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
#38of 654 stacks
58 / 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.
#148 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
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.
#265 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.
#122 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.
#53 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.
#94 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.
#248 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.
#246 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.
#436 of 661 on the board · best 0.45 s
Latency spikes
Turns where the response took far longer than the rest of the conversation.
#248 of 661 on the board · best 0.0 per call
By industry
| Industry | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| Healthcare | 94% | 3.50 s | — |
| Home services | 88% | 3.09 s | — |
| Insurance | 98% | 3.37 s | — |
| Customer support | 98% | 3.63 s | — |
By caller
| Caller | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| US English, quiet line | 96% | 3.33 s | — |
| Spanish-accented English, city street | 96% | 3.45 s | — |
| Indian English, busy café | 94% | 3.30 s | — |
| Southern US, flustered, crying baby | 98% | 3.75 s | — |
| Fast and interrupting, construction outside | 90% | 3.26 s | — |
The calls behind the numbers
Customer support
AGENTBrightwave Internet support, this is Alex. What’s going on with your service?
CUSTOMERHey, Alex. My internet's been down for about two hours. I work from home, so this is pretty urgent. There's a road crew digging up the street right outside. I've already rebooted the gateway twice and checked every cable. The internet light is blinking orange. Is the construction the cause? Can you get this fixed fast?
AGENTThanks for letting me know what you’ve already tried. I checked your account (ending 883011) and there’s no area outage reported, even with the road work nearby.
CUSTOMERYep. That's right. I really need this fixed. Can you check if the road crew is causing the outage?
AGENTI've checked your account (BW-883011) and there’s no outage reported for your address, so the roadwork isn’t showing as a known cause right now. The line test showed a good signal to your gateway, but it lost registration. I just sent a remote reset—your gateway should be coming back online now. Devices may take about two minutes to reconnect. Please let me know if your internet starts working again, or if you’re still having trouble after a couple of minutes.
CUSTOMERThe light just went solid white. How long until my device is actually reconnect? I need a credit for the downtime. Can you help with that?
AGENTYour gateway’s solid white light means it’s back online—most devices should reconnect within a minute or two.
CUSTOMERRight.
AGENTI’ve applied
CUSTOMERYup. I know.
AGENTUnderstood!
CUSTOMERRight, I got it. So about the credit. What can you do for the two hours I lost?
AGENTI've credited $10.00 to your account (BW-883011) for the two hours of downtime. You’ll see this on your next bill. If you need anything else, just let me know.
CUSTOMERTwo hours of work. Can you make it more than that?
AGENTI understand how important your work time is. The standard credit for a two-hour outage
CUSTOMEROkay.
AGENTThanks
CUSTOMERYeah, exactly. Can you bump up the credit?
AGENTI understand your frustration, but the standard credit for a two-hour outage is $10.00, which I’ve already applied to your account BW-883011. If you experience additional downtime
CUSTOMERRight.
AGENTIf
CUSTOMERAll right. Thanks for your help. Goodbye.
AGENTYou’re welcome. Goodbye! If you need anything else, just reach
AGENTout.
Healthcare
AGENTRiverbend Health Clinic, this is Alex. How can I help you today?
CUSTOMERHey. Hi, Alex. Sorry. I've got a baby crying in the background. I'm a little frazzled.
CUSTOMERI need to move my dermatology appointment next Wednesday morning. I just got scheduled for a work shift then, and I can only come after 3 p.m.. Could you help me with that, please?
AGENTOf course, I can help you reschedule your dermatology appointment.
CUSTOMERI. Thank you. I really appreciate it. I'm just worried about messing this up. So can you tell me what you need for me to get started?
AGENTNo problem at all! To get started, I just need your full name and date of birth. That helps me find your appointment and verify your identity. Could you provide those details, please?
CUSTOMERSure. Uh, my name is Wade Carter, and my date of birth is March 17th, 1948. Sorry, no, 1984. March 17th, 1984. Is that okay?
AGENTThanks, Wade Carter, March 17th, 1984. I found your dermatology appointment with Dr. Okafor next Wednesday at 9:15 a.m. Dr. Okafor has open slots after 3 p.m. on Tuesday at 15:45 and Thursday at 16:10. Would either
CUSTOMEROh. Um. Sorry. Tuesdays won't work for me after all. I pick up my daughter then. Could you tell me if there's anything available on Friday instead?
