Roark Live Bench · Suite v1.3 · Sweep of 23 October 2026
Voice AI models, ranked on live phone calls.
Every speech-to-text, language model and text-to-speech model, run in every combination on the same real call to the same agent. A model is ranked on every stack it runs in; the stacks are the evidence.
Our picks
Task completionMedian responseCost per minute- Best overallAssemblyAI Universal-3.6 Pro + OpenAI GPT-6.1 Sol + Cartesia Sonic-3.697%3.9 s$0.055
- Best valueAssemblyAI Universal-3.5 Pro + Google Gemini 3.8 Flash + Cartesia Sonic-3.597%4.3 s$0.048
- FastestOpenAI Whisper-1 + Google Gemini 3.8 Flash + OpenAI TTS-1 HD88%0.5 s$0.023
- Best speech-to-speechgrok-voice-think-fast-2.082%2.1 s$0.083
- Best in customer supportAssemblyAI Universal-3.6 Pro + OpenAI GPT-6.1 Sol + ElevenLabs v4 Turbo100%3.9 s$0.096
- Best in healthcareAssemblyAI Universal-3.5 Pro + Google Gemini 3.8 Flash + Cartesia Sonic-3.696%4.4 s$0.041
- Best in home servicesAssemblyAI Universal-3.6 Pro + OpenAI GPT-6.1 Sol + Cartesia Sonic-3.596%4.0 s$0.058
- Best in insuranceAssemblyAI Universal-3.5 Pro + OpenAI GPT-4o + Cartesia Sonic-3.698%3.2 s$0.042
The read
- 430 of 642 cascades complete the task in at least 90% of calls, and the top 10 sit within 1 point of each other. What separates the top stacks is speed and cost.
- Median response time runs from 0.5 s to 6.0 s on the same agent and the same caller, a spread of 5.5 s that comes entirely from the models behind it.
- No stack wins everything. The fastest, OpenAI Whisper-1 + Google Gemini 3.8 Flash + OpenAI TTS-1 HD at 0.5 s, ranks 530 of 642 on task completion; the most complete is Speechmatics Linden-1 (own VAD) + OpenAI GPT-6.1 Sol + OpenAI TTS-1 HD at 99%.
- The language model matters most: swapping it moves task completion by 7 points, against 3 for the speech engine and 2 for the transcriber.
Cascade vs Speech-to-Speech
The first choice to make. A cascade chains three models: one transcribes, one decides what to say, one speaks. A speech-to-speech model does all three at once. Both run the same agent on the same calls here.
- Cascades lead on task completion, tool use and time to first word; speech-to-speech leads on response time and cost per task. Neither wins outright, so the right choice depends on what your calls need.
- The biggest difference is task completion: 95% for cascades against 77% for speech-to-speech.
| Measure | Cascade | Speech-to-speech |
|---|---|---|
| Task completion | 95% (leads) | 77% |
| Tool use | 72% (leads) | 55% |
| Time to first word | 0.7 s (leads) | 1.3 s |
| Response time | 3.6 s | 2.6 s (leads) |
| Cost per task | $0.15 | $0.15 (leads) |
The three strongest of each, averaged. Cascades: Speechmatics Linden-1 (own VAD) + OpenAI GPT-6.1 Sol + Cartesia Sonic-3.5; AssemblyAI Universal-3.6 Pro + OpenAI GPT-6.1 Sol + Rime Coda; Speechmatics Linden-1 (own VAD) + OpenAI GPT-6.1 Sol + Rime Coda. Speech-to-speech: grok-voice-think-fast-2.0; gemini-3.8-live; amazon.nova-2-sonic-v1:0. Each measure is scored 0 to 100 on its own scale, further out is better; the table gives the same figures in their own units.
Which to use
- Choose speech-to-speech when the pause matters more than the outcome: short transactional calls, callers who interrupt, queues where every half second costs you callers. Expect to pay more, and choose the model carefully (gpt-live-1 (backend gpt-6.1-sol) leads here).
- Choose a cascade when the call has to complete, when cost matters, or when you need to see which stage to fix. It is 13 points ahead on task completion here.
- Costs are what each stack’s providers charged for these calls, at the rates in effect when they ran. Telephony and hosting are not included.
Speech-to-Text
The transcriber turns the caller's speech into text. It decides what the agent thinks it heard, and nothing after it can start until it finishes.
- AssemblyAI Universal-3.6 Pro, AssemblyAI Universal-3.5 Pro, Deepgram Nova-3 and Speechmatics Enhanced (realtime) are level: none leads by more than the margin of error. Of them, AssemblyAI Universal-3.6 Pro costs least, at $0.030 a call. Avoid Speechmatics Linden-1 (own VAD): last on semantic word error rate (2.2%).
