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
#87of 654 stacks
55 / 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.
#51 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.
#292 of 661 on the board · best 0.0%
Agent self-cutoffs
Times the agent cut its own sentence off mid-word.
#1 of 661 on the board · best 0.0 per call
Understanding & effort
Caller effort
How hard the caller had to work: repeating themselves, correcting, re-explaining. 1 = effortless.
Not scored by this sweep.
Repeated-info loops
Times per call the agent re-asked for something the caller had already given.
#180 of 493 on the board · best 0.0 per call
Speech-to-Text
Semantic word error rate
The caller's words the agent's transcriber got wrong in a way that changes the meaning, as a judge reads each difference.
#476 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.
#398 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.
#507 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.
#554 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.
#123 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.
#566 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.
#343 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.
#326 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 | 97% | 3.67 s | — |
| Home services | 90% | 3.51 s | — |
| Insurance | 100% | 3.64 s | — |
| Customer support | 100% | 3.58 s | — |
By caller
| Caller | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| US English, quiet line | 98% | 3.40 s | — |
| Spanish-accented English, city street | 98% | 3.40 s | — |
| Indian English, busy café | 96% | 3.42 s | — |
| Southern US, flustered, crying baby | 98% | 3.69 s | — |
| Fast and interrupting, construction outside | 94% | 3.84 s | 100% |
The calls behind the numbers
Customer support
AGENTBrightwave Internet support, this is Alex. What’s
AGENTgoing on with your service?
CUSTOMERHi, Alex. My internet's been down for about two hours now. I work from home, so it's kind of urgent. There's a road crew digging up the street outside, and 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 help me out?
AGENTSorry about that, I know how disruptive it is when you work from home. I'll look into it now. Can you give me your account number or the phone number on the account so
AGENTI can pull it up?
CUSTOMERYeah, sure. My account number is be 883017. Sorry. Last digit is one. My phone number is (618) 582-8130.
AGENTThe caller corrected the last digit: account number is BE883011? Let me re-read: "be 883017. Sorry. Last digit is one." So the corrected account number is BE883011.
AGENTFound it.
CUSTOMERThat's right. The last. Okay, so about the outage. Do you see anything on your end about the road work outside causing it?
CUSTOMERHello?
AGENTSorry for the pause, I'm here. The remote reset worked, and your gateway now shows as registered and online on our network. Devices can take about two minutes to reconnect, so give it a moment. The internet light should go from blinking orange to solid. Can you check whether you're back online?
CUSTOMERThe light just went solid white. How long until my devices reconnect? And can I get a credit for the downtime? I lost two hours of work.
AGENTSolid white is what we want to see. Devices should reconnect within about two minutes of the reset. If any don't, restart that device's Wi-Fi or reboot it. I'll apply the credit for the downtime now.
CUSTOMEROkay. What amount are you crediting? I lost two hours of work. Can you make it more than that?
AGENTI've applied a $10.00 credit to account BE883011, and it will appear on your next bill. I understand two hours of lost work is worth more to you than that. I can only apply the standard downtime credit, though, and I don't have a way to raise the amount or add a second one. Once your devices reconnect, let me know if everything
CUSTOMEROkay, I'll keep an eye on it. Thanks for your help, Alex. Goodbye.
Healthcare
AGENTRiverbend Health Clinic, this is Alex. How can I
AGENThelp you today?
CUSTOMERHey, I need to reschedule my dermatology appointment next Wednesday morning. I've gotta work shift then, so I can only come after 3 p.m.. Can you help me move it?
AGENTHappy to help with that. First I need to verify you. Can I have your full name and date of
AGENTbirth?
CUSTOMERYeah. It's me. Brooks. March 17th, 1948. Sorry. No, 1984. March 17th, 1984.
AGENTThanks. I only caught your last name, "Brooks." Can you give me your
CUSTOMERRight.
CUSTOMERYeah. Nate Brooks, that's Nate.
CUSTOMERBrooks.
AGENTThanks,
AGENTNate.
CUSTOMERRight.
CUSTOMERSo I just need something. After 3 p.m.. What do you have?