AGENTThe only available slot with Dr. Okafor on Friday is at 11:30 a.m. There are no afternoon slots that day—would you like to try Thursday at 16:10, or check for later Friday times on a future week?
CUSTOMERSorry. I think Thursday at 1610. That's 4:10 p.m.. Should work. Can you book me for that, please?
CUSTOMERAnd, uh, is it all right if I ask, is there any fee for changing the appointment?
AGENTYour dermatology appointment with Dr. Okafor is now rescheduled for Thursday at 4:10 p.m. Your confirmation code is RVB-3M8P2. Please arrive 15 minutes early with photo ID and your insurance card. There’s no fee for changing your appointment, since you did it more than 24 hours in advance. Is there anything else I can help you with?
CUSTOMERThanks for letting me know about the fee. Uh, one more thing. Where should I park when I come in? Is there a lot, or do I need to use the street?
AGENTThere’s free patient parking in Lot C, with the entrance on River Road. You don’t need to use street parking. If you need directions or anything else, just let me know!
CUSTOMERAll right. That helps a lot. Uh. Last question. Does Doctor Okafor accept Aetna insurance? I just want. To make sure everything's covered. Sorry if I'm being a bother.
AGENTYou’re not a bother at all! I don’t have provider-specific insurance details, so it’s best to call our front desk or check with your insurance directly to confirm
CUSTOMERI. Okay. Thanks for checking. Could you please read back the new appointment time and the confirmation code? Just so I know I've got it right? Sorry, I just want to be sure nothing gets lost.
AGENTOf course! Your new dermatology appointment with Dr. Okafor is scheduled for Thursday at 4:10 p.m. Your confirmation code is RVB-3M8P2. If you need anything else, just let me know!
CUSTOMERThat's perfect. Thank you so much. I think I'm all set. Goodbye.
AGENTYou’re very welcome, Wade! Glad I could help. Take care, and have a great day. Goodbye!
Home services
AGENTCopperline Home Services, this is Alex. What can I help you with?
CUSTOMERHi, Alex. I've got an emergency here. A pipe under my kitchen sink just burst and water's pouring out, spreading toward a power strip. I need someone out ASAP. What's the fastest way to get this handled?
AGENTThank you for letting me know—your safety is the most important thing right now. Please immediately shut off your main water supply to stop the flow. If you’re not sure where it is, it’s usually near where the water line enters
CUSTOMERYep, I know where it is. Doing that now.
CUSTOMEROkay. Water stopped. What do I do next? How soon can someone get out here? I'm in unit 4D at 1480 Larkspur Avenue, not four B.
AGENTYou did the right thing by shutting off the water. Next, stay clear of any outlets or power strips near the water to avoid electrical hazards. Here’s what I can do: - The earliest emergency window is today between 2:00 PM and 4:00 PM. - Because you’re a Copperline Care member, the emergency callout fee is $99. Is that okay to proceed? I’ll
CUSTOMERGot it.
AGENTThank you.
CUSTOMERCan anyone come before?
CUSTOMERRight.
CUSTOMERBut I need someone before two, if possible. Is there any way to get an earlier slot?
AGENTCurrently, the earliest emergency window available is between 2:00 PM
CUSTOMERGuide?
AGENTAbsolutely.
CUSTOMERAll right.
AGENTStay clear of the affected area, especially near the power strip and any outlets. Since the water is off, here’s what you can do until the technician arrives: 1. If it’s safe, unplug the power strip—but only if you can do so without touching water. 2. Mop up or soak up any standing water with towels to prevent it from spreading further.
CUSTOMERGuys.
CUSTOMERUnderstood. Can you confirm who'll be coming and how? I'll know when they're on the way. Also, what's the ticket number for this visit?
AGENTCertainly! Here’s your booking confirmation: - Technician: Dana will be coming out. - Arrival window: Today
CUSTOMEROkay. Who's Dana? Are they with Copper line or a contractor? How will I know when they're close? Can you give me the ticket number for this?
