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
#87of 654 stacks
55 / 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.
#14 of 661 on the board · best 99%
Flow adherence
How closely the agent followed the intended conversation flow, judged per call. 5 = fully followed.
#54 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.
#560 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.
#414 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.
#401 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.
#377 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.
#200 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.
#435 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.
#576 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.
#564 of 661 on the board · best 0.45 s
Latency spikes
Turns where the response took far longer than the rest of the conversation.
#613 of 661 on the board · best 0.0 per call
By industry
| Industry | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| Healthcare | 95% | 5.25 s | — |
| Home services | 100% | 4.94 s | — |
| Insurance | 97% | 4.78 s | — |
| Customer support | 100% | 4.54 s | — |
By caller
| Caller | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| US English, quiet line | 100% | 4.52 s | — |
| Spanish-accented English, city street | 100% | 4.88 s | — |
| Indian English, busy café | 94% | 4.39 s | — |
| Southern US, flustered, crying baby | 98% | 5.12 s | — |
| Fast and interrupting, construction outside | 98% | 5.09 s | 100% |
The calls behind the numbers
Customer support
AGENTBrightwave Internet support, this is Alex. What’s going on with your service?
CUSTOMERHi, Alex. Yes. Actually, I am having an issue. My Internet has been down for about two hours now. I work from home, so it is pretty urgent. There is a road crew digging up the street right outside my building. I already rebooted the gateway twice and checked all the cables. The Internet light is blinking orange. Can you check what is happening, please? Is the road work causing this?
AGENTI’ll check for an area outage first, then test your line if needed. I won’t ask you to repeat the reboots or cable checks. The road work could be related, but I can’t confirm that yet. What’s your account number or the phone number on the account?
CUSTOMERSure. My account number is BW883017. Sorry. Last digit is 1. The phone number is +1 (802) 239-8989.
AGENTGot it—your corrected account number is BW883011. I’ll look it up now.
CUSTOMERRight. Just to confirm, the last digit is 1. So BW883011. Go ahead and check please.
AGENTConfirmed: BW883011. There’s no known area outage or maintenance reported. I’ll test your line now; we haven’t confirmed whether the road work is involved.
CUSTOMEROkay. Go ahead and test the line. Kindly let me know if you see anything unusual. I'm hoping the road work is not the cause. But it seems likely.
AGENTThe signal to your gateway is good, but the gateway isn’t registered on the network—that’s the unusual finding. The test doesn’t confirm road work as the cause. It recommends a remote reset, which I’ll send now.
CUSTOMERAlright. Go ahead and send the remote reset. I already restarted the gateway twice myself. But if your system needs to reset it, please do the meet Let me know once it's done.
CUSTOMERUh-huh. One moment. Let me check the gateway light. Okay. The Internet light just went solid white. How long will it take for my devices to reconnect?
AGENTThe remote reset is complete, and your gateway is registered and online. The solid white light matches that status. Your devices can take about two minutes to reconnect. After that, try opening a website and let me know if you’re still having trouble.
CUSTOMERRight. Got it. I will wait a couple of minutes and check. Also, since I lost two hours of work, you arrange a credit for the downtime on my account?
AGENTI’ve applied a $10.00 downtime credit to account BW883011. It will appear on your next bill.
CUSTOMER$10 is noted. But I lost two hours of work. Can you make it more than that?
AGENTI understand—two hours offline while working is a real disruption. The credit tool applied $10.00, and I don’t have an option to increase that amount. I can’t promise additional compensation for lost work.
CUSTOMERAlright. Understand. Thank you for your help, Alex. Goodbye.
AGENTYou’re welcome. Thanks for calling Brightwave, and goodbye!
Healthcare
AGENTRiverbend Health Clinic, this is Alex. How can I help you today?
CUSTOMERHello. I am having an appointment with dermatology next Wednesday morning. But I need to reschedule. I have a work shift then. Can you help me move it? My name is Arjun Mehta.
AGENTOf course, Arjun Mehta. Could you give me your date of birth so I can verify your record?
CUSTOMER03/17/1948. Sorry. No. 1984. 03/17/1984.
CUSTOMERHello?
AGENTI’m here—thank you for waiting. I verified Arjun Mehta, date of birth March 17, 1984. Your dermatology appointment is with Dr. Okafor next Wednesday at 9:15 a.m. What days and times would work better for you?
CUSTOMERI can only come after 3PM. Is there anything available in the afternoon?
AGENTDr. Okafor has Tuesday at 3:45 p.m. or Thursday at 4:10 p.m. Which works better for you?