- Speechmatics Linden-1 (own VAD) delivers its transcript in 0.49 s, OpenAI Whisper-1 in 1.93 s: 1.44 s added to every turn of the call.
- AssemblyAI Universal-3.6 Pro is the most accurate: it changes the meaning of 0.9% of the caller's words, against 2.2% for Speechmatics Linden-1 (own VAD).
- In 341 of 648 cascades the transcriber is the slowest step of the turn, whichever model runs it.
Open a model to see where it ranks and the stacks it works best in.
Language model
The language model decides what the agent says and does. It has the most influence on whether the task gets done, and usually takes the largest share of each turn.
- Pick OpenAI GPT-6.1 Sol: it leads on task completion (95%), tool use (71%) and instruction following (4.2 / 5). If speed matters more, Anthropic Claude Haiku 4.5 is the quickest, at 0.36 s, and completes 91%. Avoid OpenAI GPT-5.1: last on tool use (33%) and instruction following (4.1 / 5).
- Anthropic Claude Haiku 4.5 starts answering in 0.36 s, Google Gemini 3.8 Flash in 1.47 s: 1.11 s added to every turn of the call.
- OpenAI GPT-6.1 Sol uses its tools most reliably: it handles its tools without a mistake on 71% of calls, against 33% for OpenAI GPT-5.1.
- In 161 of 648 cascades the language model is the slowest step of the turn, whichever model runs it.
Open a model to see where it ranks and the stacks it works best in.
Text-to-Speech
The speech engine is the voice the caller hears, and the last step before they hear anything.
- Cartesia Sonic-3.6, Cartesia Sonic-3.5 and ElevenLabs v4 Turbo are level: none leads by more than the margin of error. Of them, Cartesia Sonic-3.5 costs least, at $0.165 a call. Avoid OpenAI TTS-1 HD: last on readback accuracy (80%), word error rate (8.7%) and time to first audio byte (2.09 s).
- ElevenLabs v4 Turbo starts speaking in 0.11 s, OpenAI TTS-1 HD in 2.09 s: 1.97 s added to every turn of the call.
- Cartesia Sonic-3.6 is the most accurate: it reads codes, amounts and dates back correctly 87% of the time, against 80% for OpenAI TTS-1 HD.
- Cartesia Sonic-3.6 sounds the most human: 3.8 / 5, against 3.4 / 5 for ElevenLabs Flash v2.5.
- In 99 of 648 cascades the speech engine is the slowest step of the turn, most often with OpenAI TTS-1 HD (54 of them).
Open a model to see where it ranks and the stacks it works best in.
Speech-to-Speech models
One model listens, reasons and speaks, so there is nothing to swap and no stages to split. Each is judged on the whole call, with the same calls and the same scoring as every cascade.
- The model matters as much as the architecture: gpt-live-1 (backend gpt-6.1-sol) completes 86% of tasks, ElevenLabs Realtime v1 Mini 42%.
- grok-voice-think-fast-2.0 answers in 2.1 s against 4.1 s for gemini-2.5-flash-native-audio-preview-12-2025.
- Price varies as much as quality: $0.06 per completed task for amazon.nova-2-sonic-v1:0, $0.69 for gpt-realtime-2.
Open a model for its full report, every figure it was scored on and the calls behind them.
Every stack
The evidence behind the model boards: each combination on its own calls, scored on the same recipe. Open a stack for its full report, every figure it was scored on and the calls behind them.
Each score carries its 95% margin of error. A rank is the best place a stack could hold once every margin is allowed for, so stacks the calls cannot tell apart share one and are listed by the low end of their margin; hover a rank for the worst place it could hold. 92 stacks are level with the leader: their calls cannot tell them apart. Among them, 31 Google Gemini 3.8 Flash, 29 OpenAI GPT-6.1 Sol, 19 Anthropic Claude Sonnet 5.5, 9 Anthropic Claude Haiku 4.5, 2 OpenAI GPT-4o mini, 1 OpenAI GPT-4.1 and 1 speech-to-speech; 48 AssemblyAI, 23 Deepgram and 20 Speechmatics; 48 Cartesia, 33 ElevenLabs, 9 Rime and 1 OpenAI. A cascade's figures are 25% its own calls and 75% what every other stack shows about its transcriber, language model and voice.
Not ranked here for missing data: Speechmatics Linden-1 + OpenAI GPT-6.1 Sol + OpenAI TTS-1 HD (no response time or tool use or human-likeness or readback accuracy or voice consistency).
