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
#9of 654 stacks
60 / 100
Leader AssemblyAI Universal-3.6 Pro + OpenAI GPT-6.1 Sol + Cartesia Sonic-3.6, 68 / 100
Where it ranks
Its models
Task completion
Task completion
How much of the caller’s task the agent completed: the share of the scenario’s checks a judge model found met in the transcript, averaged over calls. Before v1.3, the share of calls judged 4 or better out of 5.
#71 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.
#618 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.
#581 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.
#616 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.
#121 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.
#109 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.
#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.
#126 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.
#91 of 661 on the board · best 0.45 s
Latency spikes
Turns where the response took far longer than the rest of the conversation.
#119 of 661 on the board · best 0.0 per call
By industry
| Industry | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| Healthcare | 92% | 3.57 s | — |
| Home services | 98% | 2.98 s | — |
| Insurance | 94% | 2.96 s | — |
| Customer support | 100% | 3.45 s | — |
By caller
| Caller | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| US English, quiet line | 96% | 3.12 s | — |
| Spanish-accented English, city street | 96% | 3.79 s | — |
| Indian English, busy café | 95% | 3.05 s | — |
| Southern US, flustered, crying baby | 100% | 3.16 s | — |
| Fast and interrupting, construction outside | 94% | 3.06 s | 96% |
The calls behind the numbers
Customer support
AGENTBrightwave Internet support, this is Alex. What’s going on with your service?
CUSTOMERHi. Yeah, my internet has been down for about two hours now. I work from home and there's a road crew digging up the street outside. I already rebooted the gateway twice and checked every cable.
CUSTOMERBut the internet light is blinking orange.
CUSTOMERCan you help me figure out what's going on?
AGENTYes. Since you’ve already rebooted twice and checked the cables, we won’t repeat those steps. What’s your account number or the phone number on the account? I’ll check for an area outage first.
CUSTOMERRight. My account number is.
CUSTOMERBW883017. Sorry, last digit is one. My phone number is (907) 416-6728. Could you check if the road work outside is causing the outage?
AGENTThanks—your corrected account number is BW883011. I’ll check for a reported outage, though that may not confirm whether the road work is responsible.
CUSTOMEROkay, go ahead and check. The road crew is right outside, so I wonder if that's the cause.
CUSTOMERIf you find anything, let me know.
AGENTThere’s no known area outage, and the line test shows a good signal reaching your gateway, but it isn’t registering on the network. That doesn’t confirm the road work as the cause. The test recommends a remote reset, which I’ll send now—no need for you to reboot it again.
AGENTThe remote reset worked—your gateway is now registered and online. Give your devices about two minutes to reconnect, then try opening a website. We haven’t confirmed any connection to the road work. Is your internet working now?
CUSTOMERYeah. The light just went solid white. How long until my devices reconnect? And since I lost two hours of work.
CUSTOMERCan I get a credit for the downtime?
AGENTYour devices should reconnect within about two minutes of the gateway coming online. I’ve also applied a $10.00 downtime credit to account BW883011. It will appear on your next bill.
CUSTOMERI lost two hours of work. Can you make it more than that?
AGENTI’ve applied an additional $10.00 credit, bringing your total downtime credit to $20.00 on account BW883011. Both credits will appear on your next bill.
CUSTOMERAll right. Thank you for your help. Goodbye.
AGENTYou’re welcome. Thanks for choosing Brightwave—goodbye!
Healthcare
AGENTRiverbend Health Clinic, this is Alex. How can I help you today?
CUSTOMERHi, this is Arjun Mehta. I am having one dermatology appointment next Wednesday morning.
CUSTOMERBut now I have a work shift at that time.
CUSTOMERI need to move the appointment to after 3 p.m..
CUSTOMERCan you do the needful?
AGENTI can help move your dermatology appointment to after 3 p.m., Arjun Mehta. What’s your date of birth so I can verify your record?
CUSTOMERMarch 17th, 1948.
CUSTOMERSorry. No. 1984. March 17th, 1984.
AGENTThank
CUSTOMERHello.
AGENTHi, I’m
CUSTOMEROkay.
AGENTThank you, Arjun Mehta—your
CUSTOMERAha!
