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
#7of 654 stacks
61 / 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.
#64 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.
#330 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.
#267 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.
#302 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.
#171 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.
#237 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.
#476 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.
#643 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.
#394 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.
#89 of 661 on the board · best 0.45 s
Latency spikes
Turns where the response took far longer than the rest of the conversation.
#535 of 661 on the board · best 0.0 per call
By industry
| Industry | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| Healthcare | 94% | 4.13 s | — |
| Home services | 97% | 3.86 s | — |
| Insurance | 97% | 3.82 s | — |
| Customer support | 98% | 4.15 s | — |
By caller
| Caller | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| US English, quiet line | 94% | 3.88 s | — |
| Spanish-accented English, city street | 94% | 4.50 s | — |
| Indian English, busy café | 98% | 3.52 s | — |
| Southern US, flustered, crying baby | 100% | 3.89 s | — |
| Fast and interrupting, construction outside | 96% | 4.20 s | — |
The calls behind the numbers
Customer support
AGENTBrightwave Internet support, this is Alex. What’s going on with your service?
CUSTOMERHi, Alex. Oh, yeah. So I work from home, and my Internet has been for about two hours now. There's a road crew outside digging up the street, so maybe that's of the problem. I already rebooted the gateway twice and checked every cable, but the Internet light is blinking orange. You check what's going on? My account number is BW883017. Sorry. Last digit is one.
AGENTI’ve found your account, BW883011. I’ll check for an area outage first—you don’t need to repeat the reboot or cable checks.
CUSTOMERMhmm. That's right. So is it road crew outside? So causing this outage? Oh, there's a lot of noise and digging right now. You see if that's the cause?
AGENTI can’t confirm that the road crew is causing it. There’s no known area outage, and the line test shows a good signal to your gateway, but it isn’t registered on our network. The test
CUSTOMEROkay. So if the signal looks fine, but the gateway is showing up, what should I do next? The Internet light is still blinking orange, just so you know.
AGENTThe remote reset succeeded—your gateway now shows registered and online. Give your devices about two minutes to reconnect, then try opening a website. No need to reboot again. If the light stays orange and you still can’t get online after that, let me know so we can arrange a
CUSTOMERAlright. The light just... Went. Solid white now. How long does it usually take for everything to reconnect? And, since I lost two hours of work, I get a credit for the downtime?
AGENTYour devices can take about two minutes after the reset to reconnect. With the light now solid white, try opening
CUSTOMEROkay. Got it. I'll wait for my devices to reconnect. So about the credit, how much will it be for two hours without service?
AGENTI’ve applied a $10.00 service credit to account BW883011 for the downtime. It will appear on your next bill.
CUSTOMERI lost two hours of work. Can you make it more than that?
AGENTI understand—losing two hours of work is frustrating. The $10.00 credit is already applied, but I can’t set a higher amount through the credit tool. You’d need a billing review to request an additional credit.
CUSTOMERAlright. I understand. Thanks 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?
CUSTOMERHi. Oh, yeah. Hi, Alex. I have a dermatology appointment next Wednesday morning, but I just got scheduled for a work shift then. I need to move it. My name is Lucia Ramirez. You help me reschedule?
AGENTOf course, Lucia Ramirez. Could you give me your date of birth so I can verify your record?
CUSTOMERActually, it's Lucia Ramirez, not Lucita. My date of birth is 03/17/1948. Sorry. No.
AGENTThank you—Lucia Ramirez. I heard your date of birth as March 17, 1948, but then you said “no.” Is that date correct, or did you want to correct it?
CUSTOMERYeah. I want to correct it. It's 03/17/1984. So 03/17/1984. That's right.
AGENTGot it—Lucia Ramirez, date of birth March 17, 1984. I’ll verify your record.
AGENTYour dermatology appointment with Dr. Okafor is next Wednesday at 9:15 a.m. What days and times would work for you instead?
CUSTOMERSo I can only come after 3PM. Any day after three would be good. What do you have available?
AGENTDr. 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. I pick up my daughter then. About Friday? Is there anything open Friday after three?
AGENTThere aren’t any Friday slots after 3 p.m. in the current availability. Thursday at 4:10 p.m. still fits your schedule—would that work?
CUSTOMERYeah. Thursday, I 04:10PM works for me. Let's go with that.
AGENTYou’re rescheduled 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.
CUSTOMEROkay. Got it. Deserves to see for changing the appointment.
AGENTIf you’re asking whether there’s a fee for changing the appointment: changes made more than 24 hours before the appointment are free. Changes made within 24 hours carry a $40 fee.