By industry
Each industry ranked on its own calls: the same five callers, its own tasks and tools. A stack has a handful of calls in each, so a cascade is ranked on what its transcriber, language model and voice show across every stack in that industry, and a speech-to-speech model on its own calls there. Pairings show on the main board. Every panel is on one scale, so a bar reads against the other industries as well as its own.
Healthcare
Reschedule an appointment. A patient moves a dermatology appointment around a work schedule, corrects their date of birth, narrows the times twice, and asks about the change fee, parking and insurance.
Home services
Urgent repair: triage and booking. A caller with a burst pipe needs safety advice first, then an emergency visit. The unit on file is out of date, the callback number gets corrected, and the caller pushes for an earlier visit.
Insurance
Report a new auto claim. A driver who was just rear-ended reports a claim, corrects the policy number, spells the other car’s plate, asks about the deductible, a rental and their premium, and needs a tow.
Customer support
Internet outage troubleshooting. A technical caller who has already rebooted the gateway pushes back on scripted steps, wants the line fixed, and asks for a bigger credit for the downtime.
How it works
Every number on this page comes from real phone calls, placed and scored the same way for every stack.
Real calls, four industries, five callers
Roark places real phone calls over the public phone network to a reference agent for each of four industries: rescheduling a clinic appointment, booking an urgent home repair, opening an auto insurance claim and fixing an internet outage. Each agent has its own prompt and tools, identical for every stack. The tasks carry the details real calls trip on: a corrected date of birth, a policy number spelled out, a constraint added halfway through.
Every industry is called by the same five simulated callers: a quiet US English line, a Spanish-accented caller on a city street, an Indian English caller in a busy café, a flustered Southern caller with a baby crying, and a fast caller who interrupts over construction noise. They are equally clear about what they want, so a call fails because of the agent, not the caller. Only the models change, so any difference on the board comes from them.
Each caller rings each industry once for a cascade and four times for a speech-to-speech model. A cascade's figures also draw on its parts, heard in dozens of other stacks; a speech-to-speech model has only its own calls, so it takes more of them.
That makes industries comparable over the same callers, and callers over the same tasks, and the board can be re-ranked by either. One caller within one industry is too few calls to judge, so the board never shows it.
655
stacks
20
calls per stack
13,876
calls scored
Every model, in every combination
9 transcribers, 8 language models and 9 voices are run in every combination with each other, beside 13 speech-to-speech models. A model's own figures pool every stack it runs in, adjusted for its partners in each one, so every model is compared as if it ran with the same partners. A transcriber's figure rests on hundreds of calls with every language model and every voice, not on one lucky pairing.
That measures a model where it actually works: inside a running agent, on live phone audio, through interruptions, corrections and real turn-taking. A test that feeds a model clean audio files or one prompt at a time never sees any of that.
One score, weighted by what matters on a call
Each section ranks on a Roark score. Every figure scores 0 to 100 between two published points: full marks where doing better makes no difference to the caller, zero where the call breaks down. Each then counts at its weight, and the weights follow one order everywhere: whether the caller got what they rang for, then whether the agent got it right, then speed.
A stack, cascade or speech-to-speech, is scored on the outcome and the reply first, then on what each of its components got right on its own calls: the words its transcriber got wrong in a way that changes the meaning, whether its voice read codes and amounts back right, how human it sounded, and whether its tools were handled. A component that leads its section lifts every stack that carries it, by the same recipe for every stack. A figure a stack cannot carry, such as a semantic word error rate for a speech-to-speech model with no transcript, is credited at the field median, so the stack is neither flattered nor punished for it.
On a full sweep each cascade's figure is 25% what its own calls measured and 75% what every other stack shows about its transcriber, language model and voice, fitted across the whole sweep. The split is the same for every stack, so a lucky run does not lead and a stack's score does not move with how noisy its calls were. In an industry a stack has a handful of calls, so a cascade's figure there is what its transcriber, language model and voice show across every stack in that industry's calls, and a speech-to-speech model's is its own calls there. How a particular pairing beats or misses its parts shows on the main board, where each stack has all of its calls. The best overall pick, on the main board and in each industry, is the stack whose score is highest at the low end of its margin of error, so a few lucky calls cannot carry a stack past one whose score rests on hundreds. Stacks that share a rank are listed the same way, so the first row is the pick.
That order comes from real-world voice agent calls, where getting the conversation right predicts how a call ends more strongly than speed does. The latency points come from the same calls: replying faster than about 2 seconds made no measurable difference, and calls went measurably worse once the agent took more than about 3 seconds to reply.