AGENTYour dermatology appointment is with Dr. Okafor next Wednesday at 9:15 AM. Let me check what's open.
CUSTOMERYeah.
CUSTOMERYep.
CUSTOMERGot it. I can't do mornings. What's open after three?
AGENTDr. Okafor has no openings on Wednesday after 3 PM. These are the slots after 3 PM on other days: - Tuesday at 3:45 PM - Thursday at 4:10 PM Would you like either of those, or should I look at something else?
CUSTOMERI can't do Tuesdays. I pick up my daughter. What about Friday? Any thoughts then?
AGENTDr. Okafor's only Friday opening is at 11:30 AM, which is before 3 PM, so it doesn't fit your after-3
CUSTOMERRight.
AGENTThat
CUSTOMERYeah. I can't do mornings. Let's take Thursday. At 4:10 p.m.. That works.
AGENTThursday at 4:10 PM works with your after-3 PM requirement. To confirm, I'll move your dermatology appointment with Dr. Okafor from next Wednesday at 9:15 AM to Thursday at 4:10 PM. Shall I go ahead?
CUSTOMERYeah. Go ahead and book it. Just need to get. This done fast. Yes, please.
AGENTYou're all set, Nate. Your dermatology appointment with Dr. Okafor is now Thursday at 4:10 PM at Main Campus, Building
CUSTOMEROkay. All right. Got it. Is there a fee for changing. The appointment?
AGENTYour confirmation code is RVB-3M8P2, for Thursday at 4:10 PM with Dr. Okafor at Main Campus,
CUSTOMEROkay. Where should I park?
AGENTParking is free for patients in Lot C, with the entrance on River Road. To recap, your appointment is Thursday at 4:10 PM with Dr. Okafor at Main Campus, Building B. Your confirmation
CUSTOMERDoes Doctor Okafor take Aetna insurance. Yeah but.
CUSTOMERDoes Doctor Okafor accept Aetna insurance. Just need to know before I go.
AGENTI don't have access to insurance or provider network information, so I can't
CUSTOMERGot it.
CUSTOMERAll right. Thanks for your help. Goodbye.
AGENTSorry I couldn't answer the Aetna question, Nate. Please call the number on the back of your insurance card or check with the clinic's billing office before your visit. Your appointment is Thursday at 4:10 PM with Dr. Okafor, confirmation code RVB-3M8P2. Arrive 15 minutes early with your photo ID and insurance card. Goodbye!
Home services
AGENTCopperline Home Services, this is Alex. What can I
AGENThelp you with?
CUSTOMERYes. Hello. I am calling from a cafe. There is a pipe under my kitchen sink that has burst. Water is spreading across the floor, heading toward a power strip. I have not shut the water off yet. Can you send someone out to fix it?
AGENTSafety first, before anything else: 1. Stay away from that power strip and anything plugged into it. Don't step in the water near it. If you can reach the breaker panel without walking through water, switch off the kitchen circuits. If you can't do that safely, leave it alone. 2. Shut off
CUSTOMERAll right.
CUSTOMEROkay, I'll shut off the water now. The water has stopped. What should I do next?
AGENTGood, that's the most important step. Stay clear of the wet floor near the power strip. If you can safely unplug it from a dry spot, do so, otherwise leave it alone. Let me pull up your account and check availability. What's the phone number on your account,
AGENTor the service address?
CUSTOMERThe service address is 1480 Larkspur Avenue. The phone number is 503550139. No. 0193.
AGENTLet me look that up. The
AGENTphone number sounds like it might be 503-550-0193 or similar, so I'll search by the address as well.
CUSTOMERYes. That's right. (503) 555-0193.
AGENTThanks. Looking you
AGENTup now.
CUSTOMEROkay. Go ahead. I'm waiting for the details.
AGENTI found your account. You're a Copperline Care member at 1480 Larkspur Avenue, Unit 4B. The earliest emergency visit is today, 2:00 PM to 4:00 PM. As a Care member, the emergency callout fee is $99 (it would be $149 for non-members). Do you agree to that fee and that window? Before I book, I need a few details:
CUSTOMERActually, I moved from unit four B to unit 4D last month. The address is 1480 Larkspur Avenue. Unit 4D. Can anyone come before 2 p.m.? And what will the total cost be for this emergency visit?