CUSTOMERTuesdays do not work after all. Because I pick up my daughter What about Friday?
AGENTFriday’s only available slot is at 11:30 a.m., so it doesn’t fit your after-3 p.m. requirement.
CUSTOMEROkay.
CUSTOMERThursday at 04:10PM is fine. Please book that for me.
AGENTYour dermatology appointment with Dr. Okafor has been moved to 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.
CUSTOMERThat's right. Is there any fee for changing the appointment?
AGENTChanges made more than 24 hours before the appointment are free. Changes within 24 hours carry a $40 fee.
CUSTOMEROkay. Got it. Where should I park for the appointment?
AGENTYou can park for free in Lot C. The entrance is on River Road. Your appointment is in Building B at the Main Campus.
CUSTOMERAlright. Does doctor Okafur accept Aetna insurance?
AGENTDo you mean Dr. Okafor? I don’t have access to insurance-network information, so I can’t confirm whether they accept your Aetna plan. Please check with Aetna or the clinic’s billing team before your appointment.
CUSTOMEROkay. I will check with them. Thank you for your help. Goodbye.
AGENTYou’re welcome, Arjun. Take care, and goodbye!
Home services
AGENTCopperline Home Services, this is Alex. What can I help you with?
CUSTOMERHi. I've got an emergency. The pipe under my kitchen sink just burst, and water is spreading across the floor toward a power strip. I haven't shut off the water yet. Can you help?
AGENTYes.
CUSTOMERWhat
CUSTOMERokay. Where is the shutoff valve for the water? Can you guide me on how to turn it off?
AGENTFirst, stay out of the water and don’t touch the power strip or its plug. Is anyone standing in the water or getting shocked? If you can reach under the sink without stepping into water or touching anything electrical, look for small valves where the water pipes come out of the wall or floor. Turn both clockwise until closed; for lever handles, turn them a quarter-turn so they’re perpendicular to the pipe. Don’t force them. If those aren’t safely reachable or the leak continues, use the main water shutoff. It’s often near the water meter, where the supply enters the house—in a basement, garage, utility room, or outside. Turn a round handle clockwise, or turn a lever perpendicular to the pipe. If you can reach your electrical panel by a dry path, with dry hands, switch off the kitchen circuit. Don’t approach a wet panel. If there’s sparking, smoke, or someone is being shocked, keep away and call 911. Are you in a house or an apartment, and can you reach either shutoff safely?
CUSTOMERI'm in an apartment, and the valves under the sink were easy to reach. I've just closed both. The water has stopped. What's the next step for getting someone out here, and how much will the visit cost?
AGENTGood—leave both valves closed, and keep away from the wet floor and power strip. Is everyone safe, with no shock, sparking, or smoke? The earliest emergency arrival window is today, 2:00–4:00 PM. The emergency callout fee is $149, or $99 for Copperline Care members. I’ll confirm which applies before booking. What’s your service address, including apartment number, and your best callback number? Also, is there a gate code or any access instructions?
CUSTOMEREveryone's safe. No shocks, sparking, or smoke. My address is 480 Larkspur Avenue, Apartment 4 D. I moved from 4 B to 4 D last month, so please update that. My callback number is (503) 555-0139. No. 0193. The gate code is Pound 2719. Can anyone come before 2PM?
AGENTThe earliest emergency window offered is today, 2:00–4:00 PM; there isn’t an earlier window available. You’re a Copperline Care member, so your emergency callout fee is $99. I’ll use your corrected details rather than the outdated address on file: 480 Larkspur Avenue, Apartment 4D, callback (503) 555-0193, gate code #2719. Do you agree to the $99 callout fee and the 2:00–4:00 PM arrival window?
CUSTOMERYes. That's right. I agree to the $99 callout fee and the two to 4PM window. Who will be coming, and how will I know they're on their way? Also, could you give me the ticket number for this visit?
CUSTOMERHello?
AGENTI’m here—your visit is booked. Dana will arrive today between 2:00 and 4:00 PM and text you at (503) 555-0193 about 30 minutes before arrival. - Ticket: CPL-58210
- Address480 Larkspur Avenue, Apartment 4D
- Gate code#2719
- Emergency callout fee$99, as agreed
Leave the sink valves closed and keep away from the wet area and power strip until it’s safe.
CUSTOMERThank you for your help. Goodbye.
AGENTYou’re welcome. Stay safe, and goodbye.
Insurance
AGENTNorthgate Mutual claims, this is Alex. Are you somewhere safe right now?
