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
68 / 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.
#89 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.
#219 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.
#153 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.
#220 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.
#153 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.
#48 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.
#411 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.
#211 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 | 91% | 4.31 s | — |
| Home services | 98% | 4.48 s | — |
| Insurance | 94% | 3.96 s | — |
| Customer support | 100% | 4.03 s | — |
By caller
| Caller | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| US English, quiet line | 98% | 3.92 s | — |
| Spanish-accented English, city street | 100% | 3.80 s | — |
| Indian English, busy café | 93% | 4.34 s | — |
| Southern US, flustered, crying baby | 94% | 4.27 s | — |
| Fast and interrupting, construction outside | 94% | 4.10 s | — |
The calls behind the numbers
Customer support
AGENTBrightwave Internet support, this is Alex. What’s going on with your service?
CUSTOMERHey, uh, yeah, sorry, I'm a little flustered. My baby's crying here and I've been working from home, but my internet's been down for about 2 hours now. There's a road crew digging up the street outside. So I'm worried that might be why. The gateway's internet light is blinking orange. I already rebooted it twice and checked all the cables, but nothing's working. Can you check if the road crew is the reason? You really need this fixed. I'm falling behind at work. My account number is BW883017— uh, sorry, last digit is 1. Is that enough?
AGENTYes—your corrected account number is BW883011. I’ll check for an outage first. You won’t need to repeat the reboots or cable checks.
CUSTOMEROh, thank you. I appreciate that. I was worried you'd ask me to do all that again. So, um, can you see if there's an outage because of the roadwork? Are you sure my account number's right? I just want to make sure nothing gets lost.
AGENTYes, I found your account using BW883011—the corrected last digit is 1. There’s no known outage or maintenance reported in your area, but that doesn’t rule out damage from the roadwork. I’ll test your line now.
AGENTThe signal
CUSTOMERUm, is the signal coming through? Should I do anything on my end or just wait? I'm a little anxious. Please let me know if you see anything wrong.
AGENTYes—the signal to your gateway is good. It wasn’t registered on the network, but after the remote reset it’s now registered and online. Just wait about two minutes for your devices to reconnect—no more rebooting or cable checks needed. Then try
CUSTOMERAll right, uh, thanks for letting me know. The light just went solid white. So how long until everything's back up? Is it really just 2 minutes? I'm sorry, I just I just want to make sure it's all okay. And since I lost 2 hours of work, can you give me a credit for the downtime? Oh, thank goodness! I just saw the internet light go solid white. So, uh, Is it really going to reconnect in about 2 minutes? I'm sorry, I just want to be sure. Should I do anything else while I'm waiting? And, uh, can I get a credit for the downtime? I lost 2 hours of work and it's been stressful.
AGENTThat’s encouraging—the gateway is confirmed online. Devices can take about two minutes after the reset to reconnect; it’s an estimate, not an exact deadline. No more rebooting is needed. Try opening a webpage now, and let me know if it still won’t load after two minutes. I’ve also applied a $10.00 downtime credit to account BW883011. It will appear on your next bill.
CUSTOMERAll right, uh, thank you for that, but honestly, I lost 2 hours of work. Is there any way you can make it more than $10? I'm just worried it won't really cover what I missed.
AGENTI understand—$10 doesn’t make up for two hours of lost work. The $10.00 credit is confirmed on your next bill, but I don’t have a way here to increase it or approve compensation for lost work. A billing representative would need to review a request for more.
CUSTOMEROkay, I get it. So I'd need to talk to billing if I want to ask for more, is that right? Sorry, just want to be sure. Can you confirm the $10 credit will show up for me? And, um, is there a way for billing to call me back, or should I reach out myself?
AGENTYes—you’d need to contact billing to request a larger credit; approval isn’t guaranteed. I can confirm that $10.00 has been credited to account BW883011 and will appear on your next bill. I can’t arrange a billing callback here, so please reach out to billing directly.
CUSTOMERThank you for confirming all that. I appreciate your help, and I'll get in touch with billing about the extra credit. Goodbye.
AGENTYou’re welcome.
AGENTI hope the rest of your workday goes smoothly. Goodbye!
Healthcare
AGENTRiverbend Health Clinic, this is Alex. How can I help you today?
CUSTOMERHey, I need to reschedule my dermatology appointment next Wednesday morning. I just got assigned a work shift then and can't make it. Can you move it to a time after 3 PM?
AGENTI can help with that. What’s your full name and date of birth so I can verify your appointment?
CUSTOMERMate Brooks, March 17th, 1948. Sorry, no, 1984. March 17th, 1984.
AGENTThanks—Mate Brooks, March 17, 1984. I’ll use the corrected year.
CUSTOMERThat's correct. Go ahead and check for a slot after 3 PM, please.