AGENTThanks, I'll use Unit 4D instead of 4B. On timing, 2:00 PM to 4:00 PM today is the earliest emergency window the system shows. I can't promise anything sooner. On cost, the $99 emergency callout fee is the only price I have. I don't have a total for the repair itself. Parts and labor depend on what the technician finds, so I can't quote that. Do you agree to the $99 callout fee and the 2:00 to 4:00 PM window? I also need two things for the ticket: 1. Is there a gate code or any access instructions?
CUSTOMERYes.
AGENTThanks, that covers the fee and the window. I still need the access details. 1. Is there a gate code or any special access instructions for 1480 Larkspur Avenue, Unit 4D? 2. Is (503) 555-0193 the best number to reach you? Since you're calling from a cafe, there may be a better one. Once I
CUSTOMERYes. The building gate code is found two, 719 and (503) 555-0193 is the best callback number.
AGENTThe gate code could be read two ways, "found two, 719" might be 4272719 or something else, so I'll confirm it before saving it. Can you give me the gate code digit by digit? I'd like to be sure the technician gets in on the first try.
CUSTOMERCertainly the gate. Code is pound 2719. That's hash 2719.
AGENTGot it, the gate code is #2719. Creating the ticket now.
CUSTOMEROkay. Who will. Be coming and how will I know they are on the way? Also, could you give me the ticket number?
AGENTYou're booked. Dana
CUSTOMEROh, and how. Will I know when Dana is on the way? Could you share the ticket number, please?
AGENTDana is your technician. Dana will text (503) 555-0193 about 30 minutes before arriving, so you'll know when they're on the way. Your ticket number is CPL-58210. Here is everything we have on the booking: - Address: 1480 Larkspur Avenue, Unit 4D - Gate code: #2719 - Callback number: (503) 555-0193 - Visit: Today, 2:00 PM to 4:00 PM - Emergency callout fee: $99 (Copperline Care member rate) Please keep the water main off and stay clear of the wet floor and the power strip until Dana arrives. If you see sparks, smell burning, or feel unsafe, leave the unit and call 911. Is there anything else I can help with?
CUSTOMERNo, that covers everything. Thank you for your help. Goodbye.
AGENTYou're welcome. Stay safe, and Dana will be there this afternoon.
AGENTGoodbye.
Insurance
AGENTNorthgate Mutual claims, this is Alex. Are you somewhere safe right now?
CUSTOMERI'm safe getting next to my car. But can we move this along? You really need help fast.
AGENTI'm glad you're safe, and I'll move through this as quickly as I can. First, is anyone hurt, including you, any passengers, or anyone in the other vehicle? If anyone is injured or in danger, please hang up and call 911 right away. If everyone is okay, I need your
AGENTpolicy number and last name so I can
CUSTOMEREveryone's fine. No injuries. My number is (907) 308-4365. Last name Brooks.
AGENTThank you, Mr. or Ms. Brooks. I'm glad no one is hurt. The number you gave me looks like a phone number, so I'll save it as your callback number. I still need your policy number to verify your policy. It's usually printed on your insurance card or in the Northgate Mutual app.
AGENTCould you read it out to me?
CUSTOMERYeah, I have it. It's 4471903. No, sorry. Last one is 24471902.
AGENTThanks for the correction. I'll use 24471902
CUSTOMERNo, that's not right. It's 4471902. Can we keep moving?
AGENTSorry about the mix-up. I'll use 4471902 instead.
CUSTOMERYep. That's right. What happened next? I need to get my car towed.
AGENTThanks, Mr. or Ms. Brooks. Your policy 4471902 is verified for the 2021 Honda CR-V, and your coverage includes roadside towing. I'll set up the tow now. Where is the car right now? I
CUSTOMERYep. Got it. I need a tow truck now. The car can't be driven. The rear wheel is rubbing, bumpers crushed. Where's it going to be taken?
