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
#153of 654 stacks
49 / 100
Leader AssemblyAI Universal-3.6 Pro + OpenAI GPT-6.1 Sol + Cartesia Sonic-3.6, 68 / 100
Where it ranks
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.
#659 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.
#399 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
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.
#77 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.
#27 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.
#66 of 661 on the board · best 0.45 s
Latency spikes
Turns where the response took far longer than the rest of the conversation.
#7 of 661 on the board · best 0.0 per call
By industry
| Industry | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| Healthcare | 70% | 2.61 s | 88% |
| Home services | 70% | 2.24 s | 67% |
| Insurance | 41% | 2.57 s | 82% |
| Customer support | 44% | 2.55 s | — |
By caller
| Caller | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| US English, quiet line | 49% | 2.25 s | — |
| Spanish-accented English, city street | 67% | 2.56 s | — |
| Indian English, busy café | 49% | 2.74 s | — |
| Southern US, flustered, crying baby | 56% | 2.62 s | — |
| Fast and interrupting, construction outside | 61% | 2.32 s | 80% |
The calls behind the numbers
Customer support
AGENTBrightwave Internet support, this is Alex. What’s going on with your service?
CUSTOMERHi. Uh, I’m calling because my internet has been down for about two hours now. I work from home, so it’s a bit urgent. There’s a road crew digging up the street outside, and I think maybe that’s causing the problem. I’ve already rebooted the gateway twice and checked every cable. The internet light is blinking orange. My account number is BW883017. Sorry, last digit is 1. Can you check if the outage is because of the road work?
AGENTI am sorry to hear you are dealing with this disruption.I understand how urgent that is. Let me check the outage map for that area right now. I am pulling up account B W eight eight three zero one one to see what is happening. It looks like a physical line cut was reported near your street due to that construction, and our field technicians are already on site working on repairs. We expect service to be restored within the next two hours, and I can send you a text update as soon as it is back up.
CUSTOMEROkay, so the road work did cause this. Uh-huh. Will the text update come to the number I’m calling from, +1 308-253-0090? And, uh, can you tell me if there’s anything I can do on my end to speed things up, or should I just wait? Also, since I’ve lost two hours of work, can I get a credit for the downtime?
AGENTI will definitely send the text to three zero eight two five three zero zero nine zero. You should wait for the technicians as resetting the modem won't help with a line cut. I can apply a credit for the two hours of downtime right now.
AGENTI can apply a credit for the downtime, but I need your account ID to do that. Can you please confirm your account ID?
CUSTOMERYes, my account number is BW883011. That’s right. So, uh, how much will the credit be?
AGENTI've applied a ten dollar credit to your account for the two hours of downtime. This will appear on your next bill.
AGENTYou are all set, and that ten dollar credit has been added to your next billing statement. We are keeping a close eye on the line repair and will ensure your connection is restored as swiftly as possible. Please let me know if there is anything else I can assist you with today.
CUSTOMERUgh, I lost two hours of work. Can you make it more than $10?
AGENTI understand this is frustrating. Is there anything else I can help you with regarding your internet service?I understand how frustrating that must be, and I wish I could offer more. However, the credit policy is set to compensate for the specific two-hour downtime period. I can certainly note your concern in your account for future review. We really appreciate your patience while the road crews finish up the repairs. Is there anything else I can check on for you today?
CUSTOMERNo, that’s all I needed. Thank you for your help. Goodbye.
AGENTYou're very welcome. I hope your service comes back online soon. Have a great day.You are very welcome, and I hope your service comes back online soon. Have a wonderful day!
Healthcare
AGENTRiverbend Health Clinic, this is Alex. How can I help you today?
CUSTOMERHello, Alex. I am having one appointment for dermatology next Wednesday morning. I need to move it because now I am having a work shift then. My name is Arjun Mehta. Can you help me reschedule?
AGENTHi Arjun, I can certainly help you reschedule. Let me look up your account. Could you please confirm your date of birth for verification? Once I have that pulled up, we can find a new day and time that works better with your work schedule.
CUSTOMERYes, March 17, 1948. Sorry, no, 1984. March 17, 1984. I can only come after 3:00 PM.
AGENTThat date format isn't quite right for my system. CouldThank you for the correction. Let me verify your account with your full name and date of birth.