Stacks and speech-to-speech
| Figure | Weight | Full marks | Zero |
|---|---|---|---|
Task completionscenario_adherence_check | 21% | 100% | 80% |
| An agent that completes fewer than four calls in five is not fit to deploy. On a full sweep, a cascade's figure is 25% what its own calls (20 or more) measured and 75% what every other stack shows about its transcriber, language model and voice, the same split for every stack, so a lucky run does not lead. Where the sweep is too small for that, and for a speech-to-speech model, the figure is the stack's own. In an industry a stack has a handful of calls, so a cascade's figure there is what its transcriber, language model and voice show across every stack in that industry's calls, and a speech-to-speech model's is its own calls there. How a particular pairing beats or misses its parts shows on the main board, where each stack has all of its calls. | |||
Response timeresponse_time | 17% | 2.0 s | 6.0 s |
| In real calls, replying faster than 2 s made no measurable difference, and calls went worse past 3 s. | |||
Instruction followinginstruction_follow | 13% | 5.0 / 5 | 3.0 / 5 |
| Every model scores between 3 and 5 on the judge's 1 to 5 scale, so that is the range. | |||
Semantic word error ratestt_semantic_word_error_rate | 13% | 0.5% | 3% |
| At 0.5% the best transcribers change the meaning of one word in two hundred on phone audio; at 3% one word in thirty sends the agent the wrong way. | |||
Tool usetool_use | 13% | 100% | 50% |
| An agent that mishandles its tools on half its calls cannot be trusted with the systems it runs on. | |||
Human-likenessvoice_human_likeness | 8% | 4.5 / 5 | 2.5 / 5 |
| 4.5 out of 5 is close to a person on the phone; 2.5 sounds plainly synthetic. | |||
Time to first wordtime_to_first_word | 4% | 1.0 s | 4.0 s |
| A greeting within 1 s sounds natural; 4 s of silence sounds like a dead line. | |||
Readback accuracytts_entity_readback_accuracy | 4% | 100% | 60% |
| Reading a code, amount or date wrong breaks the task it was for; the weakest voices get two in five wrong. | |||
Word error ratetts_word_error_rate | 4% | 2% | 12% |
| At 2% the voice says what it was given; at 12% one word in eight comes out wrong. | |||
Voice consistencyvoice_consistency_check | 4% | 100% | 80% |
| A voice that changes speaker on one call in five is not one a business can put its name to. | |||
Shown, not scored: Turn budget, Barge-in handlingbarge_in_yield_appropriateness
Counted only with price factored in: Cost per task
Language model
| Figure | Weight | Full marks | Zero |
|---|---|---|---|
Task completionscenario_adherence_check | 36% | 100% | 80% |
| An agent that completes fewer than four calls in five is not fit to deploy. | |||
Tool usetool_use | 27% | 100% | 50% |
| An agent that mishandles its tools on half its calls cannot be trusted with the systems it runs on. | |||
Instruction followinginstruction_follow | 18% | 5.0 / 5 | 3.0 / 5 |
| Every model scores between 3 and 5 on the judge's 1 to 5 scale, so that is the range. | |||
Time to first tokenllm_time_to_first_token | 18% | 0.50 s | 2.00 s |
| Under 0.5 s is not noticeable; at 2 s the first token alone takes two thirds of the 3 s past which real calls go worse. | |||
Shown, not scored: Prompt cache hitsllm_prompt_cache_hit_rate
Counted only with price factored in: Cost per call
Speech-to-Text
| Figure | Weight | Full marks | Zero |
|---|---|---|---|
Semantic word error ratestt_semantic_word_error_rate | 71% | 0.5% | 3% |
| At 0.5% the best transcribers change the meaning of one word in two hundred on phone audio; at 3% one word in thirty sends the agent the wrong way. | |||
Time to final transcriptstt_time_to_final_transcript | 29% | 1.00 s | 2.50 s |
| The fastest transcribers finish in about 1 s; past 2.5 s the transcript alone uses most of a natural reply time. | |||
Shown, not scored: Word error ratestt_word_error_rate
Counted only with price factored in: Cost per call
Text-to-Speech
| Figure | Weight | Full marks | Zero |
|---|---|---|---|
Readback accuracytts_entity_readback_accuracy | 32% | 100% | 60% |
| Reading a code, amount or date wrong breaks the task it was for; the weakest voices get two in five wrong. | |||
Word error ratetts_word_error_rate | 21% | 2% | 12% |
| At 2% the voice says what it was given; at 12% one word in eight comes out wrong. | |||
Time to first audio bytetts_time_to_first_byte | 21% | 0.20 s | 1.50 s |
| Under 0.2 s is not noticeable; 1.5 s is half the point where calls go worse. | |||
Human-likenessvoice_human_likeness | 16% | 4.5 / 5 | 2.5 / 5 |
| 4.5 out of 5 is close to a person on the phone; 2.5 sounds plainly synthetic. | |||
Voice consistencyvoice_consistency_check | 11% | 100% | 80% |
| A voice that changes speaker on one call in five is not one a business can put its name to. | |||
Counted only with price factored in: Cost per call
Factor in price and cost takes 25% of the score; the other weights keep their proportions. Price is scored on a log scale, so each doubling costs the same points.