AGENTDr. Okafor has openings Tuesday at 3:45 PM and Thursday at 4:10 PM. Which would you prefer?
CUSTOMERTuesdays actually don't work. I have to pick up my daughter. What about Friday?
AGENTFriday’s only available slot with Dr. Okafor is 11:30 AM, so it doesn’t fit your after-3-PM requirement. Would Thursday at 4:10 PM
CUSTOMERThursday at 4:10 PM works. Let's book that.
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.
CUSTOMERIs there a fee for changing the appointment?
AGENTChanges made more than 24 hours before the original appointment are free. Changes within 24 hours carry a $40 fee. Whether a fee applies to your change depends on how far away your original Wednesday appointment was when we moved it.
CUSTOMERYep, got it. Where should I park for the appointment?
AGENTFree patient parking is available in Lot C, with the entrance on River Road. Your appointment
CUSTOMERRight, last thing. Does Dr. Okafor accept Aetna insurance?
AGENTI don’t have access to Dr. Okafor’s insurance participation details, so I can’t confirm whether Aetna is accepted. Please check with the clinic’s billing team or Aetna to confirm that Dr. Okafor is in-network for your specific plan.
CUSTOMERAll right, I'll check with billing. Thanks for your help. Goodbye.
AGENTYou’re welcome,
AGENTMate. Take care, and goodbye!
Home services
AGENTCopperline Home Services, this is Alex. What can I help you with?
CUSTOMERHi, I need an emergency plumber. A pipe under my kitchen sink just burst and water is spreading across the floor toward a power strip. I haven't shut the water off yet. Can you help?
AGENTYes. Keep away from the water and power strip—don’t touch or unplug it. Is anyone in contact with the water or being shocked? If so, don’t touch them; call 911 immediately. If you can reach your main water shutoff without stepping into water or going near electrical equipment, turn it off now. If your electrical panel is dry and safely accessible, switch off the affected circuit; otherwise, stay away. Are you able to shut off the water safely, and is everyone clear of the wet area?
CUSTOMERYes, everyone's clear of the wet area. I'm shutting off the water now. It's stopped. What should I do next? Can you send someone out?
AGENTKeep the water shut off, and keep everyone away from the wet floor and power strip. Don’t touch the strip until power to that area is safely off. The earliest emergency arrival window is today, 2:00–4:00 PM. The emergency callout fee is $149, or $99 for Copperline Care members. I’ll check which applies before booking. What’s your service address, including any unit number, and the best phone number to reach you?
CUSTOMERThe address is 1480 Larkspur Avenue, Unit 4D. My phone number is 503-555-0139. No, 0139. Can anyone come before 2? And I'm not sure if I'm a Copperline Care member. Could you check that for me?
AGENTYou’re a Copperline Care member, so your emergency callout fee is $99. The earliest window offered is today, 2:00–4:00 PM; nothing earlier is currently available. Your file lists Unit 4B, but I’ll use Unit 4D, as you said, and callback number 503-555-0139. Do you agree to the $99 fee and that arrival window? Also, is there a gate code or any access instruction the plumber needs?
CUSTOMERYes, I agree to the $99 fee and the 2:00 to 4:00 PM window. The gate code is #2719. Who will be coming, and how will I know they're on the way. Also, can you give me the ticket number for this visit?
CUSTOMERHello?
AGENTI’m here—your visit is booked. Dana will arrive today between 2:00 and 4:00 PM and will text you 30 minutes before arrival. Here are the confirmed details: - Address: 1480 Larkspur Avenue, Unit 4D. - Gate code: #2719. - Callback number: 503-555-0139. - Emergency callout fee: $99. - Ticket number: CPL-58210. Keep the water shut off and stay clear of the wet area and power strip until it’s safe.
CUSTOMERThank you for all the details. I'll keep the water shut off and everyone away from the area. Goodbye.
AGENTYou’re welcome.
AGENTDana will see you today between 2:00 and 4:00 PM. Stay safe, and goodbye.
Insurance
AGENTNorthgate Mutual claims, this is Alex. Are you somewhere safe right now?
CUSTOMERUh, yes, I think so. I'm at home, but I'm a little flustered. Sorry, my baby's crying in the other room and I just got back from the accident. Nobody's hurt, but I'm pretty anxious. Is this going to be all right?
AGENTI’m glad you’re home and nobody’s hurt. It’s understandable to feel anxious after an accident. We’ll take this one step at a time, and you can pause to check on your baby whenever you need. What’s your policy number and the policyholder’s last name?
CUSTOMERI really appreciate that. Um, my policy number is NG4471903. No, sorry, last one is 2, and the last name is Carter. Is that all right?
AGENTYes, thank you for correcting it. Your policy number is NG4471902, under Carter, and I’ve verified it for your 2021 Honda CR-V. When and where did the accident happen, and can you tell me what happened?