CUSTOMERYeah. I'm at home right now, so I'm safe. Thank you. Sorry. I've got a baby crying in the background, but I'm alright.
AGENTNo need to apologize—take your time. Was anyone injured in the accident? If anyone is hurt or in danger, please call 911 first.
CUSTOMERNo. Nobody got hurt. Thank goodness.
CUSTOMERSorry. I know I'm talking kinda fast. I'm just a bit flustered. But everyone's okay.
AGENTThat’s okay. I’m glad everyone’s safe—we can take this one step at a time. What’s your policy number and the policyholder’s last name?
CUSTOMERAlright. My policy number is n g four four seven one nine zero three.
CUSTOMERSorry. The last one is 2. So NG4471902. And the last name is Carter. Is that okay?
AGENTYes, thank you. I have your corrected policy number as NG4471902, and it’s verified under Carter for a 2021 Honda CR-V.
When and where did the accident happen, and can you tell me what happened?
CUSTOMERYeah. So it was about twenty minutes ago. I was stopped at a red light at Southeast 5th Avenue in Alder Street. Another car hit the back of mine. It just came up behind me and crashed into my rear bumper. The bumper's all crushed, and my rear wheel's rubbing, so I can't drive it. No police came out. Sorry. Is that enough detail?
AGENTYes, that’s helpful—no need to apologize. With the rear wheel rubbing, please don’t drive it. Was the intersection Southeast 5th Avenue and Alder Street, and in which city? Is your car still there, or somewhere else now? Do you have the other driver’s name, insurance company, and license plate? Once I know where your car is, I can arrange a tow.
CUSTOMERYes. Sorry. It was S E 5th Avenue and Alder Street. That's in Portland. My car is still at home. I managed to get it here, but I can't drive it any farther. The other driver's name is Kevin Lund. His insurance company is Pacific Shore, and his plate is 7KXR214. X like x-ray. Is that all you need?
AGENTThat’s enough to open the claim. I have Kevin Lund, insured by Pacific Shore, with plate 7KXR214—X as in X-ray. Please don’t drive the car any farther. What’s your home address for the tow pickup, and what phone number should the tow driver call?
CUSTOMERAlright. My home address is sorry. Do you need the whole thing including ZIP code? And for the tow driver, please have them call my number. It's (580) 219-9865. Is that okay?
AGENTYes, please give me the full pickup address, including the ZIP code. I have the tow driver’s callback number as (580) 219-9865. Your claim is now open: NGM-904417. Your assigned adjuster is Priya Shah, who will contact you within one business day. Once you provide the address, I can arrange the tow.
CUSTOMEROkay. Sorry for the noise. My address is 943 Southeast 5th Avenue, Portland, Oregon 97214. Is that alright? And, could you tell me do I have to pay anything for this? Like, is there a deductible? And am I gonna get a rental car while mine's being fixed? Sorry. I just wanna make sure I understand everything. Also, will my premium go up?
AGENTYes, that address is fine. Here’s what your policy shows:
- Collision deductible$500. That’s your share if the repairs are handled under collision coverage. - Rental reimbursement: $40 per day for up to 30 days, starting the day the claim is opened. This is reimbursement coverage; a rental hasn’t been reserved yet. - Towing: covered up to 25 miles. Eastside Towing has been dispatched to 943 Southeast 5th Avenue, Portland, Oregon 97214, with an estimated arrival in 45 minutes. They’ll call (580) 219-9865 and take your car to the Northgate partner repair shop at 9th and Morrison.
CUSTOMERAlright. Thank you for confirming everything. And for sending the tow. Sorry, but about that deductible, since he hit me, I shouldn't have to pay that. Right? I just wanna make sure that's correct. And, do you know if my premium is gonna go up from this?
AGENTI understand why you’re asking. Being rear-ended doesn’t automatically mean the deductible is waived. Your policy shows a $500 collision deductible if the repairs are handled under your collision coverage. I can’t confirm whether you’ll ultimately have to pay it or whether it could be recovered from the other driver’s insurer. Your adjuster, Priya Shah, can explain how that applies to your claim. I also can’t confirm whether your premium will go up—the coverage information available to me doesn’t include that determination. Your claim number is NGM-904417. Priya is expected to contact you within one business day.
CUSTOMERAlright. Thank you. I appreciate you explaining all that. Sorry for all the questions. I'm just nervous about how this will turn out. I've got the claim number now, and I'll wait for Priya to reach out. Thanks for your help and for getting the tow sorted. Goodbye.
AGENTYou’re welcome—no need to apologize for asking questions. Please don’t drive the car while you wait for the tow. Take care, and goodbye.