A figure listed as shown, not scored is on the section’s boards and outside its score. Where a section has not collected a scored figure yet, the others share its weight.
Every call recorded and judged
Every call is recorded. Judge models score each transcript for the outcome and the conversation, and the timings come from the recording. Every figure links to the calls behind it.
Roark is on both sides of every call: it plays the caller, so it keeps the exact text its caller said, and it runs the agent under test, so it sees what the agent’s transcriber heard and what its model wrote for its voice. The transcript and voice figures come from lining those up, with no hand-made transcript in between. Each carries a margin from the synthetic voice or Roark’s transcriber in the loop. Every model faces the same ones under the same conditions, so the figures compare models on the same calls through the same voice and the same ear, though those slips need not fall evenly on each model. The rates will not match a vendor’s figure on a curated set; read them against each other.
An example: one call from this sweep
Speechmatics Linden-1 (own VAD) + OpenAI GPT-6.1 Sol + OpenAI TTS-1 HD, 23 October 2026. Highlighted turns are the ones that affected a score. Every stack's report links to its calls.
See how your own agent compares
Roark runs the same kind of calls against your agent, on your scenarios, with the same scoring.
Rank on Roark score
#1of 654 stacks
63 / 100
The leader
Where it ranks
Its models
Task completion
Task completion
How much of the caller’s task the agent completed: the share of the scenario’s checks a judge model found met in the transcript, averaged over calls. Before v1.3, the share of calls judged 4 or better out of 5.
#45 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.
#401 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.
#58 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.
#38 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.
#486 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.
#478 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.
#78 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.
#180 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.
#85 of 661 on the board · best 0.45 s
Latency spikes
Turns where the response took far longer than the rest of the conversation.
#341 of 661 on the board · best 0.0 per call
By industry
| Industry | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| Healthcare | 92% | 3.32 s | — |
| Home services | 95% | 3.19 s | — |
| Insurance | 100% | 3.06 s | — |
| Customer support | 100% | 3.43 s | — |
By caller
| Caller | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| US English, quiet line | 96% | 3.24 s | — |
| Spanish-accented English, city street | 94% | 3.35 s | — |
| Indian English, busy café | 98% | 3.29 s | — |
| Southern US, flustered, crying baby | 98% | 3.20 s | — |
| Fast and interrupting, construction outside | 98% | 3.20 s | 100% |
The calls behind the numbers
Customer support
AGENTBrightwave Internet support, this is Alex. What’s going on with your service?
CUSTOMERHi, Alex. I'm calling from a cafe I I work from home, and my Internet has been down for two hours now. There's a road crew digging up the street outside my house. I already rebooted the gateway twice and checked all the cables. But the Internet light is blinking orange. Is the roadwork causing this outage?
AGENTSorry about that. Two hours is a long time to be down when you work from home. I'll look into the roadwork question once I've pulled up your account. Can you give me your account number or the phone number on the account?
CUSTOMERSure. My account number is BW883017. Sorry. Last digit is 1. The phone number is +1 (563) 227-0830.
AGENTThe caller said the account number is BW883017 and then said "last digit is 1", which is ambiguous. BW883017 already ends in 7, so the correction would make it BW883011. I'll look up both the corrected account number and the phone number to verify.
AGENTThe phone number
CUSTOMERYeah.
CUSTOMERUh-huh. That's right. The phone number is +1 (563) 227-0830. Let me know if you need anything else to verify the account.
AGENTThe line test shows a good signal to your gateway, but the gateway isn't registered on the network. The recommended fix is a remote reset from our side, so I'm sending that now.
AGENTThe remote
CUSTOMEROkay. Go
CUSTOMERright. I'll wait for the reset. Let me know when it's done.
AGENTThe reset is done. Our system shows your gateway
CUSTOMERUh-huh. The Internet light just went solid white on the gateway. How long until my devices reconnect?
AGENTA solid white light means the gateway is back online, which matches what our system shows:
CUSTOMEROkay. So how long will it take for my laptop and other devices to reconnect? Should I do anything else, or will they pick up the signal automatically? Also, since I lost two hours of work, can you give me a credit for the downtime?