AGENTThank you for the correction. Let me verify your account with your full name and date of birth.
CUSTOMERYes, that's right. My full name is Arjun Mehta, and my date of birth is March 17, 1984. Go ahead.
AGENTI found your dermatology appointment with Dr. Okafor next Wednesday at nine fifteen in the morning. Since you can only come after three PM, let me check Dr. Okafor's availability for that day.
AGENTI see two afternoon openings after three o'clock with Doctor Okafor. We have an opening on Tuesday at three forty-five in the afternoon, or on Thursday at four ten in the afternoon. Which of those two options would work better for your schedule?
CUSTOMERTuesdays do not work for me, actually. I pick up my daughter then. What about Friday? Is there anything available?
AGENTI understand Fridays work better for you. Let me check Dr. Okafor's Friday schedule more closely for you. It looks like the only Friday opening is in the morning at eleven thirty, which is before your three o'clock cutoff. However, we do have that Thursday slot open at four ten in the afternoon. Would you like to book that Thursday appointment instead?
CUSTOMERYes, Thursday at 4:10 p.m. works for me. Please book that.
AGENTI'll reschedule your appointment to Thursday at four ten in the afternoon. This will be with Dr. Okafor in the dermatology department at the Main Campus, Building B. Let me process that change for you now.
CUSTOMERCan you tell me, is there a fee for changing the appointment?
AGENTYou are all set for Thursday at four ten in the afternoonLet me check the clinic's change policy for you.
AGENTThere is no fee for changing your appointment because you are modifying it more than twenty-four hours in advance. Your visit is confirmed for Thursday at four ten in the afternoon with Doctor Okafor. Your confirmation code is R V B three M eight P two. Please remember to arrive fifteen minutes early with a photo ID and your insurance card.
CUSTOMERAll right, got it. Where should I park when I arrive?
AGENTYou can park in Lot C on River Road, and patient parking is completely free. Just follow the signs to the entrance, and you will be all set for your appointment.
CUSTOMEROkay, one more thing. Does Dr. Okafor accept Aetna insurance?
AGENTI don't have access to specific insurance provider information for our doctors. I recommend calling the clinic directly to confirm whether Dr. Okafor accepts Aetna insurance for your visit. Our billing department can check that for you right away. Is there anything else I can help you with today, Arjun?
CUSTOMERNo, that's all. Thank you for your help. Goodbye.
AGENTYou are very welcome, Arjun. It was a pleasure helping you reschedule your appointment. Have a wonderful day and see you on Thursday at four ten. Goodbye!
Home services
AGENTCopperline Home Services, this is Alex. What can I help you with?
CUSTOMERHey, uh, sorry. I'm a little flustered over here. I've got a pipe under my kitchen sink that just busted, and water's spreading across the floor toward a power strip. I've got a baby crying, so, um, can you send someone out? I haven't shut the water off yet. What should I do?
AGENTOh my goodness, that sounds incredibly stressful. Please turn offPlease turn off the main water valve and unplug that power strip immediately to keep everyone safe.First, go shut off the main water valve immediately to stop the flow. Then unplug that power strip to prevent any electrical hazards. Once you and your baby are in a safe spot, please give me your address. I will get an emergency technician dispatched to your home right away. Take a deep breath, and let me know when you are ready.
CUSTOMEROkay. Sorry. I'm just a little anxious right now. I just shut off the water, and the power strip's unplugged. The baby's safe too. So, um, my address is 1480 Larkspur Avenue, Unit 4D. I moved from 4B to 4D last month. Just making sure you have the right unit. Is that all right?
AGENTI am ready to help you. Please provide your street address so I can locate your account and get a technician to you as quickly as possible.That is excellent work. I am sorry you are dealing with this stressful situation. Please provide your street address so I can locate yourI am glad to hear that everyone is safe. Please provide your street address so I can locate your account and open an emergency service request for thePerfect, that is completely clear. Let me look up your account to confirm the details for that unit. Give me just a moment to pull up the information.
AGENTI have found your account for unit 4B, but I see you have moved to unit 4D. Please confirm that you are currently at 1480 Larkspur Avenue, Unit 4D, and that this is where the emergency is happening. I need to make sure I send the technician to the correct location. Also, could you provide the best callback phone number to reach you while I set up the emergency dispatch?
