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.
#11 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.
#1 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.
#440 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.
#319 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.
#501 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.
#345 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.
#428 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.
#87 of 661 on the board · best 0.45 s
Latency spikes
Turns where the response took far longer than the rest of the conversation.
#496 of 661 on the board · best 0.0 per call
By industry
| Industry | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| Healthcare | 94% | 4.25 s | — |
| Home services | 98% | 4.07 s | — |
| Insurance | 100% | 3.75 s | — |
| Customer support | 100% | 4.26 s | — |
By caller
| Caller | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| US English, quiet line | 96% | 3.73 s | — |
| Spanish-accented English, city street | 100% | 3.97 s | — |
| Indian English, busy café | 98% | 3.92 s | — |
| Southern US, flustered, crying baby | 100% | 4.46 s | — |
| Fast and interrupting, construction outside | 96% | 4.36 s | 100% |
The calls behind the numbers
Customer support
AGENTBrightwave Internet support, this is Alex. What’s going on with your service?
CUSTOMERHi, Alex. My Internet has been down for about two hours now. I'm working from home, but I'm calling from a cafe because I can't get online. There is a road crew digging up the street outside my place. I already rebooted the gateway twice and checked all the cables. The Internet light is blinking orange. Can you check if the roadwork is causing this? My account number is BW883017. Sorry. Last digit is one.
AGENTThanks—your corrected account number is BW883011. I’ll check for an area outage first; no need to repeat the reboot or cable checks.
CUSTOMERThat's right. I already did those steps twice. Kindly check if the road crew is causing the outage, please.
AGENTThere’s no known area outage or maintenance reported. That doesn’t rule out damage from the roadwork—I’ll test your line now.
CUSTOMEROkay. Go ahead and do the need for.
AGENTThe remote reset worked—your gateway is now registered and online. Devices may take about two minutes to reconnect. The line test showed a good signal, so I couldn’t confirm the roadwork caused the interruption. No technician is needed at this
CUSTOMERAlright. The Internet light just went solid white. How long until my devices reconnect fully? Also, I lost two hours of work today. Can you apply a credit for the downtime?
AGENTYour devices should reconnect within about two minutes of the reset. If one still won’t connect, try toggling its Wi-Fi off and on. I’ve applied a $10.00 downtime credit to account BW883011. It will appear on your next bill.
CUSTOMEROkay. I appreciate that. But I lost two hours of work. Can you make the credit more than $10?
AGENTI understand—two hours offline while working is a real disruption. The $10 credit is confirmed, but I can’t select a higher amount with the credit tool available to me. I don’t want to promise an additional credit I can’t verify.
CUSTOMERUh-huh. Understood. Thanks for confirming the credit and helping with the reset. Goodbye, Alex.
AGENTYou’re welcome. Glad we got you back online. Goodbye!
Healthcare
AGENTRiverbend Health Clinic, this is Alex. How can I help you today?
CUSTOMERHello. This is Arjun Mehta. I'm having one dermatology appointment next Wednesday morning. But I need to move it because I'm scheduled for work there. Can you help me reschedule? I can only come after 3PM.
AGENTOf course, Arjun Mehta. I’ll look for a dermatology appointment after 3 PM. To verify your record, what’s your date of birth?
CUSTOMER03/17/1948. Sorry. No. 1984. 03/17/1984.
AGENTThank you—Arjun Mehta, date of birth March 17, 1984.
CUSTOMERYeah. That's right. So what times are available after 3PM?
AGENTDr. Okafor has Tuesday at 3:45 PM or Thursday at 4:10 PM available. Which works better for you?
CUSTOMERTuesdays do not work after all. Because I pick up my daughter then. What about Friday? Is anything open after 3PM on Friday?
AGENTThe availability returned for Dr. Okafor shows only Friday at 11:30 AM, so nothing after 3 PM on Friday. Thursday at 4:10 PM still fits your timing—would that work?
CUSTOMERYes. Thursday at 04:10PM works for me. Kindly book that slot. Is there any fee for changing the appointment?
AGENTYour 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 at 3:55 PM with your photo ID and insurance card. Changes made more than 24 hours before the original appointment are free; later changes carry a $40 fee. I can’t confirm from the information provided whether that fee applies to this change.
CUSTOMEROkay. Understood. Where should I park for the appointment at Main Campus, Building B?
AGENTFree patient parking is available in Lot C, with the entrance on River Road, for your appointment at Main Campus, Building B.
CUSTOMERRight. Thanks.
CUSTOMERDoes doctor Okafor accept Aetna insurance?
AGENTI don’t have insurance-network information for Dr. Okafor. Please check with the clinic’s billing team or Aetna to confirm whether Dr. Okafor is in-network for your specific plan.
CUSTOMERUnderstood. Thanks for your help. And goodbye.
AGENTYou’re welcome,
AGENTArjun. Goodbye, and take care!
Home services
AGENTCopperline Home Services, this is Alex. What can I help you with?
CUSTOMERHey. Hi. Sorry. I've got a bit of a situation here, and I'm kinda flustered. There's a pipe under my kitchen sink that just burst and water spreading all over the floor heading right toward a power strip. I got a baby crying in the background, so I need some help fast. What should I do first? Can y'all send someone out?