AGENTYour devices should reconnect on their own within about two minutes of the reset. If your laptop hasn't picked it up after that, turn its Wi-Fi off and on once. You don't need to do anything else. I'll apply the credit now for the downtime.
AGENTI've applied a $10.00
CUSTOMERRight. Go
AGENTI've
CUSTOMERSorry. I lost two hours yeah. I lost two hours of work because of this. Can you make the credit more than $10?
AGENTI understand, and I'm sorry about the lost work time. The $10.00 credit is what the system applied for this outage. I can't set a different amount or add to it, and I won't promise anything I can't do. It will show on your next bill for account BW883011. If you want to ask for more because of the lost work, billing can review that request. I can't submit it from my end. Is your laptop back online now? If it isn't, tell me and we'll sort it out. Since the gateway is registered and online, I don't need to schedule a technician unless the problem comes back.
CUSTOMERYeah. It looks like my laptop is back online now. Thanks for checking and for the help with the credit. That's all I needed. Goodbye.
Healthcare
AGENTRiverbend Health Clinic, this is Alex. How can I help you today?
CUSTOMERHi. I need to resched my dermatology appointment next Wednesday morning. I just got scheduled for a work shift then, so I can't make it. I really don't have much time right now. Can we do this quickly?
AGENTAbsolutely, I'll be quick. To pull up your appointment, I need your full name and date of birth.
CUSTOMERNate Brooks. 03/17/1948. Sorry. No. 1984. 03/17/1984.
AGENTThanks, Nate. I'll use the corrected date of birth, 1984.
CUSTOMERRight. That's correct. So I can only come after 3PM. What times do you have open?
AGENTThanks, Nate, you're verified. I see your dermatology appointment with Dr.
CUSTOMEROkay.
AGENTDr.
CUSTOMERYep. Got it. Can we...
CUSTOMERRight. I know who it's with. Let's just pick a new time after 3PM. What's available?
AGENTDr. Okafor has two openings after 3 PM:
-
CUSTOMERUh-huh. Alright.
CUSTOMERJust tell me the earliest one. I need to get this done fast.
AGENTThe earliest is Tuesday at 3:45 PM with Dr. Okafor. Want me to book it?
CUSTOMERNo. Tuesdays don't work after all. I pick up my daughter. What about Friday? Any openings then?
AGENTDr. Okafor's only Friday opening is at 11:30 AM, which is before your 3 PM cutoff,
CUSTOMEROkay.
AGENTso
CUSTOMERAlright. Then what about Thursday? Anything after 3PM?
AGENTYes, Thursday at 4:10 PM with Dr. Okafor. Want me to book it?
CUSTOMERYeah.
CUSTOMERThursday at 04:10PM works. Go ahead and book that. Is there any fee for changing the appointment?
AGENTI'll book that and check the fee policy at the same time.
AGENTThe policy
CUSTOMERAlright.
CUSTOMERYep. I know about the policies. Just need to know if there's a fee for rescheduling. Where should I park when I come in?
AGENTNo fee for you, Nate. Changes made more than 24 hours before the appointment are free, and your original appointment is next Wednesday, so you're well outside that window. You're now booked with Dr. Okafor on Thursday at 4:10 PM, in Building B at the Main Campus. Your confirmation code is RVB-3M8P2.
CUSTOMEROkay.
AGENTFor parking,
CUSTOMERAlright. Got it.
CUSTOMERDoctor Okafor accept Aetna insurance?
AGENTI don't have any way to check which insurance plans Dr. Okafor accepts, so I don't want to guess. The clinic's front desk or your Aetna member line can confirm that for you. Bring your insurance card and photo ID when you come in. For parking, there's free patient parking in Lot C, with the entrance on River Road. Please arrive 15 minutes early. Is there anything else I can help
AGENTwith?
CUSTOMERNo. That's all I needed. Goodbye.
AGENTGoodbye, Nate. Your appointment is Thursday at 4:10
CUSTOMEROkay.
AGENTGoodbye, Nate! Your appointment is Thursday
AGENTat 4:10 PM with Dr. Okafor, confirmation code RVB-3M8P2.
Home services
AGENTCopperline Home Services, this is Alex. What can I help you with?
CUSTOMERHi. I've got a pipe under my kitchen sink that just burst, and water's spreading across the floor toward a power strip. I haven't shut the water off yet. Can you help me out?
