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
#24of 654 stacks
59 / 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.
#28 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.
#643 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.
#489 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.
#586 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.
#550 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.
#585 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.
#160 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.
#49 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.
#254 of 661 on the board · best 0.45 s
Latency spikes
Turns where the response took far longer than the rest of the conversation.
#49 of 661 on the board · best 0.0 per call
By industry
| Industry | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| Healthcare | 94% | 2.75 s | — |
| Home services | 95% | 2.88 s | — |
| Insurance | 100% | 2.44 s | — |
| Customer support | 100% | 2.89 s | — |
By caller
| Caller | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| US English, quiet line | 96% | 2.61 s | — |
| Spanish-accented English, city street | 98% | 2.87 s | — |
| Indian English, busy café | 98% | 2.72 s | — |
| Southern US, flustered, crying baby | 98% | 2.59 s | — |
| Fast and interrupting, construction outside | 96% | 2.74 s | — |
The calls behind the numbers
Customer support
AGENTBrightwave Internet support, this is Alex. What’s
AGENTgoing on with your service?
CUSTOMERHi, Alex. Oh, yeah. So my internet has been down for about two hours now. I work from home and there is a road crew digging up the street outside. I already rebooted the gateway twice and checked all the cables. The internet light is blinking orange. Can you check if the road work is causing this? My account number is BW883017. Sorry, last digit is one.
AGENTThanks. I'll use
AGENTthe corrected account number, BW883017, and check the account and outage status now.
AGENTNo
CUSTOMERGo ahead. I just want to confirm the last digit is one, so bear with double ocho Ocho three 011, right? And is the road crew outside maybe causing the outage?
AGENTThe remote reset worked, and your gateway now shows as registered and online. It can take about two minutes for your devices to reconnect, so give it a moment. The internet light should go solid instead of blinking orange. Please check that you're back online. If it's still down after a few minutes, let me know and I'll schedule a technician. Since you
AGENTwork from home, I can also look into a service credit for the downtime if you'd
CUSTOMERYeah, actually the light just went solid white. Now, how long until my devices reconnect and. Yes, please.
CUSTOMERI lost two hours of work, so I'd like a credit for the outage. Could you check that for me?
AGENTGood news on the light. Devices should reconnect within about two minutes of the reset. If any don't, restart that device's Wi-Fi. I'll apply the credit now.
AGENTI've applied a $10.00
CUSTOMEROkay. Go ahead.
AGENTI've applied a $10.00 service
CUSTOMERI.
CUSTOMERI lost two hours of work.
CUSTOMERCan you make it more than that?
AGENTI understand, two hours without internet is a real cost when you work from home. I don't have a tool to change the credit amount, and I won't promise anything beyond what the system applied. The credit on account BW883011 is $10.00, for about two hours of downtime, and it will appear on your next bill. If you'd like to ask for more, you can request a review from our billing team. I can't submit that from here, so you'd need to contact billing directly. Your gateway is registered and online with a solid light, so you should be back to normal. If the connection drops again or any device won't reconnect, contact us and I'll schedule a technician.
AGENTIs there anything else I
CUSTOMERNo. That's all. Thank you. Alex. Goodbye.
AGENTYou're welcome. To recap, account BW883011 is back online after the remote reset, and a $10.00 credit will appear on your next bill. Take care, and good luck catching up on work. Goodbye.
Healthcare
AGENTRiverbend Health Clinic, this is Alex. How can I
AGENThelp you today?
CUSTOMERHi.
CUSTOMERAlex.
CUSTOMERI am Arjun Mehta. I'm having one dermatology appointment next Wednesday morning.
CUSTOMERBut I need to move it because I have a work shift. Then.
CUSTOMERCan you help me reschedule it? I can only come after 3 p.m..
AGENTHappy to help, Arjun. First I need to verify you. Can you give me your full name as it appears on your record and your
AGENTdate of birth?
CUSTOMERYes.
CUSTOMERMy full. Name is Arjun Mehta. My date of birth is March 17th, 1948.
CUSTOMERSorry. No. 1984. March 17th, 1984.
