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
#67of 654 stacks
56 / 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.
#169 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.
#594 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.
#1 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.
#35 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.
#59 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.
#448 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.
#436 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.
#46 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.
#356 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.
#541 of 661 on the board · best 0.45 s
Latency spikes
Turns where the response took far longer than the rest of the conversation.
#535 of 661 on the board · best 0.0 per call
By industry
| Industry | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| Healthcare | 89% | 3.70 s | — |
| Home services | 91% | 3.46 s | — |
| Insurance | 100% | 3.81 s | 100% |
| Customer support | 96% | 3.54 s | — |
By caller
| Caller | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| US English, quiet line | 94% | 3.37 s | — |
| Spanish-accented English, city street | — | — | — |
| Indian English, busy café | 96% | 3.73 s | — |
| Southern US, flustered, crying baby | 96% | 3.64 s | — |
| Fast and interrupting, construction outside | 87% | 3.74 s | — |
The calls behind the numbers
Customer support
AGENTBrightwave Internet support, this is Alex. What’s going on with your service?
CUSTOMERHi Alex, I am calling from a cafe. My internet at home has been down for about 2 hours now. There is one road crew digging up the street outside, so I'm wondering I'm wondering if that might be the cause. I already rebooted the gateway twice and checked all the cables. The internet light is blinking orange. Can you check what is happening? My account number is B EW883017. Sorry, last digit is 1.
AGENTI've got you down as account number BEW883017. Let me look that up and check for any outages in your area.
CUSTOMERThat's not quite right. My account number is BW883011. Kindly check with that, please.
AGENTGot it—account number BW883011. Let me check that for you.
CUSTOMERIt is B—
CUSTOMERUh-huh. Go ahead, what did you find?
AGENTGood news! The signal to your gateway is strong, and I've sent a remote reset. Your gateway is now registered and online. Give your devices about two minutes to reconnect—you should be back up shortly.
CUSTOMEROkay, the internet light just went solid white. How long until all my devices reconnect? And since I lost 2 hours of work, can you apply a credit for the downtime?
AGENTPerfect—solid white means you're good to go. Your devices should reconnect within the next couple of minutes as they pick up the signal. Let me apply a service credit for those 2 hours of downtime.
AGENTDone. I've applied a $10.00 credit to account BW883011 for the 2 hours of downtime. It'll show up on your next bill. You should be all set now!
CUSTOMERI lost 2 hours of work. Can you make it more than that?
AGENTI understand the frustration, but the $10.00 credit is what the system applied based on the 2-hour outage. That's the standard credit for downtime like this. I don't have the ability to adjust it beyond what was issued. If you'd like to discuss further compensation, you'd need to reach out to our billing department—they may be able to review your case individually.
CUSTOMERAll right, got it. Thank you for your help. Goodbye.
AGENTYou're welcome! Glad we got you back online. Take care!
Healthcare
AGENTRiverbend Health Clinic, this is Alex. How can I help you today?
CUSTOMERYes, hello. I am having one dermatology appointment next Wednesday morning, but now I have a work shift. I need to move the appointment to a later time, after 3 PM if possible. My name is Arjun Mehta.
AGENTI'd be happy to help you reschedule your dermatology appointment to after 3 PM. To pull up your account, I need your date of birth. Could you please provide that in the format MM/DD/YYYY or YYYY-MM-DD?
CUSTOMERMarch 17th, 1948. Sorry, no, 1984. March 17th, 1984.
AGENTNo problem. Let me verify your account with March 17th, 1984.
CUSTOMEROkay, go. Right, so can you tell me what times are available after 3 PM next week?
AGENTI'll check Dr. Okafor's availability after 3 PM starting next Wednesday.
AGENTBased on your preference for after 3 PM, here are the available slots with Dr. Okafor:
- Tuesday at 345 PM (15:45)
- Thursday at 410 PM (16:10)
Would either of these work for you?
CUSTOMERTuesday does not work after all because I pick up my daughter that day. What about Friday? If that is not possible, I can take Thursday at 4:10 PM.
AGENTUnfortunately, Friday's available slot with Dr. Okafor is at 11:30 AM, which is before 3 PM and wouldn't work for you. So your best option is Thursday at 4:10 PM. Would you like me to reschedule your appointment to Thursday at 4:10 PM?
CUSTOMERYes, kindly reschedule my appointment to Thursday at 4:10 PM. That works for me. Before we finish, can you tell me if there is a fee for changing the appointment?
AGENTGreat news!
CUSTOMERAh.
CUSTOMEROkay, where should I park when I come for the appointment?
