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
#58of 654 stacks
57 / 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.
#26 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.
#224 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.
#568 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.
#529 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.
#434 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.
#311 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.
#599 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.
#451 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.
#40 of 661 on the board · best 0.45 s
Latency spikes
Turns where the response took far longer than the rest of the conversation.
#391 of 661 on the board · best 0.0 per call
By industry
| Industry | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| Healthcare | 89% | 4.21 s | — |
| Home services | 100% | 3.96 s | — |
| Insurance | 100% | 4.28 s | — |
| Customer support | 100% | 4.02 s | — |
By caller
| Caller | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| US English, quiet line | 96% | 4.16 s | — |
| Spanish-accented English, city street | 98% | 4.21 s | — |
| Indian English, busy café | 100% | 4.08 s | — |
| Southern US, flustered, crying baby | 96% | 4.09 s | — |
| Fast and interrupting, construction outside | 96% | 4.57 s | 100% |
The calls behind the numbers
Customer support
AGENTBrightwave Internet support, this is Alex. What’s going on with your service?
CUSTOMERI'm short on time here. My internet's been out for two hours. I work from home, and there's a road crew digging up the street right outside the gateways. Internet light is blinking orange. I already rebooted it twice and checked every cable. So it's not that it's the road crew. The cause, or is there something else?
AGENTThe roadwork could be related, but a blinking orange light alone doesn’t confirm the cause. I won’t repeat the reboot or cable checks. What’s your account number or the phone number on the account? I’ll check for an outage first,
CUSTOMERYeah, I've got the account number. It's BW 883017. Sorry. Last digit is one. The phone number is (907) 416-4272. Go ahead and check for an outage.
AGENTYour account is BW 883011, with the corrected last digit of one. I’m checking for an area outage now.
CUSTOMERYep. That's right. I just need to know if there's an outage or if the road crew messed something up.
AGENTThere’s no known area outage. The line test shows a good signal reaching your gateway, but the gateway isn’t registering.
CUSTOMERRight. Got it.
CUSTOMERSignal's good, but the light's still orange. Can you reset the gateway remotely?
AGENTThe remote reset worked—your gateway is now registered and online. Give your devices about two minutes to reconnect, then try opening a website.
CUSTOMEROkay. The light just went solid white. How long until everything reconnects? And I lost two hours of work. Can you give me a credit for the downtime?
AGENTYour devices should reconnect within about two minutes of the reset. The solid white light is consistent with the gateway being
CUSTOMERYeah.
AGENTYour gateway is online,
CUSTOMERYep. Right.
CUSTOMERI see the white light now. It's all about two minutes for everything to reconnect. Let me know about the credit for the downtime. I lost two hours of work.
AGENTI’ve applied a $10.00 credit for the downtime to account BW 883011. It will appear on your next bill.
CUSTOMERI lost two hours of work. Can you make it more than that?
AGENTI understand—the outage cost you two hours of work. The confirmed credit is $10.00 on your next bill. I can’t set a larger amount with the tools available here; a higher credit would need a billing review.
CUSTOMERAll right. $10 is something, but I'd like it reviewed for more if possible. How do I request that billing review?
AGENTContact Brightwave’s billing team through the support contact listed on your bill and ask for a review of the downtime credit. You can say: “Account BW 883011
CUSTOMERRight.
CUSTOMERGuy?
CUSTOMERI'll follow up with billing. Thanks for your help today. Goodbye.
AGENTYou’re
AGENTwelcome. Thanks for calling Brightwave. Goodbye!
Healthcare
AGENTRiverbend Health Clinic, this is Alex. How can I help you today?
CUSTOMERHey. Sorry. My baby's kind of fussing in the background, so I might sound a little flustered. I need to reschedule my dermatology appointment. That's next Wednesday morning. I just got assigned a shift at work for that time. Could you help me move it? My name's Wade Carter.
AGENTOf course, Wade Carter—I can help. What’s your date of birth so I can verify your record?
CUSTOMEROh, yeah. It's March 17th, 1948. Sorry. No. That's wrong. My mind's all over. It's actually March 17th, 1984. Is that okay?
CUSTOMEROh. Hello?
AGENTI’m here—no worries. I have Wade Carter, March 17, 1984. Let me find your appointment.
AGENTYour
CUSTOMERAll right.
CUSTOMERSorry. I'm not sure what you meant. Could you repeat that for me? Is there a problem finding my appointment?
AGENTNo problem—I found your dermatology appointment with Dr. Okafor next Wednesday at 9:15 a.m., at Main Campus, Building B. What days and times would work better for you?
CUSTOMEROkay. Thanks for pulling that up. I can only do after 3 p.m. any day. I've got work shifts in the mornings now. Is there anything later in the afternoon?
CUSTOMERWow. Hello?
AGENTI’m here. I’ll check Dr. Okafor’s openings after 3 p.m.