AGENTThank you. Using
AGENTthe corrected year, 1984.
CUSTOMERThat's right.
CUSTOMERSo. About rescheduling. My appointment.
CUSTOMERCan you check what afternoon slots you have available after 3 p.m.?
AGENTThanks, Arjun, you're verified: Arjun Mehta, date of birth March 17th, 1984. I see your dermatology appointment with Dr. Okafor next Wednesday at 9:15 AM. Let me check the openings.
AGENTHere are
CUSTOMEROkay.
CUSTOMERGo ahead.
CUSTOMERI just need something after. 3 p.m., please.
AGENTDr. Okafor has two openings after 3 p.m.: 1. Tuesday at 3:45 PM 2. Thursday at 4:10 PM There's nothing after 3 p.m. on Wednesday itself. Which of these works for you?
CUSTOMERAha.
CUSTOMERTuesdays do not work. After all because I pick up my daughter. What about Friday?
CUSTOMERIs there anything available then?
AGENTFriday's only opening with Dr. Okafor is at 11:30 AM, which is before your 3 p.m. cutoff, so it doesn't fit. The only slot that meets all your requirements (after 3 p.m., not Tuesday) is Thursday at 4:10 PM. Would you like me to move your appointment to Thursday at 4:10 PM?
CUSTOMERYes.
CUSTOMERKindly move my appointment. To Thursday at 4:10 p.m..
CUSTOMERBefore you finish. Can you tell me, is there any fee for changing the appointment?
AGENTLet me check the policy first so you know before I make the change.
CUSTOMERAll
CUSTOMERright. Yes.
AGENTYour appointment is moved, Arjun. Here are the details: - Patient: Arjun Mehta - Provider: Dr. Okafor, Dermatology - New time: Thursday at 4:10 PM - Location: Main Campus, Building B - Confirmation code: RVB-3M8P2 (R-V-B, 3, M, 8, P, 2) Please arrive 15 minutes early with a photo ID and your insurance card. There was no fee for this change. Is there anything else I can help you with?
CUSTOMERWhere should I park for this appointment?
AGENTParking is free for patients in Lot C, and the entrance is on River Road. Is there anything else I can help you with?
CUSTOMERYeah. One more thing.
CUSTOMERDoes Doctor. Okafor accept Aetna insurance?
AGENTI don't have access to insurance or provider network information, so I can't confirm whether Dr. Okafor accepts Aetna. I don't want to guess and have you hit a surprise bill. The best way to find out is to call the number on the back of your Aetna card, or check the provider directory on Aetna's website. You can also ask the front desk when you check in, but it's better to confirm before Thursday. Is there anything
AGENTelse I can help you with?
CUSTOMERNo.
CUSTOMERThat covers everything.
CUSTOMERThank you for your help. Alex.
CUSTOMERGoodbye.
AGENTYou're welcome, Arjun. Your appointment is
AGENTThursday at 4:10 PM with Dr. Okafor, confirmation code RVB-3M8P2. Take care, goodbye!
Home services
AGENTCopperline Home Services, this is Alex. What can I
AGENThelp you with?
CUSTOMERHi. Yes. I need help right now. A pipe under my kitchen sink has burst and the water is spreading across the floor toward a power strip. I haven't shut the water off yet.
CUSTOMERCan you send someone out?
AGENTSafety first, before anything else: 1. Stay away from that power strip and
CUSTOMEROkay.
CUSTOMERRight.
CUSTOMERSorry. Could you repeat that? I didn't catch the.
CUSTOMERAir.
AGENTNo problem. Here's what to do right now: 1. Stay away from the power strip and don't touch it or anything plugged into it while there's water around it. 2. Shut off the water. Look under the sink for the small valves on the pipes and turn them clockwise until they stop. If that doesn't stop the water, shut off the main valve, usually where the water line enters your home, near the water heater, or by the meter. 3. If you can reach your electrical panel without stepping through water, turn off the breaker for the kitchen. If you can't reach it safely, leave it and don't wade in. Tell me once the water is off or if you're having trouble. While you do that, can you give me your phone number or the service address
AGENTso I can pull up your account?