AGENTYes, I can help arrange a technician—but first, keep yourself and your baby away from the water and power strip. - Don’t touch or unplug the power strip, and don’t step into water near it. - If you can reach your electrical panel by a dry, safe route, turn off the kitchen circuit. Otherwise, stay clear. - Shut off the water main if you can reach it safely. Only use the valves under the sink if you can reach them without getting near the water or electrical hazard. If you see sparks or smoke, or anyone has been shocked, move to safety and call 911. Are you and your baby safely away from the water? What’s the service address, including any unit number, and your best callback number?
CUSTOMERYeah. We're both clear of the water now. Thank you for checking. I'm gonna try to shut off the water main. Just one sec.
CUSTOMEROkay. I think it's off. The water stopped. The address is 1480 Larkspur Avenue, and I'm in Unit 4 D, not 4 B. I moved last month. My callback number is (503) 555-0139.
CUSTOMERNo. 0193. Is that enough?
CUSTOMERSorry. I just wanna make sure everything's right.
AGENTYes—thank
CUSTOMERHello?
CUSTOMERAlright. Thanks for confirming. Could you let me know how much the visit's gonna cost before I agree to anything? And is there any way someone can come before two? I just wanna make sure it gets handled quick.
AGENTI’m here—sorry for the interruption. As a Copperline Care member, your emergency callout fee is $99. That’s the callout fee, not a quote for the repair. The earliest arrival window available is today, 2–4 PM; I don’t have an earlier window to offer. Your request is CPL-58210, for 1480 Larkspur Avenue, Unit 4D—not 4B, with callback number (503) 555-0193. I haven’t booked the visit yet. Do you agree to the $99 callout fee and the 2–4 PM window? Is there a gate code or any access instruction we’ll need? Until then, leave the water off and keep everyone away from the wet area and power strip.
CUSTOMEROkay. I guess I don't have much choice. So, yes, I agree to the $99 callout fee, and the two to 4PM window. Is that okay? And... Yeah. You'll need the gate code. It's pound 2719. Sorry. Is there any way to know who's coming or how I'll know they're on the way? I just wanna be sure I get the right person. And the ticket number is CPL58210. Right?
AGENTYes—you’re booked for today, 2–4 PM, with the $99 emergency callout fee you approved. - Technician: Dana
- On-the-way noticeDana will text (503) 555-0193 about 30 minutes before arrival. - Address: 1480 Larkspur Avenue, Unit 4D
- Gate code#2719
- TicketCPL-58210—you have it right. You can ask the arriving technician to confirm their name and your ticket before letting them in. Keep the water off and stay away from the wet area and power strip until it’s safe.
CUSTOMERAlright. Thank you for everything, Alex. I appreciate you helping me out. Goodbye.
AGENTYou’re welcome.
AGENTTake care of yourself and your baby, and keep clear of the wet area. Goodbye.
Insurance
AGENTNorthgate Mutual claims, this is Alex. Are you somewhere safe right now?
CUSTOMERYeah. I'm fine. Just standing next to the car, but there's some construction work out outside, so it's loud. We go through this quickly?
AGENTYes—we’ll keep it quick. Is anyone injured, and are you safely away from traffic and the construction? If anyone is hurt or in danger, call 911 first. What’s your policy number and last name?
CUSTOMERRight. Nobody's hurt. My policy number is NG4471903. No. Sorry. Last one is two. Last name is Brooks.
AGENTThank you, Brooks. Your corrected policy number is NG4471902.
AGENTYour policy is verified
CUSTOMERYep.
CUSTOMERYeah. I know. Listen. The other driver is Kevin Lund, insured with Pacific Shore. Plate is 7KX like X-ray, R214. I need to know how much I have to pay.
AGENTYour collision deductible is $500. I have Kevin Lund, insured with Pacific Shore, and plate 7KXR214—is that correct? When and where did the accident happen, what happened, and did police attend? Can your car be driven safely?
CUSTOMERYeah.
CUSTOMERRight. That's correct. It happened about twenty minutes ago at Essie 5th Avenue and Alder Street. I was stopped at a red light, he hit the back of my car. Police didn't come. The back bumper's crushed, the rear wheel rubs, so I can't drive it. But he hit me, so I should not have to pay that deductible. Right? Do I get a rental car?
AGENTI can’t promise the deductible will be waived because he hit you. Your policy’s collision deductible is $500; the adjuster will review how it applies.
CUSTOMEROkay.
CUSTOMERYeah.
CUSTOMERGot it. But I really need a tow truck. Where will the cargo? Is my premium going to go up?
AGENTYour rental coverage is $40 per day for up to 30 days, starting the day the claim is opened. Towing is covered up to 25 miles. I can arrange a tow, but I don’t yet have a destination confirmed. I also can’t
CUSTOMERMhmm.
CUSTOMERYeah. That's...
CUSTOMERFine. Just get the tow started. Make sure I hear the claim number before I go.
AGENTYour claim number is NGM-904417—N G M, nine zero four four one seven. Your adjuster is Priya Shah. To send the tow to the right place: did you mean SE 5th Avenue and Alder Street, and in what city? What phone number can the tow
CUSTOMERRight.
CUSTOMERYeah.
CUSTOMEREstee 5th Avenue and Alder Street. It's Portland. For the phone number, it's (507) 698-9041.
AGENTEastside Towing has been dispatched, with an estimated arrival in 45 minutes. They’ll take the car to the Northgate partner repair shop at 9th and Morrison. Your callback number is (507) 698-9041. Your claim number again is NGM-904417.
CUSTOMERYep. That's what I needed. For your help. Goodbye.
AGENTYou’re
AGENTwelcome. Stay safely away from traffic while you wait. Goodbye.