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
#1of 654 stacks
63 / 100
The leader
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.
#25 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.
#523 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.
#199 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.
#9 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.
#451 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.
#417 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.
#116 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.
#369 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.
#319 of 661 on the board · best 0.45 s
Latency spikes
Turns where the response took far longer than the rest of the conversation.
#613 of 661 on the board · best 0.0 per call
By industry
| Industry | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| Healthcare | 97% | 3.97 s | — |
| Home services | 93% | 3.82 s | — |
| Insurance | 100% | 3.61 s | — |
| Customer support | 100% | 3.87 s | — |
By caller
| Caller | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| US English, quiet line | 98% | 3.50 s | — |
| Spanish-accented English, city street | — | — | — |
| Indian English, busy café | 94% | 3.70 s | — |
| Southern US, flustered, crying baby | 100% | 3.82 s | — |
| Fast and interrupting, construction outside | 96% | 4.16 s | — |
The calls behind the numbers
Customer support
AGENTBrightwave Internet support, this is Alex. What’s going on with your service?
CUSTOMERHi. This is Lucia Ramirez. My internet has been down for about two hours now. I work from home and there's a road crew digging outside. Can you help me check what's going on?
AGENTHi Lucia. To pull up your account, could you give me your account number or the phone number on the account? And what's happening with your internet?
CUSTOMERI'm not hearing anything from your side, so I'll let you go. Goodbye.
Healthcare
AGENTRiverbend Health Clinic, this is Alex. How can I help you today?
CUSTOMERHi. Yes, I have a dermatology appointment next Wednesday morning, but I need to reschedule it because I just got a work shift. My name is Lucia Ramirez. Can you help me move it to a time after 3 p.m.?
AGENTI can help with that, Lucia. First I need to verify your identity. Can you give me your date of birth?
CUSTOMERYeah, sure. March 17th. Sorry. No. March 17th. Cuatro.
AGENTThanks, Lucia. I want to make sure I have your date of birth right. "Cuatro" sounds like you might be giving the year or part of it, but I don't want to guess. Could you give me the full date, including the
CUSTOMERRight. Sorry. My date of birth is March 17th, 1984. Yes, of course. It's March 17th, 1984.
AGENTThank you. Let me verify that.
AGENTDr. Okafor has two openings after 3 PM: - Tuesday at 3:45 PM. - Thursday at 4:10 PM. There's nothing after 3 PM on Wednesday itself. Would either of those work for you?
CUSTOMEROkay. Um. Actually, Tuesdays do not work for me because I pick up my daughter that day. What about Friday? If not, Thursday at 410 should be fine.
AGENTFriday's only opening is at 11:30 AM, which doesn't fit your after-3 PM requirement. The one that fits everything is Thursday at 4:10 PM. Would you like me to move your appointment with Dr. Okafor from next Wednesday at 9:15 AM to Thursday at 4:10 PM?
CUSTOMERYes. That's right. Please move it to Thursday at 4:10 p.m.. By the way, is there any fee for changing the appointment?
AGENTI'll make the change and check the policy on fees at the same time.
AGENTSince the change policy says changes made more than 24 hours ahead are free, this one won't cost you anything. I'll go ahead and move it now.
AGENTYour appointment
CUSTOMEROkay. Thank you.
CUSTOMEROh, sorry. I think you cut off. Could you repeat that?
AGENTOf course, Lucia. Your dermatology appointment with Dr. Okafor has been moved to Thursday at 4:10 PM, at Main Campus, Building B. Your confirmation code is RVB-3M8P2. Please arrive 15 minutes early with your photo ID and insurance card. On the fee question: changes made more than 24 hours before the appointment are free, and later changes carry a $40 fee. Since this change was made well ahead of time, there's no charge. Is there anything else I can help you with?
CUSTOMERYeah. Just a couple more quick things. Where should I park when I come for the appointment?
AGENTFree patient parking is in Lot C, with the entrance on River Road. Is there anything else you need?
CUSTOMERYes. One last thing. Does Doctor Okafor accept Aetna insurance?
AGENTI don't have any information on which insurance plans Dr. Okafor or the clinic accepts, so I don't want to guess. Please call your Aetna member services line or ask the clinic's front desk or billing office. It's worth confirming before Thursday, and you'll need to bring your insurance card to the appointment anyway. Is there anything else I can help with?
CUSTOMERNo. That's all. Thank you for your help. Goodbye.
AGENTYou're welcome, Lucia.
AGENTYour appointment is Thursday at 4:10 PM with Dr. Okafor, confirmation code RVB-3M8P2. Take care, goodbye!
Home services
AGENTCopperline Home Services, this is Alex. What can I help you with?
CUSTOMERHi. I have a problem in my apartment. The pipe under my kitchen sink just burst and there's water spreading all over the floor toward a power strip. I haven't shut the water off yet. What should I do?