CUSTOMERYeah. My address is 1480 Larkspur Avenue. I moved from unit four B to unit four D last month, so it's four D now. My phone number is 503.
CUSTOMER555.
CUSTOMER0139 no, 0193.
CUSTOMERI'm shutting off the water now. Okay, it has stopped.
AGENTGlad the water is stopped. Let me pull up
AGENTyour account.
AGENTFound you. I'll use unit 4D, since you've moved. Opening the request now.
CUSTOMEROkay.
CUSTOMERCan you tell me how much the visit will cost?
AGENTYou're a Copperline Care member, so the emergency callout fee for you is $99 (it would be $149 for non-members). That covers the visit itself, and any repair work would be quoted separately. The earliest emergency window is today, 2:00 PM to 4:00 PM. Would you like me to book that window at the $99 fee? Also, is there a gate code or any access instructions the technician should know about for the
AGENTbuilding?
CUSTOMERYes. The gate code. Is 2719. Can anyone come before 2 p.m.? If not, I'll take the 2 p.m. to 4 p.m. window. Who will be coming and how will I know that on the way?
AGENTThe 2:00 PM to 4:00 PM window is the earliest emergency slot available, so I can't get anyone there sooner. I also don't have a technician name to give you, and I won't guess at one.
AGENTI'll book that window now.
CUSTOMERAll right. Please let me know. The ticket number when it's ready.
AGENTThe visit is booked, and the technician assigned is Dana. Here's everything to double-check: - Ticket number: CPL-58210 - Address: 1480 Larkspur Avenue, Unit 4D - Gate code: 2719 - Callback number: 503-555-0193 - Arrival window: Today, 2:00 PM to 4:00 PM - Fee: $99 emergency callout (Copperline Care member rate). Any repair work is quoted separately. Dana will text your callback number 30 minutes before arriving, so you'll know when they're on the way. Until then, keep the water off, stay clear of that power strip and the wet area, and don't plug anything in nearby. If you see sparks, smell burning, or the water starts up again, call us right away, and call 911 if there's any immediate
AGENTdanger. Is there anything
CUSTOMERNo. That's everything. Thank you and goodbye.
AGENTYou're welcome. Stay safe, and Dana will be in touch before arriving.
AGENTGoodbye.
Insurance
AGENTNorthgate Mutual claims, this is Alex. Are you somewhere safe right now?
CUSTOMEROh, yes. I'm safe. I'm just standing next to my car on the sidewalk. There's traffic going past, but nobody is hurt.
AGENTI'm glad you're safe. Since traffic is going by, please
CUSTOMERYeah. Thank you. So I need to report an accident. Another car hit me from behind about 20 minutes ago. Right at SW fifth Avenue and Alder Street. The back bumper is crushed and the rear wheel is rubbing, so I can't drive it.
CUSTOMERPolice didn't come.
AGENTThanks for the details. Since your car can't be driven, I'll get a tow set up once we've verified your policy. Could you give me your policy number
AGENTand your last name?
CUSTOMERFor my policy number is NFS cuatro cuatro 319.
CUSTOMERZero three.
CUSTOMEROne. Two.
CUSTOMERAnd my last name is Ramirez, that's RAMIREZ.
AGENTLet me interpret that: NFS, then "cuatro cuatro" (44), then 319, then 03, 1, 2. That gives NFS-44319-0312. I'll verify with that.
AGENTI notice I put a
CUSTOMERActually, it's.
AGENTGo ahead, Ms. Ramirez. What would
AGENTyou like to correct?
CUSTOMERYes, please. The policy number is NG4471902.
AGENTThank you for the correction. The number I
AGENTchecked earlier was wrong, so I'll verify the right one now.
AGENTYou're verified,
CUSTOMEROkay, so can
AGENTMs. Ramirez, your policy is verified. I have it as NG4471902 for your 2021 Honda CR-V. Is that right? Your policy includes towing up to 25 miles, so I can send a tow truck to SW 5th Avenue and Alder Street now. What phone number should the driver call when they get there? I was also cut off a moment ago and want to make sure I answer what you were asking. What was your question? Two things I still need to finish your claim: 1. The other driver's name, their insurance company, and their license plate number. 2. About what
CUSTOMERI. Waiting for the policy, right. Okay. La.