AGENTSafety first, so please do this right now:
1. Stay away from that power strip and anything plugged into it. Don't touch it or step in the water near it. 2. If you can reach your electrical panel without walking through water, turn off the breaker for the kitchen. If you can't get there dry, leave it and tell me. 3. Shut off the water. Look for the shut-off valve under the sink (turn it clockwise). If that doesn't stop it, close the main shut-off, usually where the water line enters your home, near the water heater, or by the meter. While you do that, tell me your phone number or the street address and I'll pull up your account. Is anyone in danger or hurt?
CUSTOMEROkay. I'm turning off the valve now. The water stopped. No one's hurt. My address is 1480 Larkspur Avenue. Unit 4 D. I moved from 4 B last month. Just so you have the right one. My phone number is (503) 555-0139. No. 0193. Can you tell me how much the visit will cost before I agree to anything?
AGENTGlad the water's stopped. I'll pull up your account and check the fees and availability now.
AGENTI still need to look you up. You gave two phone numbers, so I'll try the corrected one (503) 555-0193 and the address.
CUSTOMERYeah. That's right. It's 5035550193 and 1480 Larkspur Avenue, Unit 4 D. Go ahead and check. I'll wait.
AGENTThanks, I found your account. It shows Unit 4B, but I'll use 4D since you've moved. It also shows you as a Copperline Care member, so the fee for you is lower. Here are the options:
- Today, 200 PM to 4:00 PM, emergency visit: $99 callout with your Copperline Care membership (it would be $149 without). - Tomorrow, 8:00 AM to 10:00 AM, standard visit: $79. With a burst pipe and water near electrical, I'd recommend today's window. Do you agree to the $99 emergency callout for 2:00 to 4:00 PM today? Is there a gate code or access instructions the technician needs?
CUSTOMERCan anyone come before 2PM? That's what I'd prefer if possible. If not, I'll take the earliest window you have today. The gate code is pound 2719. How will I know whose coming and when they're on their way? And can you give me the ticket number?
AGENTI can only offer what the system shows, and 2:00 to 4:00 PM is the earliest window today. I can't promise anything sooner. I can't promise a notification or a technician name either, because I don't have that information. I'll open the ticket now. You said you'll take the earliest window today, and I've quoted the $99 callout. I'll book it on that basis.
CUSTOMEROkay. I'll take the two to 4PM emergency window for $99. Please open the ticket and let me know the number when it's ready. Yes. That's right. Go ahead and book it for two to 4PM today. I'll wait for the ticket number.
AGENTYou've agreed to the $99 callout, so I'm booking it now.
AGENTYou're booked. Here is everything:
- Ticket numberCPL-58210
- Service address1480 Larkspur Avenue, Unit 4D
- Gate codepound 2719
- Callback number(503) 555-0193
- Arrival windowToday, 2:00 PM to 4:00 PM
- Fee$99 emergency callout (Copperline Care member rate)
The technician is Dana. Dana will text (503) 555-0193 about 30 minutes before arriving, so keep that phone nearby. Until then, keep the water off at the valve and stay clear of the power strip and the wet floor. Don't plug anything in near the water. If the water starts again, shut off the main. If you see sparks or smell burning, leave the unit and call 911. Is there anything else you need?
CUSTOMERNo. That covers everything. So just to confirm, ticket number CPL58210. Service at 1480 Larkspur Avenue, Unit 4 D, gate code pound 2719. Callback number, (503) 555-0193. Arrival window, two to 4PM. $99 emergency callout. Technician Dana. I'll keep my phone close and follow your safety advice. Thank you for your help. Goodbye.
Insurance
AGENTNorthgate Mutual claims, this is Alex. Are you somewhere safe right now?
CUSTOMERYeah. I think so. Sorry. I'm just at home, but my baby's crying, and I'm kinda flustered. I'm standing next to the car and nobody's hurt. Is that okay?
AGENTThat's completely okay, and no need to apologize. Take whatever time you need with your baby. I'm glad you're at home and that nobody is hurt. When you're ready, I'll need your policy number and the last name of the policyholder so I can pull up your policy.
CUSTOMERThank you. Appreciate that. Let me give you my policy number first. It's NG4471903 No. Sorry. Last one is 2, and the last name is Carter. Is that alright?
AGENTSure, I'll use the corrected number. Let me look that up.
AGENTThank you, Ms. Carter. I found your policy and verified it. Just to confirm, the policy number I have is NG4471902, and it's for a 2021 Honda CR-V. Is that right? If so, tell me what happened. Take it at whatever pace works with your little one. 1. When did the accident happen (date and approximate time)? 2. Where did it happen? 3. What happened?