AGENTI have your corrected
CUSTOMERRight.
AGENTYour date of birth is March 17, 1984. I found your appointment with Dr. Okafor next Wednesday at 9:15 a.m. The available slots after 3 p.m. are Tuesday at 3:45 p.m. or Thursday at 4:10 p.m. Which works for you?
CUSTOMERTuesdays do not. Work after all. Because I pick up my daughter. What about Friday?
AGENTFriday’s available appointment is at 11:30 a.m., so it doesn’t meet your after-3-p.m. requirement. Would Thursday at 4:10 p.m. work?
CUSTOMERYes.
CUSTOMERThursday at 4:10 p.m.. Works for me. Please book that slot.
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.
CUSTOMEROkay.
CUSTOMERThank you.
CUSTOMERI have a few. Quick questions.
CUSTOMERFirst.
CUSTOMERIs 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. What’s your next question?
CUSTOMERRight. Where should I park when I come for the appointment?
AGENTThere’s free patient parking in Lot C, with the entrance on River Road.
CUSTOMEROkay.
CUSTOMEROne last thing.
CUSTOMERDoes Doctor Okafor. Accept Aetna insurance?
AGENTI don’t have access to Dr. Okafor’s insurance participation details. Please check with the clinic’s billing team or Aetna to confirm that Dr. Okafor is in-network for your specific plan.
CUSTOMERAll right. I will check with billing then. Thank you for your help.
CUSTOMERGoodbye.
AGENTYou’re welcome,
AGENTArjun. Goodbye!
Home services
AGENTCopperline Home Services, this is Alex. What can I help you with?
CUSTOMERSorry.
CUSTOMERI'm kind of flustered right now.
CUSTOMERThere's a pipe under my kitchen sink that just burst and water's all over the floor.
CUSTOMERIt's heading toward a power strip, and I've got a baby crying right here. Can you send someone out, please?
AGENTYes—we can help. First, take your baby to a dry spot away from the water. Don’t touch the power strip, its plug, or any wet outlets, and don’t step into water near them. If you can reach your water main shutoff without entering that area, turn it off. If you can safely reach your electrical panel by a dry route, switch off power to the kitchen. Don’t go through water to do either. Is anyone hurt, and can you and your baby get safely clear? What’s the service address, including any unit number?
CUSTOMERThank you.
CUSTOMERI'm really nervous about all this.
CUSTOMERNo. Nobody's hurt, and I think we can get clear. I'll just grab the baby and move back. Okay. I'm turning off the water now.
AGENTYou’re doing the right
CUSTOMERYeah.
AGENTKeep yourself
CUSTOMERAre. Right.
CUSTOMERI'm keeping my. Baby away from the water. And we're both safe now.
CUSTOMERSorry. I should have said.
CUSTOMERMy address is 1480 Larkspur Avenue, unit 4D.
CUSTOMERI moved from four B last month, so.
CUSTOMERPlease make sure it's four D.
CUSTOMERIs that okay?
AGENTYes—unit 4D, not 4B. I’ll use your corrected address: 1480 Larkspur Avenue, unit 4D. I’m glad you and your baby are safe; stay away from the wet power strip. The earliest emergency arrival window is today, 2:00–4:00 PM. Your Copperline Care member emergency callout fee is $99. Is that okay to book? What’s the best callback number, and is there a gate code or any access instruction?
CUSTOMERThank you.
CUSTOMERI'm still pretty anxious, but. I appreciate you saying that.
CUSTOMERUh, is there any way someone can come before 2 p.m.?
CUSTOMERI really want to get this fixed as soon as possible.
CUSTOMERSure. My callback number is (503) 555-0139.
AGENTI
CUSTOMERNo.
CUSTOMER0193.
CUSTOMERSorry, it's (503) 555-0193.
CUSTOMERMy gate code is Pound two.
CUSTOMER719.
CUSTOMERWill the $99 fee cover everything, or should I expect extra charges?
AGENTThe $99 is the emergency callout fee, not a confirmed total for repairs. I don’t have repair pricing available, so additional charges may apply. The earliest window offered is today, 2–4 p.m.; I don’t have an earlier arrival available. I have 1480 Larkspur Avenue, unit 4D, callback (503) 555-0193, and gate code #2719. Is that gate code correct, and do
CUSTOMERI.
