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
#360of 654 stacks
46 / 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.
#658 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.
#1 of 661 on the board · best 0.0%
Agent self-cutoffs
Times the agent cut its own sentence off mid-word.
#1 of 661 on the board · best 0.0 per call
Understanding & effort
Caller effort
How hard the caller had to work: repeating themselves, correcting, re-explaining. 1 = effortless.
Not scored by this sweep.
Repeated-info loops
Times per call the agent re-asked for something the caller had already given.
#1 of 493 on the board · best 0.0 per call
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.
#430 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.
#98 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.
#513 of 661 on the board · best 0.45 s
Latency spikes
Turns where the response took far longer than the rest of the conversation.
#21 of 661 on the board · best 0.0 per call
By industry
| Industry | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| Healthcare | 79% | 3.01 s | — |
| Home services | 64% | 3.01 s | 100% |
| Insurance | 57% | 2.87 s | 100% |
| Customer support | 75% | 2.91 s | 100% |
By caller
| Caller | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| US English, quiet line | 69% | 3.14 s | — |
| Spanish-accented English, city street | 73% | 2.83 s | — |
| Indian English, busy café | 69% | 2.98 s | — |
| Southern US, flustered, crying baby | 70% | 2.94 s | — |
| Fast and interrupting, construction outside | 65% | 2.94 s | 100% |
The calls behind the numbers
Customer support
AGENTBrightwave Internet support, this is Alex. What’s going on with your service?
CUSTOMERhi alex my internet's been down for two hours i work from home and there's a road crew digging up the street right outside the gateway's internet light is blinking orange i already rebooted it twice and checked every cable is the construction the reason for this
AGENTThanks for the details. I understand this is urgent since you work from home. Let’s start by verifying your account so I can check for any known issues in your area.
Could you please provide your account number or the phone number on the account?
CUSTOMERmm hmm account number is b w eight eight three zero one seven sorry last digit is one and the number i'm calling from is plus one seven four three two one five one seven one nine
CUSTOMERokay but there's a crew literally digging up the street outside can you check if that's affecting my line
AGENTSince there’s no reported outage, the construction might still be impacting the physical line. Let’s run a line test to see if the line from the network to your gateway is passing signals properly. I’ll initiate that now and get back to you with the results.
CUSTOMERsure go ahead
AGENTThe line test shows a good signal to the gateway, but the gateway itself isn’t registered. Let’s try a remote reset of the gateway to see if that brings it back online. I’ll initiate the reset now.
CUSTOMERright do it
AGENTThe remote reset was successful. Your gateway is now registered and online. It may take a couple of minutes for your devices to reconnect. If everything comes back up and you’re able to get online, we’re all set.
CUSTOMERyeah the internet light just went solid white how long until my device is actually reconnected
AGENTGreat to hear the light is solid white— that means the gateway is fully online. Your devices should reconnect within one to two minutes, depending on how many you have and how they connect (wired or wireless).
CUSTOMERgot it all right makes sense can you add a credit for the downtime i had i lost two hours of work
AGENTA $10 credit has been applied to your account for the downtime and will appear on your next bill. If you have any more issues or need further assistance, just let me know.
CUSTOMERi lost two hours of work can you make it more than that
AGENTI understand this was a significant disruption for your work. The standard credit for downtime is $10, which has already been applied. If you’d like, I can note your request for a review of a higher credit amount and forward it to our billing department for consideration based on the impact to your
CUSTOMERgot it
Healthcare
AGENTRiverbend Health Clinic, this is Alex. How can I help you today?
CUSTOMERhi yes i need to reschedule my dermatology appointment next wednesday morning i just got a work shift that day so i can't make it my name is lucia ramirez
AGENTI can help you reschedule your dermatology appointment. To get started, I need to verify your identity. Could you please provide your date of birth?
CUSTOMERmarzo diecisiete mil novecientos cuarenta y ocho lo siento no mil novecientos ochenta y cuatro marzo diecisiete mil novecientos ochenta y cuatro
AGENTI understand you provided your date of birth in Spanish. To make sure I have the correct information for verification, could you please confirm your date of birth in YYYY MM DD format?
CUSTOMERclaro es mil novecientos ochenta y cuatro cero tres diecisiete
AGENTGreat, I found your dermatology appointment with Dr. Okafor next Wednesday at 9:15 AM at the Main Campus, Building B. To reschedule, I need to check what morning slots are available for you. Could you tell me what day you'd like to move it to? I assume you still want a morning appointment, but please confirm.