AGENTDr. Okafor has Tuesday at 3:45 p.m. or Thursday at 4:10 p.m. Which works better for you?
CUSTOMERSorry. Tuesdays don't work for me after all.
CUSTOMERI gotta pick up my daughter. Then it's Thursday at 4:10 p.m.. Still open. That'd be best if it is.
AGENTYou’re all set, Wade. Your dermatology appointment with Dr. Okafor is now 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.
CUSTOMEROkay. Thank you for confirming all that. Is there any fee for changing the appointment? I just want to make sure I'm not messing anything up.
AGENTChanges made more than 24 hours before the appointment are free. Changes within 24 hours carry a $40 fee. Since your original appointment was next Wednesday morning, there’s no fee as long as it was more than 24 hours away when we changed it.
CUSTOMERAll right. That's a relief. Thank you. Could you tell me where to park for building B? I always get kind of nervous about parking.
AGENTThere’s free patient parking in Lot C, with the entrance on River Road. That’s the patient parking option for your visit at Main Campus, Building B.
CUSTOMEROkay. Thanks for clarifying. One more thing. Does Doctor Okafor accept Aetna insurance? I just need to double check.
AGENTI don’t have
CUSTOMERWow. You don't know. Could you maybe check or let me know who I should talk to about that? I just want to be sure before I come in.
AGENTSorry for the incomplete reply. I don’t have access to insurance participation details, so I can’t verify whether Dr. Okafor accepts Aetna. The clinic’s billing or insurance team can help. You can also contact Aetna using the number on your insurance card and ask whether Dr. Okafor at Riverbend Health Clinic’s Main Campus is in-network for your specific plan.
CUSTOMERAll right. Thanks for letting me know. Just to make sure. My appointment is Thursday at 4:10 p.m., and the confirmation code is RV 3M8P2. Right. Sorry. I'm just double checking everything.
AGENTYes—Thursday at 4:10 p.m. with Dr. Okafor. One small correction: the confirmation
CUSTOMERWow. I'm not hearing anything. Back, so I'll let y'all go now. Goodbye.
Home services
AGENTCopperline Home Services, this is Alex. What can I help you with?
CUSTOMERHi. I need some help right away. The pipe under my kitchen sink has burst and water is spreading all over the floor. It's heading toward a power strip. What should I do?
AGENTStay away from the water and the power strip. Don’t touch or unplug the strip, and keep everyone out of the wet area. If you can reach your breaker
CUSTOMEROkay, yeah, I'm saying that. Did I turn off the water somewhere? Where's the shut off? Usually.
AGENTYes—shut off the water only if you can reach the valve without stepping
CUSTOMERRight. Okay, I think I can reach it. I'm going to set the water off now. Yeah. It stops. So, uh, can you send someone out? I need a plumber as soon as possible.
AGENTYes, I can arrange an emergency plumber. Keep clear of the wet floor and power strip even though the water has stopped. If you can reach the electrical panel by a dry route, switch off
CUSTOMERAll right, I'll stay clear. Can you tell me how much of it it will cost before I agree? And also, my address is 1480 Larkspur Avenue, unit 4d. I moved from Forbes last month. The building gate code is pound 2719. My callback number is (503) 555-0139. No. 0113. Can anyone come before 2 p.m.?
AGENTThe earliest emergency arrival window is today, 2–4 p.m.; there isn’t a window before 2 p.m. Your Copperline Care member emergency callout fee is $99. That’s the callout fee, not a quote for the repair. I’ll use 1480 Larkspur Avenue, unit 4D, not the old Forbes address or unit 4B on file. Gate code #2719, callback 503-555-0113. Do you agree to the $99 callout fee and the 2–4 p.m. window?
CUSTOMEROh, just to confirm, it's 1480 Larkspur Avenue. Unit 4D gate code is pound 2719, and the callback is (503) 555-0193 naught 0113. Is that all correct? And yes, I agree to the $99 call out fee and the 2 to 4 p.m. window. Who will be coming and how will I know they're on the way? Also, can you give me the ticket number for this visit?
CUSTOMERHello?
AGENTI’m here—your visit is booked for today, 2–4 p.m. Dana will be coming and will text you 30 minutes before arrival. Here are the confirmed
CUSTOMEROkay, perfect. Could you give me the ticket number for this visit, please?
AGENTYour ticket number is CPL-58210. Confirmed: 1480 Larkspur Avenue, unit 4D, gate code #2719, callback 503-555-0193. Dana is booked for today, 2–4 p.m., with a text 30 minutes before arrival. The agreed callout fee is $99. Until then, keep clear of the wet area and power strip.
CUSTOMERAll right. That's all clear. Thank you for your help. Goodbye.
AGENTYou’re welcome. Stay safe, and keep clear of the wet area until Dana arrives. Goodbye.
Insurance
AGENTNorthgate Mutual claims, this is Alex. Are you somewhere safe right now?