AGENTYou can use the free patient parking in Lot C, with the entrance on River Road. To confirm your rescheduled appointment:
- NameArjun Mehta
- Date of BirthMarch 17th, 1984
- New AppointmentThursday at 4:10 PM with Dr. Okafor, Dermatology
- LocationMain Campus, Building B
- Confirmation CodeRVB-3M8P2
- ParkingLot C, entrance on River Road
- Please arrive 15 minutes early with your photo ID and insurance card
Regarding the change feesince your original appointment was next Wednesday morning and you're rescheduling more than 24 hours in advance, there is no fee for this change. You're all set!
CUSTOMERThat's right, I am all set. One last thing, can you confirm if Dr. Okafor accepts Aetna insurance?
AGENTI don't have access to information about which insurance plans Dr. Okafor accepts. I'd recommend calling the clinic directly during business hours—we're open Monday through Friday, 8:00 AM to 6:00 PM, and Saturday 9:00 AM to 1:00 PM. They'll be able to confirm your Aetna coverage right away. Is there anything else I can help you with?
CUSTOMERNo, that's everything. Thank you for your help. Goodbye.
AGENTYou're welcome, Arjun! Thank you for calling Riverbend Health Clinic. Have a great day!
Home services
AGENTCopperline Home Services, this is Alex. What can I help you with?
CUSTOMERHey, I'm sorry, I'm a little flustered right now. There's water all over my kitchen floor. The pipe under the sink just burst and it's spreading toward a power strip. I really need someone to come out as soon as possible. Can you help?
AGENTI can absolutely help you. First, let's make sure everyone is safe—this is important. Right now, please do this:
1. Turn off the water main to your house immediately. Do you know where it is? It's usually in the basement, garage, or outside near the foundation. 2. Unplug that power strip or anything electrical near the water right away. 3. Keep people and pets away from the wet area until we get a tech there. Once you've done those things, let me get your information and get you scheduled for an emergency visit. Are you able to locate and shut off the water main?
CUSTOMERHold on. Okay, I think I found it outside near the foundation. I'm turning off the water now. All right, it's off. The water stopped. Thank goodness. Is that enough? Sorry, I'm just a little anxious. My baby's crying and I'm worried about the power strip too. Should I unplug it even if the floor is wet?
AGENTYou did exactly right—shutting off the water was the critical step, and the water stopping is a huge relief. Yes, absolutely unplug that power strip right now, even if the floor is wet. Just don't touch it with wet hands; use a dry cloth or have someone else unplug it if you can. Safety first. Take a breath—you're handling this well. Your baby is safe, and you've stopped the water. That's the hardest part. Now let me get you set up with an emergency technician. I need a few details:
What's your phone number? I can look up your account, or if you'd prefer to give me your address, I can find you that way too.
CUSTOMERI hope this is the right one. My phone number is 503-555-0139.
CUSTOMERNo, 0193. Is that what you need, or do you want my address too?
AGENTGot it—so 503-555-0193. Let me look that up for you.
CUSTOMERYeah, that's
CUSTOMEROkay.
CUSTOMERSo, um, just to double-check, does it show unit 4D? I moved from 4B to 4D last month, and I want to make sure you have the right one. Sorry, I just want to be sure nothing gets mixed up.
AGENTGood catch—thank you for letting me know. I have 4B on file, but you're saying you moved to 4D last month. I'll use 4D for your service request. The address is still 1480 Larkspur Avenue, unit 4D. Is that correct?
CUSTOMERYes, sorry, that's right. 1480 Larkspur Avenue, unit 4D. Is that okay?
AGENTPerfect, that's exactly what I need. Now let me check emergency availability and get you booked.
AGENTGreat news—I have an emergency window available today from 2:00 PM to 4:00 PM. Since you're a Copperline Care member, the emergency callout fee is $99. Does that work for you, and are you okay with that fee?
CUSTOMERIs there any way someone could come before 2? I've got a baby and the kitchen's a mess, and I'm a little worried it's gonna get worse. If not, I guess I'll take that window, but I just want to make sure— is $99 all I'll need to pay today?
AGENTI understand—with a baby and water damage, I know this is stressful. Unfortunately, 2:00 PM to 4:00 PM is the earliest emergency window I have available today. I wish I could get someone there sooner. As for the $99—that's the emergency callout fee. That covers the technician coming out and diagnosing the problem. If parts or additional work are needed beyond that initial visit, there would be additional charges, but the $99 is what you'll owe for today's emergency call. Does that work for you?
CUSTOMERI guess that'll have to do. Yes, that's fine. I'll take the 2 to 4 PM window. Sorry, I'm just nervous with everything going on. Who will be coming out, and how will I know they're on their way?
AGENTDon't apologize—you're doing great. Let me open your service request now and get you booked.