CUSTOMERYes, that's right. I'm at 1480 Larkspur Avenue, Unit 4D. The emergency's happening here, not 4B. Sorry if that's confusing, but please make sure it's Unit 4D. My callback number is 503-555-0139. No, sorry, 0193. Is that okay? Will the technician be able to get in with the gate code? It's #2719.
AGENTThank you for the correction and the phone number. I will open an emergency service request for the burst pipe at 1480 Larkspur Avenue,Thank you for clarifying. I have noted the correct address as 1480 Larkspur Avenue, Unit 4D, and the callback number is 503-555-0193. I will also include the gate code 2719 so the technician can enter easily. I am creating the emergency service request for the busted pipe under your kitchen sink right now.
CUSTOMERAll right, thank you. Uh, sorry. Just wanna make sure everything's all set. Before you send someone, could you tell me how much the visit is going to cost? I just need to know before I agree to anything. And, um, can anyone come before 2:00 p.m.? I really need help as soon as possible.
AGENTSince you are a Copperline Care member, the emergency callYour total emergency callout fee will be ninety-nine dollars sinceThe emergency callout fee is ninety-nine dollars since you are a Copperline Care member. Unfortunately, the earliest available window is today between two and four in the afternoon. I can book that slot for you right now if you are ready.
CUSTOMEROkay, $99. That's not too bad. Uh, but 2:00 to 4:00 p.m. is the earliest? That's a little late for me, but I guess I'll take it if there's nothing sooner. Who's coming out, and how will I know they're on their way? Will I get a call or a text? And, um, can you give me the ticket number for this visit? I just wanna make sure I've got everything right.
AGENTI will book the technician for the two to four o'clock window today. You will receive a call from the dispatch teamThe ticket number is CPL five eight two one zero. You will receive a call from the technician as they are on their way. I will book the two to four p.m. window for you right now.
CUSTOMERAll right, CPL58210. Got it. Thank you so much. I really appreciate your help. I'll wait for the call from the technician. Goodbye.
AGENTYou are all booked for today between two and four in the afternoon under ticket number CPL five eight two one zero. Your technician will be Dana. Dana will send a text update to your callback number thirty minutes before arriving. Please let meDana is the technician who will be coming out to fix your pipe. You will receive a text message on your phoneYou are very welcome. Dana will be your technician, and she will text you thirty minutes before she arrives.You are very welcome. Dana will be there between two and four p.m. today. Take care of yourself and your baby, and have a good day.
Insurance
AGENTNorthgate Mutual claims, this is Alex. Are you somewhere safe right now?
CUSTOMERUm, yeah. I'm okay. I'm at home now, but I can't drive my car. I got hit about 20 minutes ago. Sorry, the baby's crying in the background. I'm a little flustered. Nobody's hurt, but I just wanna make sure everything's going to be all right. Is it okay if I go ahead and start a claim?
AGENTI am so sorry to hear that. Please stay where you are safe and keep the baby calm. II am sorry to hear about your accident, but I am glad everyone is safe. I can certainly help you start your claim right away. Take a deep breath, and do not worry about the baby crying. When you are ready, could you please share your policy number or full name so I can pull up your account? We will walk through this step by step together.
CUSTOMERThank you. I appreciate you saying that. Uh, my policy number, let me read it out for you. It's NG4471903. Oh, sorry, last one is 2. So NG4471902. Is that coming up okay for you?
AGENTThank you for that. Let me quickly verify your policyYes, that policy number is coming through perfectly. Let me verify your account details right now to ensure everything is secure. Please hold for just a moment while I pull up your information.
AGENTYes, that policy number is coming through perfectly. Let me verify your account details right now to ensure everything is secure. Please hold for just a moment while I pull up your information. I have your account open, and I see your twenty twenty-one Honda CR-V listed. Whenever you are ready, could you tell me a little bit about what happened and where the accident occurred?
CUSTOMERAll right. Thanks for confirming. So, uh, I was stopped at a red light at SE 5th Avenue and Alder Street, and another car, Kevin Lund, he's insured with Pacific Shore, 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 out, but nobody's hurt. Is that enough to get things started?