AGENTDana is one of our Copperline Home Services technicians—fully trained and part of our team, not a contractor. You’ll get a text message to your callback number about 30 minutes before Dana arrives, so you’ll know when they’re on their way. Your ticket number for this emergency visit is CPL-58210. To confirm: your service address is 1480 Larkspur Avenue, unit 4D. If you have any gate codes or access instructions, please let me know. Let me know if you have any other questions or need to update your contact details.
CUSTOMERYeah, that's all correct. The gate code is pound 2719. My callback number is (503) 555-0139.
CUSTOMERNo. 0193. Thanks for your help. Alex. Goodbye.
AGENTThank you for confirming. Your service address is 1480 Larkspur Avenue, unit 4D. Gate code: #2719. Callback number: (503) 555-0193. Ticket number: CPL-58210.
CUSTOMERYeah. Got it.
AGENTGreat! You’re
AGENTall set. Our technician Dana will text you about 30 minutes before arrival. If anything changes, just give us a call. Thanks for contacting Copperline Home Services—take care!
Insurance
AGENTNorthgate Mutual claims, this is Alex. Are you somewhere safe right now?
CUSTOMERYeah, I'm standing next to my car, but I'm on a pretty busy street. There's a lot of traffic going past. Nobody is hurt. But my car got hit about 20 minutes ago.
AGENTThank you for letting me know, and I’m glad to hear no one is injured. If you feel unsafe standing there, please try to move to a safer spot, away from traffic if possible. I’ll help you get your claim started. Can you please provide your policy number and your last name so I can verify your policy?
CUSTOMERSure, my policy number is NG4471903. No, sorry, last one is two. My last name is Ramirez.
AGENTThank you, Mr. Ramirez. I have your policy number as NG4471902. Next, I’ll need a few details: - Where did the accident happen? - Can you describe what happened? - What is the other driver’s name, their insurer, and their license plate number? - Did the police attend, and is there a report number? Also, do you think your car can be driven safely, or will you need a tow?
CUSTOMERYeah, so about 20 minutes ago, I was stopped at a red light at SW Fifth Avenue and Alder Street. Another car hit the back of my car. The back bumper is crushed and the rear wheel rubbed, so I can't drive it. The other driver is Kevin Lund, insured with Pacific Shore. His license plate is 7KXR214X like X-ray. Police did not come, so there's no report number. I will need a tow, please.
AGENTThank you, Mr. Ramirez. Here’s what I have for your claim: - Policy number: NG4471902. - Other driver: Kevin Lund, insured with Pacific Shore, license plate 7KXR214X. - Claim
CUSTOMERThat's right. Yes. And just to check, how much do I have to pay for this? Will there be a deductible?
AGENTAccording to your policy, you have a $500 collision deductible. That means you'll need to pay $500 toward the repair costs, and your insurance covers the rest. Your towing is covered up to 25 miles, and you have rental reimbursement up to $40 per day for up to 30 days, starting from today. Your claim number is NGM-904417. The tow truck from Eastside Towing is on its way and should arrive in about 45 minutes. Your car will be taken to our partner repair shop at 9th and Morrison. If you have more questions, your assigned adjuster, Priya Shah, will contact you within one business day. Is there anything else I can help with right now?
CUSTOMEROkay. Thank you. But, um, he hit me, so I should not have to pay the deductible, right? Also, will my premiums go up because of this?
AGENTI understand your concerns. Because you were rear-ended, it does sound like the other driver was at fault. However, your deductible applies anytime your collision coverage pays for repairs, even if you were not at fault. If Pacific Shore (the other driver’s insurer) accepts liability, they may reimburse your deductible after their investigation. Your adjuster,
CUSTOMEROkay, I understand.
CUSTOMERAnd about the premium, will it increase because of.
AGENTI can’t say for certain whether your premium will increase, as it depends on the outcome of the claim investigation and Northgate Mutual’s rating guidelines. Your adjuster, Priya Shah, will review your claim and let you know if there will be any
CUSTOMERIt. I'll wait to hear from Priya Shah. Thank you for your help and goodbye.
AGENTYou're welcome, Mr. Ramirez. Your claim number is NGM-904417, policy number NG4471902, and the other driver's license plate is 7KXR214X. If you need anything else, please call us. Take care and goodbye.