AGENTTake your time.
CUSTOMERYes.
AGENTThank you. Just to confirm before I book: do you agree to the $99 emergency callout fee for today’s 2–4 p.m. window, with any repair charges additional?
CUSTOMERUm, yeah, I guess I don't really. Have a choice.
CUSTOMERYes. That's fine.
CUSTOMERI'm okay with the $99 call out fee and. I understand repairs could cost extra.
CUSTOMERIs that all right?
CUSTOMERCould you tell me who's coming and how? I'll know when they're on the way.
CUSTOMERAnd, uh, what's the ticket number for this visit, please?
AGENTYes—your emergency visit is booked for today, 2–4 p.m. Dana is your technician and will text you 30 minutes before arrival. Here are the confirmed details:
- Address1480
CUSTOMERI.
AGENTI’m
CUSTOMERAll right.
AGENTYou’re booked with Dana today, 2–4 p.m. Dana will text (503) 555-0193 30 minutes before arrival. - Address: 1480 Larkspur Avenue, unit 4D
- Gate code#2719
- TicketCPL-58210
- Emergency callout fee$99; repairs may cost extra
Until then, keep yourself and your baby away from the wet area and power strip.
CUSTOMERThat's. 1480 Larkspur Avenue, unit 4D. Yes.
CUSTOMERThank you so much for your help. I really appreciate
AGENTYou’re very welcome. We have unit 4D confirmed. Stay safe with your baby, and watch for Dana’s text before arrival.
CUSTOMERit. Goodbye.
CUSTOMERI.
AGENTGoodbye.
AGENTTake care of yourself and your baby.
Insurance
AGENTNorthgate Mutual claims, this is Alex. Are you somewhere safe right now?
CUSTOMERYeah, I'm at home, but I'm a little flustered. My baby's crying, and I just got hit about 20 minutes ago. Nobody's hurt, but I can't drive my car. Sorry.
CUSTOMERIs that going to be a problem?
AGENTNo need to apologize—it’s okay to take a moment to settle your baby. I’m glad you’re home and nobody’s hurt. Since your car can’t be driven, I can help arrange a tow. What’s your policy number and the policyholder’s last name?
CUSTOMERThank you. I appreciate that. Um, my policy number is NG4471903.
CUSTOMERNo, sorry.
CUSTOMERLast one is two.
CUSTOMERAnd the last name is Carter.
CUSTOMERIs that okay?
AGENTYes, that’s okay. Your corrected policy number is NG4471902, under Carter, and I’ve verified it for your 2021 Honda CR-V.
Where is the car now, and what’s a good callback number for the tow driver?
CUSTOMERThank you for double checking. I'm parked right at Southeast fifth Avenue and Alder Street where the accident happened.
CUSTOMERAnd my phone number is (563) 261-8407.
CUSTOMERIs that all right?
AGENTYes, thank you. I have your callback number as (563) 261-8407. What city and state is Southeast Fifth Avenue and Alder Street in? Also, just to make sure I understand: are you and your baby safely at home with the car left at the intersection, or are you still there with the car?
CUSTOMERI'm sorry, I. Should have mentioned I'm still here with the car at the intersection in Portland, Oregon. My baby's with me, but we're both okay.
CUSTOMERIs that all right?
AGENTNo need to apologize. Are you and your baby safely out of traffic, and is the car in a safe spot? If you’re in danger, call 911 first. If you’re safe, I can arrange a tow to Southeast Fifth Avenue and Alder Street in Portland.
CUSTOMERYeah, we're all right. We're out of the way. And nobody's hurt. The car's in a safe spot. Just can't drive it.
CUSTOMERSo could you send a tow truck out here?
CUSTOMERUm, where's it going to be taken?
AGENTEastside
CUSTOMERHello?
AGENTI’m here—sorry about that. 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. To continue opening your claim, you said the accident happened about 20 minutes ago. What happened?
CUSTOMERThanks for. That sorted.