AGENTI need to create the service request first. Before I do, do you have a gate code or any access notes I should include? And what's the best callback number to reach you on?
CUSTOMERYeah, sorry, the gate code is #2719, and the best callback number is 503-555-0139.
CUSTOMERNo, 0193. Will that work?
AGENTPerfect. 503-555-0193 is what I have. Let me create your service request now.
AGENTExcellent. Now let me book your visit.
AGENTPerfect! You're all set. Here's what you need to know:
Your ticket number is CPL-58210. Your technician is Dana, and she'll be arriving sometime between 200 PM and 4:00 PM today. She'll text you at 503-555-0193 about 30 minutes before she arrives, so you'll have a heads up. To confirm:
- Service address1480 Larkspur Avenue, unit 4D
- Gate code#2719
- Callback number503-555-0193
- Emergency fee$99 (Copperline Care member rate)
Keep that water main shut off until Dana gets there. You did the right thing. Hang tight—Dana will take care of you this afternoon.
CUSTOMERThank you so much for your help. I really I'll keep the water main off and wait for Dana's text.
CUSTOMERGoodbye.
AGENTYou're very welcome. You handled this perfectly—stay safe, and Dana will be there soon. Goodbye!
Insurance
AGENTNorthgate Mutual claims, this is Alex. Are you somewhere safe right now?
CUSTOMEROoh, yes, I am safe. I'm standing next to my car, but it's on a busy street.
AGENTGood, I'm glad you're safe. Before we go any further, I need to ask: is anyone injured? You, the other driver, any passengers, or pedestrians?
CUSTOMERNo, nobody is injured. It's just the cars, not the people.
AGENTThat's a relief. Okay, let's get this sorted out.
CUSTOMERSure, my policy number is NG4471903. No, sorry, last one is 2.
AGENTThank you for the correction. Let me verify your policy.
AGENTPerfect, I've verified your policy NG4471902. Your vehicle is a 2021 Honda CR-V.
Now, can you tell me what happened? What time did the accident occur, and where exactly are you right now?
CUSTOMERYeah, so, uh, about 20 minutes ago I was stopped at a red light at SE 5th Avenue and Alder Street. Another car hit me from behind. I'm still here standing next to my car.
CUSTOMERYes, that's right. The back bumper is crushed and the rear wheel is rubbing, so I can't drive the car.
AGENTOkay, so you were hit from behind at SE 5th Avenue and Alder Street about 20 minutes ago. The back bumper is crushed and the rear wheel is rubbing, so the car isn't safe to drive. Did the police come to the scene?
AGENTDo you have a police report number?
CUSTOMERNo, the police did not come, and there is no police report number.
AGENTGot it.
CUSTOMERRight, so, um, what happens next? Do you need information about the other driver?
AGENTYes, I do need that information.
CUSTOMERYes, the other driver is Kevin Lund. He is insured with Pacific Shore and his plate is 7KX, like X-ray, R214.
AGENTThank you. So the other driver is Kevin Lund, insured with Pacific Shore, and his license plate is 7KX R214. Let me open a claim for you now.
CUSTOMEROkay.
CUSTOMERYeah.
AGENTI need a callback number so the tow truck driver can reach you. What's the best number to call you on?
CUSTOMERYou can reach me at my number. It's 1-800-289-8004.
AGENTPerfect, thank you. Let me open your claim now.
CUSTOMEROkay, and, uh, can you tell me how much I have to pay for this? Will I have a deductible?
AGENTLet me check your coverage details for you.
AGENTYes, you do have a collision deductible of $500. That's what you'll pay out of pocket toward the repair costs. You also have rental coverage of $40 per day for up to 30 days, starting from today when the claim was opened. And your towing is covered up to 25 miles—your car is being towed to our partner repair shop at 9th and Morrison, which should be within that distance. Here's a summary of everything:
Policy numberNG4471902
Other driver's license plate7KX R214
Claim numberNGM-904417
Your adjuster is Priya Shah, and she'll contact you within one business day. A tow truck from Eastside Towing is on the way—they should be there in about 45 minutes.
CUSTOMEROh.
CUSTOMERAll right, and, uh, about the deductible, he hit me, so I should not have to pay that, right? Also, is my premium going to go up because of this?
AGENTThose are good questions, but they're things I can't answer right now. Your adjuster Priya Shah will go over all of that with you when she calls within one business day. She'll talk to the other driver's insurance company and work through who's at fault, and she can explain how that affects your deductible and your premium going forward. Is there anything else I can help you with right now?
CUSTOMERNo, that's everything. I have the claim number. The tow truck is coming, and I will wait for Priya to call. Thank you for your help. Goodbye.
AGENTYou're welcome. Stay safe, and we'll take care of you. Goodbye.