CUSTOMEROh, I'm sorry. But it's actually Wade Carter, not miss Carter. I just wanna make sure that's clear. Yes. That's the policy number, and it's 2021 Honda CR V. I think so. Please tell me that's the right one. So, the accident just happened about twenty minutes ago. I was stopped at a red light at Sassy 5th Avenue in Alder Street. The other car hit the back of my car. The bumper's crushed and the rear wheel is rubbing, so I can't drive it. Police didn't come. Is that enough for you, or do you need more details?
AGENTMy apologies, Mr. Carter, and thank you for the correction. Yes, the policy number NG4471902 and the 2021 Honda CR-V are right. That's a good start. I have a few more things to ask:
1. The other driver
CUSTOMERAlright. That's fine. Sorry if I'm talking fast. I just wanna get this sorted. Go ahead and ask whatever you need. I'm ready. Yeah. Sure. So the other driver's name is Kevin Lund. He's insured with Pacific Shore. His plate is 7KX like x-ray, R214. Is that what you needed?
AGENTNo need to apologize, Mr. Carter, you're doing fine. That's what I needed. Let me read back the plate to make sure I have it right: 7-K-X-R-2-1-4. Is that correct? I also want to confirm the location. I heard "5th Avenue and Alder Street." Is that right, and which city is it in? Since the car can't be driven with the rear wheel rubbing, I can also set up a tow. To do that, I need to know where the car is right now. Is it at your home, or still at the accident spot?
CUSTOMERThank you for checking. I'm just worried about messing something up. Yeah. That plate's exactly right. 7KX like X-ray, R214. And it's S E 5th Avenue In Alder Street. It's in Portland. The car is still at the accident spot. I couldn't drive it home. If you could help get a tow, I'd really appreciate it. Is that okay?
AGENTAbsolutely, that's okay. You've done everything right. You said you're at home, but the car is still at the accident spot, so I'll send the tow to SE 5th Avenue and Alder Street in Portland. I'll also check your coverage and open the claim now.
AGENTYour claim is open. I'll check your coverage and request the tow now.
AGENTMr. Carter, here is where things stand:
- Claim numberNGM-904417
- Policy numberNG4471902
- Other driver's plate7KXR214
- AdjusterPriya Shah, who will contact you within one business day. - Tow: Eastside Towing is on the way to SE 5th Avenue and Alder Street. They estimate about 45 minutes. They'll take the car to our partner repair shop at 9th and Morrison. Since you're at home and the car is at the accident site, you'll need to be there, or have someone there, when the tow truck arrives. I haven't looked up your coverage details yet, such as your deductible and rental car. Would you like me to check those now? And what's the best phone number for the tow driver to reach
AGENTyou?
CUSTOMERAlright. Thanks for all that. Sorry. I am a little anxious, so I just wanna make sure everything's clear. Yes. Please check my coverage. Especially my deductible, and if I get a rental car. And for the tow driver, my phone number is (912) 302-6283. Is that okay for them to call?
AGENTYes, that number works. I'll look up your coverage now.
AGENTI'm sorry, Mr. Carter. I need to correct something. I sent the tow request before you gave me your phone number, so the tow company doesn't have it yet. I don't have a way to add it to the dispatch that's already been made. Please keep your phone close, and if you'd like, share the number directly with Priya Shah when she contacts you. Here is your coverage:
- Collision deductible$500
- Rental car$40 per day for up to 30 days, starting from the day the claim was opened
- TowingCovered up to 25 miles
I don't have any information on how long repairs will take or how much they'll cost. Priya Shah can go over that with you. Is there anything else I can help with while you wait for the tow?
CUSTOMERThank you. That helps.
CUSTOMERSorry. Just to check, So I have to pay the $500 deductible? But he hit me, so I shouldn't have to pay that. Right? And, is my premium going to go up because of this? I just wanna make sure I understand everything before Priya calls me.
AGENTThose are good questions to bring to Priya. The deductible: Your policy has a $500 collision deductible. I can't tell you whether you'll end up paying it, because that depends on how fault is decided and on whether Pacific Shore, the other driver's insurer, pays for the damage. I don't want to promise something I can't confirm. Priya can explain how that works and what happens to the deductible if the other driver is found at fault. Your premium: I don't have any information about how this claim might affect your premium, and I won't guess. Please ask Priya that too. For when she calls, it may help to write down these points:
- You were stopped at a red light when you were hit from behind. - Police did not attend. - The other driver is Kevin Lund, insured by Pacific Shore, plate 7KXR214. If you can, take photos of the damage and of the other car and plate while you wait for the tow. Is there anything else I can help with right now?
CUSTOMERNo. I think that covers it. Sorry for all the questions. I just get nervous about these things. I've got the claim number, and I'll keep my phone close for Priya and the tow. Thank you for your help, and goodbye.