AGENTSafety first, so please do this right now: 1. Stay away from the power strip and anything plugged in near the water. Don't touch it or step in the water near it. If you can safely reach the breaker panel without walking through water, switch off the breaker for the kitchen. 2. Shut off the water. First try the shutoff valves under the sink, the small knobs on the pipes, turning them clockwise. If that doesn't work or you can't reach them safely, shut off the main water valve for the apartment. It's often near the water heater, in a utility closet, or where the main line enters the unit. Are you able to get the water off safely? And is everyone okay, with no one hurt or shocked?
CUSTOMERYeah, I just turned off the valves under the sink. The water stopped. Everyone is okay. Nobody is hurt. And I kept away from the power strip. Can you send someone to fix this?
AGENTGlad everyone is safe and the water is off. Let me pull up your account and check what we have available. What's the best phone number or the street address for the service?
CUSTOMERSure. The address is one 480 Larkspur Avenue. My phone number is 503 5550139. No. 0193.
AGENTJust to confirm, I heard the address as 1480 Larkspur Avenue and the phone as 503-555-0193. I'll look up by both, and check availability at the same time.
AGENTThe lookup didn't run in that step, so let me do it now.
CUSTOMEROkay, so just to clarify, it's Larkspur Avenue, not Lark's Avenue. And I moved from unit four B to 4D last month, in case it shows the old unit.
AGENTThanks, that's Larkspur Avenue, and I'll use unit 4D instead of the 4B on file. I found your account, and you're a Copperline Care member. The earliest option is an emergency visit today, 2:00 PM to 4:00 PM. Because you're a Care member, the emergency callout fee is $99 rather than the standard $149. Does that window and fee work for you? Also, is there a gate code or any access instructions for the technician, and is 503-555-0193 the best number to reach you?
CUSTOMERCan anyone come before 2 p.m.? I really need it as soon as possible. And yes, the gate code is pound 2719 and 503 550 50193 is the best number.
AGENTI only see one emergency window today, 2:00 PM to 4:00 PM, and nothing earlier, so I can't promise anything sooner. The only other option is tomorrow morning, which is later, so 2:00 PM is the earliest. I also want to check the callback number. You said 503
CUSTOMERYes, (503) 555-0193. That's right. And, uh okay, the 2 to 4 window is fine. Who will be coming and how will I know they're on the way? And can you give me the ticket number? Also, just to confirm, the emergency visit is $99, right?
AGENTYes, the emergency callout is $99 with your Care membership. I'll get the ticket opened and the visit booked now.
CUSTOMERAll right. Could you tell me who is coming and how I'll know when they are on the way? And, uh, what's the ticket number for this visit?
AGENTYou're booked. Here are the details: - Ticket number: CPL-58210. - Technician: Dana. - Window: today, 2:00 PM to 4:00 PM. - Fee: $99 emergency callout (Copperline Care rate). - Address: 1480 Larkspur Avenue, Unit 4D. - Gate code: pound 2719. - Callback number: 503-555-0193. Dana will text that number about 30 minutes before arriving, so keep your phone nearby. Until then, keep the water off at the sink valves, stay clear of the power strip, and don't plug anything in near the wet area. If you can do it safely, mop up or towel off the standing water. If the water starts again or you see sparks, a burning smell, or anything electrical acting up, call us right back. Is there anything else I can help with?
CUSTOMERNo, that covers everything. Thank you. Alex. Goodbye.
AGENTYou're welcome. Dana will be there between 2:00 and 4:00 PM. Stay safe, and goodbye.
Insurance
AGENTNorthgate Mutual claims, this is Alex. Are you somewhere safe right now?
CUSTOMERYeah, I'm at home now. I just got back a bit ago. My baby's here crying, so I'm kind of flustered, but yeah, we're safe. Nobody got hurt. Is that okay?
AGENTThat's completely okay, and I'm glad you and your baby are safe. Take whatever time you need. We can go at your pace. To get started, can you give me your policy number and your last name so I can pull up your policy?
CUSTOMERYeah, yeah. Let me grab that for you. The policy number, I think it's NG4471903. Oh wait, sorry, the last one is two, not three. So NG4471902. And my last name is Carter. Is that gonna work?
AGENTNo problem at all. I'll use the corrected number, NG4471902, and look it up now.
AGENTThank you, Ms. Carter. I found your policy and it's verified. Just to confirm, the number I have is NG4471902, and it's for a 2021 Honda CR-V. Now I'd like to get the details of the accident. When did it happen, and where?
CUSTOMERIt was about 20 minutes ago. I think I was stopped at a red light at Fifth Avenue and Alder Street. Sorry. Is that the right way to give the location? I just want to make sure you got it.