CUSTOMERSo. Um, I was stopped at a red light at Southeast fifth Avenue and Alder Street. And another car hit my back bumper.
CUSTOMERIt's all crushed. And now the rear wheel rubs. Nobody's hurt, but I can't. drive it. The other driver's name is Kevin Lund. He's insured with Pacific Shore.
CUSTOMERHis plate is 7KXR214.
CUSTOMERX, like X-ray.
AGENTThank
CUSTOMERSorry, is that enough detail?
AGENTYes, that’s helpful—no need to apologize. I have that you were stopped at a red light when another car hit your rear bumper, crushing it and causing the rear wheel to rub. The other driver is Kevin Lund, insured with Pacific Shore, and his plate is 7KXR214, with X as in X-ray. Did police attend, and was there a report number? Also, what’s today’s date and the approximate time the collision happened?
CUSTOMERPolice didn't come out, so. There's. No. Report number.
CUSTOMERAnd, um, today's date. I'm sorry. I'm a little flustered.
CUSTOMERCould you remind me what day it
AGENTThat’s okay—take
CUSTOMERis? The accident happened about 20 minutes
CUSTOMERago. Thank you.
AGENTThat’s okay—take your time. I don’t have a reliable current date available, and I don’t want to give you the wrong one. If it’s easy, could you check the date on your phone? Otherwise, I can record the timing as “about 20 minutes before you contacted us.”
I’ve noted that police didn’t attend and there’s no report number.
CUSTOMERI appreciate you being patient.
CUSTOMERUm. I don't want to mess anything up, so if it's all right, just mark it as about 20 minutes before I called. That should be fine, right?
AGENTYes—that’s fine. I recorded it as “about 20 minutes before you called,” with the exact date and time unconfirmed. Your claim is open. Your claim number is NGM-904417—that’s NGM, 904417. Your assigned adjuster is Priya Shah, who will contact you within one business day. Eastside Towing is dispatched to take your car to the Northgate partner repair shop at 9th and Morrison. Please keep yourself and your baby safely out of traffic while you wait.
CUSTOMERThank you so much. That's a
AGENTYou’re very
CUSTOMERrelief. It's just. Um.
CUSTOMERI know I'm. Probably.
AGENTYou’re welcome. Take your time—I’m here. What’s on your mind?
CUSTOMERSorry. I just want to make sure. Do I have to pay anything out of pocket for this?
CUSTOMERLike. Is there a deductible?
AGENTYou don’t need
CUSTOMERAnd since
AGENTYes—your policy
CUSTOMERhe hit. Okay.
CUSTOMERSo. All right. Sorry,
AGENTYes—your policy has a $500 collision deductible. That’s the amount you would generally pay toward repairs if your collision coverage pays for the damage. Since you were hit by another driver, your adjuster can explain whether his insurer will pay or whether your deductible might be recovered. I can’t promise that you won’t have to pay it. Your towing coverage pays for up to 25 miles. If you need a rental, your coverage is $40 per day for up to 30 days, starting the day your claim is opened.
CUSTOMERbut. He hit me, so I shouldn't have
AGENTNo need to apologize.
CUSTOMERto pay that. Thank
AGENTI understand—it feels
CUSTOMERyou. Okay. Thank you for explaining.
AGENTI understand—it’s frustrating to think about paying when you were hit from behind. The $500 deductible applies if your collision coverage pays for the repairs; being hit by someone else doesn’t automatically waive it. Whether his insurer pays, or whether any deductible you pay can be recovered, still needs to be determined. Priya can review that with you.
CUSTOMERSorry. I'm just. Anxious about all this. Will my premium go up because
AGENTIt’s understandable to feel
CUSTOMERof this? And, um, how do I get the rental
AGENTIt’s understandable to feel anxious. I can’t tell from the information available whether this claim will affect your premium, and I don’t want to guess. Priya can help you get that question answered. For a rental, your coverage is $40 per day for up to 30 days, starting today when your claim was opened. I can’t book one through this chat. Ask Priya how to arrange it and whether billing can go directly to Northgate or you’ll need to submit receipts. Before booking, confirm any costs above your covered amount.
AGENTYour claim number is NGM-904417. Priya is expected to contact you within one business day.