AGENTYou're very welcome. Please take your time, and let me know when you are ready to describe what happened.Thank you for sharing those details, and I am glad no one was hurt. Since your vehicle cannot be driven,That is plenty of information to get us started, and I am glad no one was injured. I will open that claim for you right now with the details you provided. Please hold for just a moment while I process this.
AGENTYour claim has been opened successfully, and your claim number is N G M dash nine zero four four one seven. Your assigned adjuster is Priya Shah, who will reach out to you within one business day. Since your car is not drivable and you have roadside towing and rental reimbursement on your policy, would you like me to arrange a tow truck for you now?
CUSTOMERYes, please, if you could. I'd really appreciate it. Um, could you tell me where the car will be taken? And, uh, while you're at it, do I get a rental car right away or how does that work? Sorry, I just wanna make sure I don't mess anything up here.
AGENTI understand your concern, and I will make sure everything is handled correctly for you. Let me arrange the tow truck for your vehicle right now. I will need to know the exact location where the car is parked so the driver can find it. Is the car currently at your home?
CUSTOMERNo, sorry. It's not at my home. The car's still at SE 5th Avenue and Alder Street, right where the accident happened. Is that all right or do I need to move it somewhere else for the tow?
AGENTThat is perfectly fine, and we will send the tow truck directly to that location. I will dispatch the driver to SE 5th Avenue and Alder Street right away. To make sure the driver can reach you if needed, could you please provide a phone number where you can be reached?
CUSTOMERYeah, sure. It's 507-415-2110. Is that all right? Just wanna make sure you got the right number.
AGENTThat phone number is all correct, and I have noted it down. I will dispatch the tow truck to SE 5th Avenue and Alder Street right now. Regarding the rental car, since you have rental reimbursement on your policy, you will be able to get a rental once the vehicle is at the repair shop. Priya Shah will guide you through that process when she contacts you tomorrow. That phone number is all correct, and I have noted it down. I will dispatch the tow truck to S E Fifth Avenue and Alder Street right now. Regarding the rental car, since you have rental reimbursement on your policy, you will be able to get a rental once the vehicle is at the repair shop. Priya Shah will guide you through that process when she contacts you tomorrow.
AGENTI have dispatched the tow truck to Southeast Fifth Avenue and Alder Street, and the driver has your contact number, five zero seven, four one five, two one one zero. They will typically tow it to one of our approved local collision repair facilities or a shop of your choice. As for the rental car, your policy includes rental reimbursement, so Priya Shah will help coordinate your reservation when she contacts you within one business day. Is there anything else I can help you with right now?I can hear the little one crying, so let me make this quick for you. Your tow is on the way, and Priya will handle the rental details tomorrow. Please take care of yourself and the baby. eimbursement, so once your vehicle is at the repair shop, Priya will help you arrange a rental. Is there anything else I can assist you with right now?
CUSTOMERbaby crying Um, I just got a couple more questions, if you don't mind. How much am I gonna have to pay for this? Is there a deductible or since he hit me, should I not have to pay that? And, uh, will my premium go up because of this? Sorry, I just wanna make sure I know what to expect.
AGENTI understand you want to be clear on costs, so let me look up your specific coverage details right now. Please hold for just a moment while I check your policy.
AGENTYour collision deductible is five hundred dollars, and your policy covers rental reimbursement up to forty dollars per day for up to thirty days, as well as towing up to twenty-five miles. Since the other driver rear-ended you, our team will pursue his insurance to recover your deductible. Generally, not-at-fault accidents do not increase your rates, but Priya Shah will discuss the exact details and any next steps with you.
CUSTOMERAll right. So just to be sure, since he hit me, I shouldn't have to pay that 500 dollar deductible, right? And you're saying my premium shouldn't go up? Sorry, I just wanna make sure that's how it works. Is there anything I need to do right now or just wait for Priya to call?
AGENTThat is exactly right, and I am glad we couldThat is exactly right, and we will work to get that deductible back for you. You do not need to do anything else right now, so just wait for Priya Shah to call you tomorrow. I will keep an eye on the tow status and update you if anything changes. Please focus on yourself and your baby right now, and let us take care of the rest. Have a wonderful rest of your day, and take care.