AGENTThat's a good way to give the location. I have it as Fifth Avenue and Alder Street, about 20 minutes ago, while you were stopped at a red light. Can you tell me what happened? Take your time and tell it in your own words.
CUSTOMEROkay. Um, so I was just sitting there at the light, waiting for it to turn green, and another car came up behind me and hit the back of my car. It was pretty hard. The bumpers crushed, and now the rear wheels rubbing real bad so I can't drive it. Nobody got hurt, thank goodness. But I'm a little worried about what happens next. Is that enough detail, or do you need more?
AGENTThat's plenty of detail, thank you. You were stopped at the light and were rear-ended, the bumper is crushed, and the rear wheel is rubbing, so the car can't be driven safely. Nobody was hurt. Next I need the other driver's information. Do you have their name, their insurance company, and their license plate number? If you got the plate, please read it out slowly so I get it right. I'll also ask about the car itself in a moment. You said you got home a bit ago, so I want to know where it is right now.
CUSTOMERYeah, I've got all that. The other driver's name is Kevin Lund. His insurance is with Pacific Shore. The license plate is 7KX, like X-ray R 214. Sorry, is that clear? I just want to make sure you get it right. And, um, my car's at home now. I managed to get it here after the accident, but I can't drive it any farther. The wheel's rubbing and it sounds awful. Is that okay, or do you need more about the location?
AGENTThanks, that's clear. Let me read the plate back so we're sure it's right. I have 7 K X R 2 1 4, the "X" as in X-ray. Is that correct? Yes, I need one more thing for the car. What's your home address, so I know exactly where it's parked? Also, did police come to the scene, and if so, do you have a report number? Once I have those, I can send a tow truck to your home. I'd also like a phone number the tow driver can call you on.
CUSTOMERSo, um. No, the police didn't come to the scene, so I don't have a report number. Sorry. Is that going to be a problem? My home address is, uh. Do you need the whole thing? And for the phone number, the tow driver can call me at (573) 229-5170. Is that okay, or do you need anything else?
AGENTNo problem at all. Police not coming is very common, and I'll note that on the claim. Yes, I do need the whole address. The tow driver needs the street number, street name, and city to find your car. Please read it to me slowly.
CUSTOMERAll right. Sure. My address is 1146 Southwest Alder Street, Portland, Oregon. Sorry. Is that enough, or do you need the zip code, too? I just want to make sure the tow driver can find me.
CUSTOMERHello?
AGENTI'm here, and that's very close to enough. A zip code would help the tow driver, so if you have it, go ahead and give it to me. If you don't have it handy, I can go with the street address and city. While we sort that out, I'll open your claim and check your coverage. I'm doing both now.
CUSTOMEROkay. Sorry. I think the zip code is 97214. Oh. That's right. Please tell me if it works. And, uh, I appreciate you checking my coverage. Is there anything else you need for the claim?
AGENTThe zip code works. I'll open the claim and set up the tow
CUSTOMERAll right.
CUSTOMEROkay.
AGENTI still need to open the claim, so I'm doing that now with everything you've told me.
CUSTOMERAll right. Thanks. Uh, just let me know if you need anything else from me. I'm a little anxious about what comes next. Will I have to pay anything out of pocket, or is that covered since he hit me? And do I get a rental car while mine's being fixed? Sorry. I just want to make sure I don't miss anything.
AGENTYour claim is open and the tow is on its way. Here is where things stand. Your claim. - Claim number: NGM-904417. - Policy number: NG4471902. - Other driver's plate: 7 K X R 2 1 4. - Your adjuster is Priya Shah, and she will contact you within one business day. Your tow. - Eastside Towing is on the way to 1146 Southwest Alder Street, with an estimated arrival of about 45 minutes. - They will call you at (573) 229-5170 if they need anything. - They will take the car to our partner repair shop at 9th and Morrison. - Your policy covers towing up to 25 miles. Your questions. - Out of pocket: Your policy has a $500 collision deductible. I can't tell you whether you will have to pay it, because who is at fault and how the other driver's insurer responds are decided in the claim review. Priya can explain that when she calls. - Rental: Yes, your policy includes rental reimbursement. It pays $40 per day for up to 30 days, counted from the day the claim was opened, which is today. You didn't miss anything, and you did everything right. If you have photos of the damage or a photo of the other driver's information, keep them handy for Priya. Is there anything else I can help with while you're here?
CUSTOMERNo, I. Think that covers it. Thank you. Sorry for all the questions. I just worry about missing something. I got the claim number and I'll wait for Priya to call. Uh, thank you for being so patient.
CUSTOMERGoodbye.
AGENTYou never need to apologize. Those were all good questions. Your claim number is NGM-904417,
AGENTand Priya Shah will be in touch within one business day. Eastside Towing should reach you in about 45 minutes. Take care of yourself and your baby, Ms. Carter. Goodbye.