CUSTOMERUm, it happened about 20 minutes ago. I was stopped at a red light at Southeast 5th Avenue and Alder Street. Another car hit the back of mine. The bumper's all crushed and the rear wheel is rubbing, so I can't drive it. Nobody got hurt, thankfully. Is that enough detail, or do you need more about what happened?
AGENTThat’s enough about how it happened, thank you. I’ve noted that you were rear-ended while stopped at the light, and nobody was injured. Please don’t drive the car with the rear wheel rubbing. I need a few more details: - What’s the other driver’s name, insurer, and license plate, if you have them? - Did police attend, and is there a report number? - What city did this happen in, and is your car still at the intersection or somewhere else? I can help arrange a tow once I have the car’s location.
CUSTOMERAll right, um, the other driver's name is Kevin Lund. He's insured with Pacific Shore. The plate is 7KX, like X-ray, R21. Police didn't come, so there's no report number. This happened in Portland. My car's at home now. I had it towed here since I couldn't drive it. Is that going to be a problem for arranging a tow?
AGENTNo—you’ve already had it towed home, so we don’t need to arrange another tow right now. If it needs a tow to a repair shop, I can help with that. Any towing coverage would need to be checked separately. I have Kevin Lund, insured with Pacific Shore, and his plate as 7KXR21—is that correct? Before I open the claim, what was the date and approximate local time of the accident?
CUSTOMERPlease tell me that's the right one. It's 7KX, like X-ray, R214. And the accident was today, about 20 minutes ago. It's, uh, probably It's around 1 PM now, so that'd be about 12:40 PM. Is that what you needed?
AGENTYes, that’s what I needed. Thank you for correcting the plate—I’ve recorded 7KXR214, including the final 4. Your claim is now open: - Policy number: NG4471902. - Claim number: NGM-904417. - Assigned adjuster: Priya Shah, who will contact you within one business day. I recorded the accident as today at approximately 12:40 PM in Portland. Since your car is already at home, there’s no need to arrange another tow right now. Please don’t drive it with the wheel rubbing.
CUSTOMERAll right, I won't drive it, promise.
CUSTOMERSorry if I sounded unsure earlier. I just wanted to make sure everything was clear. Is there anything else I should do while I wait for the adjuster? And, uh, could you tell me how much I might have to pay?
AGENTNo need to apologize—you helped make sure the details were accurate. While you wait for Priya Shah: - Keep the car parked and don’t drive it. - Take photos of the damage if it’s safe. - Save your towing receipt and any other accident-related receipts. - Keep the other driver’s details handy, and check with the adjuster before authorizing repairs. I checked your coverage: - Collision deductible: $500. That’s the deductible if the damage is handled under your collision coverage; it isn’t a confirmed final out-of-pocket total. - Rental reimbursement: $40 per day for up to 30 days, starting the day the claim is opened. - Towing: covered up to 25 miles. Keep the receipt for the tow you already had so the adjuster can review it. Your claim number is NGM-904417. Priya can explain how the deductible applies and review any expenses beyond your coverage limits.
CUSTOMEROkay, thank you. Um, bye. me, so I should not have to pay that, right? Is there a way the deductible gets waived since it wasn't my fault? And, uh, will my premium go up because of this? I'm just real worried about what all this could mean for us. Also, do I get a rental car for sure, or does that depend on something else?
AGENTI understand—those costs can be worrying, especially when you were hit while stopped. - Deductible: Your policy shows a $500 collision deductible. Being not at fault doesn’t automatically mean it’s waived. I can’t confirm a waiver or whether you’ll get it back; Priya will need to review that. - Premium: I can’t tell from the coverage information whether your premium will change. Your adjuster or policy service team can help clarify that. - Rental: You have rental reimbursement of $40 per day for up to 30 days, starting the day the claim was opened. That confirms coverage, but not a guaranteed rental reservation or payment of every rental expense. Before booking, ask Priya how to arrange it and which charges are eligible. Keep your receipts, and ask Priya about the deductible and rental arrangements before committing to expenses.
CUSTOMERRight, thank you. That helps a lot. I'll wait for Priya and make sure to check with her before I do anything else. I really appreciate you walking me through all that. I'm just trying to make sure nothing gets missed. And, uh, could you repeat the claim number one more time for me? I want to write it down so I don't lose it.
AGENTOf course. Your claim number is NGM-904417. That’s N-G-M, dash, 9-0-4-4-1-7. Your assigned adjuster is Priya Shah.
CUSTOMERAll right, got it. Thank you. That's NGM-904417. I'll wait for Priya Shah to reach out. I really appreciate your help. Okay, I think that's everything. Goodbye.
AGENTYou’ve got it—NGM-904417. You’re welcome, and take care of yourself and your family. Goodbye.