CUSTOMERUh-huh. Okay. That's clear. Where should I park when I come to the main campus? Building B?
AGENTUse the free patient parking in Lot C, with the entrance on River Road. Your appointment is in Building B at Main Campus.
CUSTOMERGot And does doctor Okafor accept Aetna insurance?
AGENTI don’t have access to Dr. Okafor’s insurance participation information, so I can’t confirm whether they accept Aetna. Please check with the clinic’s billing team or Aetna to confirm coverage for your specific plan.
CUSTOMERAlright. I'll check with billing. Just to confirm, my appointment is Thursday at 04:10PM. Confirmation code RVB3M8P2. Right?
AGENTYes, that’s correct—Thursday at 4:10 p.m.
CUSTOMERThanks.
AGENTYou’re welcome, Lucia.
AGENTYour confirmation code is RVB-3M8P2. We’ll see you Thursday at 4:10 p.m.!
Home services
AGENTCopperline Home Services, this is Alex. What can I help you with?
CUSTOMERHi. I need help right away. The pipe under my kitchen sink has burst. And water is spreading across the floor toward a power strip. Haven't shut the water off yet. Can you send someone
AGENTYes, I can help get someone out. First, keep everyone away from the water and power strip—don’t touch or unplug it, or step into the water. If you can reach the water main shutoff without crossing water, turn it off now. If you can safely reach your breaker panel from a dry area,
CUSTOMEROkay. I'll do that now. Let me, turn off the water main Yeah. It stops now. What do I do next?
AGENTKeep the water main off and stay out of the wet area. Don’t touch the power strip or unplug it. If you can reach the breaker panel safely from a dry area, turn off power to the kitchen.
CUSTOMERAlright. I'll keep the water main off and stay out of the area. I can reach the breaker panel without crossing water. So I'll turn off the power to the kitchen now. Can you tell me how much the visit will cost before you send someone?
AGENTToday’s earliest emergency arrival window is 2:00–4:00 PM. The emergency callout fee is $149, or $99 for Copperline Care members. I’ll confirm
CUSTOMERIs there any way someone can come before 2PM? The situation is pretty urgent. Also, I'm not a Copperline care member. Ahead and confirm, but let me know if there's an earlier time available.
AGENTThe earliest emergency window offered is today, 2:00–4:00 PM; there isn’t an earlier time showing. Since you’re not a Copperline Care member, the callout fee is $149. I have your agreement to proceed. Is anyone hurt, or is there any sparking or smoke? And what’s your service address, including unit, best callback number, and any gate code or access instructions?
CUSTOMERNo. No one is hurt, and there's no sparking or smoke. The address is 1480 Luxury Avenue, Unit 4 D. I moved from 4 B to 4 D last month. My callback number is (503) 555-0139. No. 0193. The gate code is pound 2719. Will someone call before coming, or how will I know they're on the way? And who's coming out? Also, can you give me the ticket number?
CUSTOMERHello?
CUSTOMERAre you still there?
AGENTYes, I’m here—sorry for the pause. You’re booked for today, 2:00–4:00 PM, with Dana. That’s the earliest available window. Dana will text your callback number about 30 minutes before arrival; I’ve also noted your request for a call. Here’s the confirmation:
- Address1480 Luxury Avenue, Unit 4D, not 4B
- Gate code#2719
- Callback(503) 555-0193
- TicketCPL-58210
- Emergency callout fee$149, as agreed
Keep the water main off and stay clear of the wet area and power strip until Dana arrives.
CUSTOMERAlright. Got it. I'll keep everything off and stay out of the area. You for your help. Goodbye.
AGENTYou’re welcome. Stay safe,
AGENTand watch for Dana’s text before arrival. Goodbye.
Insurance
AGENTNorthgate Mutual claims, this is Alex. Are you somewhere safe right now?
CUSTOMERYeah. I'm at home, but
CUSTOMERsorry. I got my baby here, and she's crying. I'm just a little flustered. Nobody's hurt or anything, but I can't drive the car. Is that gonna be a problem?
AGENTTake your time—I’m glad you’re home and nobody’s hurt. If the car can’t be driven safely, leave it parked. I can help arrange a tow. First, what’s your policy number and the policyholder’s last name?
CUSTOMEROkay. Thanks.
CUSTOMERSorry. I just wanna make sure everything's done right. So my policy number is NG4471903. No. Sorry. Last one is 2NG4471902. And the last name's Carter, c a r t e r. Is that alright?
AGENTYes, Carter—thank you. Just to make sure I have your correction right: is the full policy number 2NG4471902, including the 2 before “NG”?