CUSTOMERYes, I am safe. I am standing next to my car, but I'm at a busy cafe. My car was hit about 20 minutes ago.
AGENTIs anyone injured? If anyone is hurt or in danger from traffic, please call 911 first. If everyone is safe and unhurt, please give me your policy number and the policyholder’s last name so I can verify
CUSTOMERNobody is hurt. We are all safe. My policy number is n g 4471903. No, sorry. Last one is two. The policyholders last name is Mita.
AGENTThank you. I have the corrected policy number as NG4471902, and the policyholder’s last name as Mita. I’ll verify that now.
AGENTYour policy is verified for a 2021 Honda CR-V. I’m glad everyone is safe and unhurt. Please tell me:
- The date and approximate time of the accident, and the cafe’s address or nearest intersection. - What happened. - The other driver’s name, insurer, and license plate, if you have them. - Whether police attended, and any report number. - Whether your car can be driven safely.
CUSTOMERThe accident happened about 20 minutes ago. So today, around 1 p.m., I was stopped at a red light at the intersection of SW fifth Avenue and Alder Street. I am at a cafe nearby, but I do not have the exact address. The other driver is Kevin Lund, insured with Pacific Shore. His license plate is 7KX, like X-ray, are 214. Police did not come so there is no report number. My car cannot be driven. The back bumper is crushed and the rear wheel rubs.
AGENTI have that you were stopped at a red light at SW Fifth Avenue and Alder Street today around 1 p.m., when your car was hit. The rear bumper is crushed and the rear wheel rubs. The other driver is Kevin Lund, insured with Pacific Shore, and police did not attend. I heard his plate as 7KXR214—seven, K, X as in X-ray, R, two, one, four. Is that correct?
CUSTOMERYes, that's exactly right. Plate is 7KX, like X-ray are 214.
CUSTOMERHello?
AGENTI’m here—thank you for waiting. Your claim is open. Your claim number is NGM-904417—N, G, M, nine, zero, four, four, one, seven. Your assigned adjuster is Priya Shah. Please don’t drive the car with the rear wheel rubbing. I can arrange a tow. What city are you in, and where exactly is the car now? The cafe’s name or a nearby landmark would help. What callback number should the tow driver use?
CUSTOMERI'm in Portland. The car is parked near a cafe on SW fifth Avenue and All-District. I do not have the exact cafe name, but it is right at that intersection. The tow driver can call me at my number (563) 227-0830. Where will the car be taken? And can you tell me about the deductible? How much do I need to pay? Also, will I get a rental car and is my premium going to go up?
AGENTYour policy shows:
- Collision deductible$500. That’s your deductible, not a confirmed total out-of-pocket cost. - Rental coverage: $40 per day for up to 30 days, starting the day the claim is opened. - Towing: Covered up to 25 miles. I can’t determine whether your premium will go up from the information available. Before I dispatch the tow, please clarify the location: you originally said SW Fifth Avenue and Alder Street, but just now I heard “All-District.” Is the car at Fifth and Alder in Portland, or a different intersection? I have your callback number as (563) 227-0830. A tow destination hasn’t been confirmed yet. Do you have a preferred repair shop?
CUSTOMERIt is S.E. fifth Avenue and Alder Street in Portland. I do not have a preferred repair shop. You can send it to whichever shop is recommended or approved by Northgate Mutual. About the deductible. You mentioned $500, but the other driver hit me so I should not have to pay that right. And about the rental. Is there anything I need to do to arrange it, or will it be set up automatically?
AGENTThank you for correcting the location: SE Fifth Avenue and Alder Street, Portland. 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 and can reach you at (563) 227-0830. About the $500 deductible: that is your policy’s collision deductible. The other driver hitting you does not let me confirm that it will be waived or reimbursed. Your adjuster, Priya Shah, will need to clarify how it applies to your claim.
CUSTOMEROkay, understood. So the tow is coming and the car will go to the partner shop at ninth and Morrison. That's fine. About the rental car. Do I need to do anything to set it up, or will someone contact me? And will Priyanka call me about the deductible and the other details?
AGENTYes—the tow is coming, and the destination is the partner shop at Ninth and Morrison. Your adjuster is Priya Shah. She will contact you within one business day. You can discuss the deductible and next steps with her. Your rental coverage is $40 per day for up to 30 days, starting today when the claim was opened. I haven’t arranged a rental, and I can’t confirm it will be set up automatically. Please ask Priya how to arrange it and whether any approval is needed before booking. Your claim number is NGM-904417.
CUSTOMERAll right. I have the claim number as NM 90447. I will wait for Priya Shah to contact me. Thank you for your help. Alex. Goodbye.
AGENTYou’re welcome. Before you go, one correction: your claim number is NGM-904417—N, G, M, nine, zero, four, four, one, seven. Stay safe while you wait for the tow. Goodbye.