AGENTtime the accident happened. I have roughly
CUSTOMERYeah that's correct. About 20 minutes ago. The other driver's name is Kevin Lund in Broadway, Pacific shore.
CUSTOMERAnd the plate is seven. K.
CUSTOMERX, like X-rays are two, one, four. And the phone number for the tow driver is the one I'm calling from.
CUSTOMER1779.
CUSTOMER345.
CUSTOMER104.
CUSTOMEREight.
AGENTLet me work through what you gave me. The plate sounds like 7KX214. The phone number came out as 1-779-345-1048. The insurer sounds like "Pacific Shore", and I'll note "Broadway" as unclear. I'll set up the tow first, then confirm the rest with you.
CUSTOMEROkay. So, um, can you. Tell me?
CUSTOMERHow much do I have to pay for this? And do I get a rental car? Also, is my premium going to go up and where will my car be taken?
AGENTYour tow truck is on the way. Eastside Towing should arrive in about 45 minutes. They'll call you at 1-779-345-1048, and they'll take your car to our partner repair shop at 9th and Morrison. Here is what your policy says: - Cost: Your collision deductible is $500. Towing is covered up to 25 miles. - Rental: You have rental coverage of $40 per day for up to 30 days, starting from the day the claim is opened. - Premium: I can't tell you whether your premium will change. I don't have that information, and I won't guess. I'll open your claim now, but I want to check a few details first: - Policy number: NG4471902 - License plate: 7KX214. Is that right? - Other driver: Kevin Lund. You said "Broadway, Pacific Shore". Is his insurer Pacific Shore, and what does "Broadway" refer to? Is it a street, or part of the company name? - Police: No police attended. Please confirm the plate
AGENTand clear up the insurer, and I'll open the claim.
CUSTOMERRight now. From the police came. The plate is seven.
CUSTOMERK.
CUSTOMERX like X-ray r 214.
CUSTOMERAnd the insurer is Pacific Broadway. It's just a street nearby, not part of the company name. So, um, the deductible. You said $500, but he hit me, so I should not have to pay that.
AGENTThanks, I've got the plate as 7KX214 and the insurer as Pacific Shore. One thing to check before I open the claim. Your first words were "From the police came." Earlier you said the police didn't come. Did police come to the scene or not? On the deductible: the $500 is what your policy lists for collision claims. I can't say who will end up paying it, or whether you could get it back, because I don't have that information and won't guess. The adjuster assigned to your claim can explain how fault and the deductible work. Once you confirm the police question,
AGENTI'll open the claim.
CUSTOMERYeah. Just to be clear, no police came to. The scene. It was just me and the other driver.
AGENTThank you, no police attended.
AGENTOpening your claim now.
CUSTOMEROkay. And can you tell me the claim number. Once it's open? I need to have that for my records.
AGENTYour claim is open. Your claim number is NGM-904417. I'll say it again so you can write it down: N, G, M, nine, zero, four, four, one, seven. Here is a summary of your claim: - Policy number: NG4471902 - Other driver's plate: 7KX214 (Kevin Lund, insurer Pacific Shore) - Adjuster: Priya Shah, who will contact you within one business day - Tow: Eastside Towing should arrive in about 45 minutes and will call you at 1-779-345-1048. Your car will go to our partner repair shop at 9th and Morrison. - Rental: $40 per day for up to 30 days, counted from today, the day the claim was opened - Deductible: $500 on your policy. Priya can explain how fault affects it. Please stay on the sidewalk, away from traffic, until the tow truck arrives. Is there anything else I can help with?
CUSTOMERNo, that's everything I needed. Thank you for your help. Goodbye.
AGENTYou're welcome, Ms. Ramirez. Your claim number is NGM-904417. Stay safe while you wait for the tow truck. Goodbye.