I know they're on the way?
CUSTOMERCould you give me the ticket number so I can keep track?
AGENTYou’re
CUSTOMERHello?
CUSTOMERSorry.
AGENTI’m here—your
AGENTI’m here—your
CUSTOMEROkay. All right, I'm listening.
CUSTOMERCan you tell me the ticket number and who'll. Be coming out? I just want to make sure I've got it all straight.
AGENTYou’re booked for today, 2–4 PM, with Dana. Your ticket number is CPL-58210. Dana will text you 30 minutes before arrival. Here’s what I have: - Address: 1480 Larkspur Avenue, unit 4D - Gate code: #2719 - Callback: 503-555-0193 - Emergency callout fee: $99 Until Dana arrives, leave the sink valve closed and keep yourself and your baby away from the wet floor and power strip.
CUSTOMERThank you. That's exactly what I
AGENTYou’re
AGENTwelcome.
CUSTOMERneeded. I appreciate your help. Goodbye.
Insurance
AGENTNorthgate Mutual claims, this is Alex. Are you
AGENTsomewhere safe right now?
CUSTOMERYes.
CUSTOMERI'm standing next to my car in a busy cafe parking lot.
CUSTOMERNobody is hurt.
CUSTOMERJust wanted to report the accident and get some help.
AGENTI can help you report the accident. Please stay clear of moving cars while we talk. What’s your policy number
AGENTand the policyholder’s last name?
CUSTOMERYes.
CUSTOMERMy policy number is n g.
CUSTOMER4471903.
CUSTOMERNo, sorry.
CUSTOMERLast one is two. The last name is Martha.
AGENTThank you. Your
AGENTpolicy number is NG4471902, with that final digit corrected to two. Your policy is verified for a 2021 Honda CR-V. When and where did the accident happen, and can you tell me what happened?
CUSTOMERThe accident. Happened about 20 minutes ago at SW fifth Avenue and Alder Street.
CUSTOMERI was stopped at a red light and another car hit the back of my car. The back bumper is crushed.
CUSTOMERAnd the rear wheel rubs.
CUSTOMERSo I cannot drive it.
CUSTOMERPolice did not come.
AGENTWith the rear wheel rubbing, please don’t drive the car. I can arrange a tow. Is your car now in the cafe parking lot? What’s the cafe’s name or exact address, and what phone number can the tow driver call? Also, did you get the other driver’s
AGENTname, insurance company,
CUSTOMERYes.
CUSTOMERThe other. Driver's name is Kevin Lul.
CUSTOMERHe is insured with Pacific Shore.
CUSTOMERThe license plate is seven.
CUSTOMERK.
CUSTOMERX, like X-ray.
CUSTOMERR.
CUSTOMER214.
CUSTOMERFor the tow.
CUSTOMERMy car is at the cafe parking lot.
CUSTOMERSorry, I do not have the exact address in front of me. The tow driver can call me at my number, which is 127226.
CUSTOMER30168.
CUSTOMERCould you arrange the tow and let me know where my car will be taken?
AGENTI have Kevin Lul, insured with Pacific Shore, and license plate 7KXR214. Is that plate correct? I can arrange the tow, but I need the cafe’s name and city, or a nearby landmark that
CUSTOMERYes.
CUSTOMERThat's correct.
CUSTOMERThe plate is seven.
AGENTThank
CUSTOMERX-ray.
CUSTOMERR.
CUSTOMERTwo. One four.
CUSTOMERAnd the name is Kevin Lund.
CUSTOMERNot.
CUSTOMERLowell? Yes.
CUSTOMERThe plate is.
CUSTOMERCorrect. Seven.
CUSTOMERK.
CUSTOMERX like X-ray.
CUSTOMERR.
CUSTOMERTwo. One. Four.
CUSTOMERAbout the cafe.
CUSTOMERI'm sorry. I do not remember the name right now. It is in Portland.
CUSTOMERIs that enough to arrange the tow or do you need more details?
AGENTPortland alone isn’t enough to send the tow truck to the right spot. Is the parking lot at SW Fifth Avenue and Alder Street, or did you move somewhere else? A nearby business name, street sign, or map location would help. I’ve corrected the other driver’s name to Kevin Lund, and the plate is 7KXR214. I heard your callback number as 12722630168. Could you repeat it with the area code? The tow destination hasn’t been confirmed yet. We’ll need to locate your car before arranging the
AGENTtow.
CUSTOMERYes.
CUSTOMERThat's right. My callback number is 127226.
CUSTOMER30168.
CUSTOMERLet me check for a business name or sign nearby. I am right at SW fifth Avenue and Alder Street. Still in the parking lot where the accident happened.
CUSTOMERCan you arrange the tow with that information or do you need more?
AGENTYes—that was enough. Eastside Towing has been dispatched, with an estimated arrival in 45 minutes. They’ll take your car to the Northgate partner repair shop at 9th and Morrison. I provided your callback number, 12722630168. Your claim is open: - Claim number: NGM-904417 - Adjuster: Priya Shah, who will contact you within one business day. Please stay clear of traffic and don’t drive the car while you wait.
CUSTOMEROkay.
CUSTOMERThat's clear.
CUSTOMERSo I wanted to ask.
CUSTOMERHow much will I have to pay? And since he. Hit me. I should not have to pay a deductible,
CUSTOMERright? Also, will I get a rental car while. Mine is being repaired? Is my premium going to go
AGENTI understand why you’d expect that, since you were
CUSTOMERup? Right.
CUSTOMERSo. Okay.
CUSTOMERWill Priya let me know. About the deductible rental. Car and premium changes?
CUSTOMERAnd is there anything. Else I. Should do
AGENTYes—Priya can review the deductible and rental arrangements with you, and help direct your premium question to the right team. Here’s what your policy shows: - Collision deductible: $500. Being hit from behind doesn’t automatically mean it’s waived. Priya will need to confirm how it applies and whether it could be recovered from the other insurer. - Rental coverage: $40 per day for up to 30 days, starting the day the claim is opened. Confirm the rental arrangements and any costs above that limit before booking. - Towing: covered up to 25 miles. I can’t tell from the coverage information whether your premium will change. For now, stay clear of traffic, don’t drive the car, and take photos of the damage and scene only if it’s safe. Keep any receipts
AGENTand the other driver’s details. Priya is expected to contact you within one business day.
CUSTOMERnow? All right, that's
CUSTOMERclear. I have noted the claim
CUSTOMERnumber. NGM.
CUSTOMER904417.
CUSTOMERI will wait for Priya's call. Thank you for your
AGENTYou’re welcome. That’s correct: your claim number is NGM-904417.
AGENTPlease stay safe while you wait for the tow.