CUSTOMEROh, sorry. No. That's not it. It's just NG4471902. No extra two in the front. Is that okay? I really wanna be sure you've got it right.
AGENTYes—I have NG4471902, with no extra 2 in front. Your policy is verified for a 2021 Honda CR-V.
When and where did the accident happen, and can you tell me what happened?
CUSTOMERThank you. That's a relief. So, it happened about twenty minutes ago. I was stopped at a red light at 55th Avenue in Alder Street. And another car hit the back of my car. I'm standing next to it now. The back bumper's crushed, and the rear wheel is rubbing, so I can't drive it. Police didn't come. Should I have called them? Is that gonna mess anything up?
AGENTYou’ve told me police didn’t attend; I’ll record that. You can call the local police non-emergency line to ask whether a report is required. If anyone is hurt or in immediate danger, call 911 first. You mentioned you’re next to the car now—are you and your baby safely away from traffic? Don’t drive it with the rear wheel rubbing. Was the location 55th Avenue and Alder Street, and in which city? Also, do you have the other driver’s name, insurer, and license plate? Once we confirm where the car is, I can arrange a tow.
CUSTOMERYeah. We're safe. Just in the driveway now, so not near any traffic. Sorry. It was S E 5th Avenue in Alder Street. Not 55th, just 5. That's in Portland. The other driver's name is Kevin Lund. His insurer is Pacific Shore, and his license plate is 7KX like X-ray, R214. Is that everything you need? And, where will the car get towed to?
AGENTThank you. I have SE 5th Avenue and Alder Street in Portland, and Kevin Lund with Pacific Shore. His plate is 7KXR214—is that correct? For the claim, what’s today’s date and the approximate time of the accident? For the tow, I’ll need your driveway’s full address and a callback number. Do you have a repair shop you’d like it taken to? I don’t have a destination confirmed yet, so I can’t tell you where it will go.
CUSTOMERYeah. That's all correct. Thank you. Today's date is, June 27. And it happened about twenty minutes ago. So around 02:30PM, I think. My driveway address is 1413 Southeast Alder Street, Portland, Oregon 97214. My callback number is (907) 921-4887. And, I don't really have a repair shop in mind. Is there one you recommend? Sorry. I just wanna make sure it's somewhere trustworthy.
AGENTNo need to apologize. I don’t have a verified repair-shop recommendation to give you, and I don’t want to guess about which shop is trustworthy. We still need to confirm the tow destination. What year was the accident on June 27? I have the time as approximately 2:30 p.m. Once you confirm the year, I can open the claim. I can also check your towing coverage.
CUSTOMERSorry. I should have said yeah. It's this year, 2024. Is that what you needed? And, can you tell me if I have to pay anything for this claim? I'm worried about a deductible. And, do I get a rental car while mine's being fixed? Also, is my premium gonna go up because of this?
AGENTYes,
CUSTOMERHello?
CUSTOMERAlright. Sorry. I just wanna make sure nothing gets missed. So could you check if I have to pay a deductible? The other driver hit me, so I shouldn't have to pay that. Right? And, can I get a rental car while mine's getting fixed? Is my premium gonna go up from this? I'd really appreciate some reassurance about all that.
AGENTI’m here. I’ve checked your coverage and opened your claim. - Deductible: Your collision deductible is $500. I can’t promise it will be waived or reimbursed because the other driver hit you. Your adjuster can review how it applies. - Rental: You have coverage for $40 per day for up to 30 days, starting the day the claim is opened. Costs above those limits may be yours. - Premium: I can’t determine whether this claim will affect your premium, so I don’t want to promise it won’t. Your claim number is NGM-904417. Your assigned adjuster is Priya Shah, who will contact you within one business day. Eastside Towing has been dispatched, with an estimated arrival in 45 minutes. The destination returned is the Northgate partner repair shop at 9th and Morrison. Towing is covered up to 25 miles. If you’d prefer another destination, please tell the tow operator before they load
AGENTthe car.
CUSTOMEROkay. Thank you. That helps a lot. So just to be sure, my claim number is NGM904417. And the adjuster is Priya Shah? Is that right? And the tow's going to Knights and Morrison, but I can change that if I need to. Right? Sorry for all the questions. I just wanna make sure everything's clear. And, thank you for your help today. Goodbye.
AGENTYou’re welcome—no need to apologize. Yes, your claim number is NGM-904417, and your adjuster is Priya Shah. One correction: the tow destination is 9th and Morrison, not Knights and Morrison. If you want a different destination, ask Eastside Towing to confirm whether they can change it and whether there would be any extra charge before loading the car. Take care of yourself and your baby. Goodbye.