AGENTI still need the car's exact location to send the truck, so please give me the street address, intersection, or nearest landmark.
CUSTOMERGot it.
CUSTOMERYeah.
CUSTOMERIt's fifth Avenue and Alder Street. I need to know where the car will go after it's towed.
AGENTI'll dispatch the tow now using the phone number you gave earlier as the callback number.
AGENTYour towing
CUSTOMERRight.
CUSTOMEROkay. I just need to know where it's going to be taken. And I have a couple questions. How much am I going to have to pay? Will I get a rental car and is my premium going to go up?
AGENTThe tow is dispatched. Eastside Towing is on the way to Fifth Avenue and Alder Street, with an estimated arrival of 45 minutes. They will call you at (907) 308-4365.
CUSTOMEROkay, that's funny. About the claim. How much do I pay and do I get a rental car also? Will my premium go up?
AGENTHere is what your policy shows: Where the car is going: Eastside Towing will take it to the Northgate partner repair shop at 9th and Morrison. Your towing is covered up to 25
CUSTOMERYup. That's clear. I just need to know if I get a rental, how much I pay, and if my premiums going up.
AGENTHere is what your policy shows: What you pay: Your collision deductible is $500. I can't tell you the final repair cost or exactly what you'll owe until an adjuster has assessed the damage. Rental: Yes, your policy includes rental reimbursement of $40 per day for up to 30 days, starting from the day the claim is opened. If a rental costs more
CUSTOMERYeah.
CUSTOMERAll right. So I get a rental. But he hit me, so I should not have to pay that deductible, right?
AGENTI can't promise that either way. Who is at fault is decided during the claim investigation, and I can't make that call on this phone call. If the other driver is found responsible, the deductible may be recoverable from their insurer, but the adjuster will be the one to confirm that. For now, your policy shows a $500 collision deductible. On your premium, I don't have any information on whether it will change, and I won't guess. Your adjuster or your agent can
CUSTOMERRight.
CUSTOMERAbout 20 minutes ago. Can we. Keep this moving?
AGENTYes, let's finish this so the claim is open and your rental clock starts. I have the location as Fifth Avenue and Alder Street, and the accident was about 20 minutes ago.
CUSTOMERRight. That's right. The other driver. Is Kevin Lund, insured with Pacific Shore plate is 7214 x, like X-ray.
AGENTGot it. The other driver is Kevin Lund, insured with Pacific Shore, and the plate is 7214X, as in X-ray. Did police come to the scene?
CUSTOMERYeah, exactly.
CUSTOMERDo I get a claim number now?
AGENTAlmost there. I just need to know one thing first: did police come to the scene? If they did, do you have a report number?
CUSTOMERGood.
CUSTOMERNo. Police did not come. There's no report.
AGENTI have everything I need, so I'm opening the claim now.
CUSTOMERAll right.
CUSTOMERI need the claim number, please.
AGENTYour claim number is NGM-904417. That's N, G, M, dash, 9,
CUSTOMERGot it.
CUSTOMEROkay. Can you repeat. The full claim number for me? I need to. Make sure I. Have it right.
AGENTYour claim number is NGM-904417. That's N, G, M, dash, nine, zero, four, four, one, seven. Here is everything I have on file: - Policy number: 4471902 - Other driver's plate: 7214X, as in X-ray (Kevin Lund,
CUSTOMERYeah.
CUSTOMEROkay. That's clear. Thanks for your help. Goodbye.
AGENTYou're welcome.
AGENTHere is a summary: - Claim number: NGM-904417 - Policy number: 4471902 - Other driver's plate: 7214X, as in X-ray (Kevin Lund, Pacific Shore) - Adjuster:
AGENTPriya Shah, who will contact you within one business day - Tow: Eastside Towing is on the way, about 45 minutes out. They'll call (907) 308-4365 and take the car to the Northgate partner repair shop at 9th and Morrison. - Rental: $40 per day for up to 30 days, starting from today, the day the claim opened. - Deductible: $500 collision. Whether you end up paying it depends on the fault decision, which your adjuster will handle. Stay with the car and stay safe while you wait for the tow. If anything changes, you can call back with your claim number.