CUSTOMERen realidad necesito venir después de las tres de la tarde porque trabajo tengo que ir a trabajar tienes alguna plaza después de las tres
AGENTI found an available slot that fits your needs: Thursday at 4:10 PM with Dr. Okafor. Would you like to reschedule your appointment to that time?
CUSTOMERsí jueves a las cuatro y diez de la tarde funciona para mí adelante y reprograme por favor
AGENTYour appointment has been successfully rescheduled. Here are the details:
New appointmentThursday at 4:10 PM with Dr. Okafor
LocationMain Campus, Building B
Confirmation codeRVB-3M8P2
Please arrive 15 minutes early with your photo ID and insurance card. Is there anything else I can help you with today?
CUSTOMERoh sí un par de cosas hay una tarifa por cambiar la cita
AGENTAccording to our clinic policy, changes made more than 24 hours before the appointment are free, but later changes carry a $40 fee. Since you're rescheduling from next Wednesday to Thursday, which is less than 24 hours away, there would be a $40 fee for this change. Is there anything else you'd like to know about the clinic or your appointment?
CUSTOMERsí dónde debo aparcar cuando entro
AGENTFor parking when you come in, you can use free patient parking in Lot C, with the entrance on River Road. Is there anything else I can help you with today?
CUSTOMERsí una última cosa el doctor okafor acepta la aetna de seguro
AGENTI understand you're asking about whether Dr. Okafor accepts Aetna insurance. Unfortunately, I don't have a tool to check specific provider insurance acceptance. To get accurate information about which insurance plans Dr. Okafor accepts, I'd recommend:
Calling the clinic's main line and asking about Dr. Okafor's insurance policies
Checking your Aetna member portal for participating providers
Bringing your insurance card to the appointment and verifying coverage at check-in With your rescheduled appointment confirmed for Thursday at 410 PM, would you like me to help with anything else today?
CUSTOMERno eso es todo gracias por su ayuda adiós
AGENTYou're very welcome! Your appointment is rescheduled for Thursday at 4:10 PM with Dr. Okafor. Confirmation code RVB-3M8P2. Remember to arrive 15 minutes early with your photo ID and insurance card, and use Lot C parking on River Road. Have a great day, and thank you for calling Riverbend Health Clinic!
Home services
AGENTCopperline Home Services, this is Alex. What can I help you with?
CUSTOMERhi uh yes i need urgent help a pipe under my kitchen sink just burst and water is spreading all over the floor heading toward a power strip i haven't shut the water off yet what should i do
AGENTFirst, safety is most important. Please shut off the water right now if you can. If the pipe is under the sink, you can usually turn the shut-off valve for that specific fixture, which is probably near the sink. If you cannot find it, turn off the main water supply to your home, typically found near where the water line enters the house, often in a basement or utility area. After you shut the water off, move anything electrical, especially the power strip, away from the water if you can do so safely— do not touch electrical devices with wet hands or while standing in water. Dry yourself completely before touching anything electrical. If the water is still spreading and you cannot shut it off, call 911 or your local emergency services immediately, as this could become an electrical hazard.
Once the water is shut off and the area is safe, I can help arrange urgent service. Can you please give me your phone number or street address so I can look you up and open a service request? Also, what is the best number to reach you back on? And can you confirm the full service address, including the unit number if there is one, and any gate code or access instructions?
CUSTOMERokay i just turned off the water under the sink and the leak has stopped the power strip is out of the way now my address is fourteen eighty larkspur avenue unit four d i moved from four b to four d last month so please use four d the gate code is pound two seven one nine my callback number is five o three five five five zero one three nine no cero uno nueve tres that's five zero three five five five zero one nine three how much will the visit cost and can anyone come before two
AGENTBased on your Copperline Care membership, the emergency call-out fee for today’s urgent visit will be $99. The earliest emergency window available is today from 2:00 PM to 4:00 PM. Would you like me to book that window for ticket CPL-58210? Just to confirm, the service address is 1480 Larkspur Avenue unit 4d, gate code # 2719, and your callback number is 5035550193. If this is correct, I’ll go ahead and schedule it now.
CUSTOMERyes that's all correct please book the two p m to four p m window for ticket c p l five eight two one zero who will be coming and how will i know they're on the way
AGENTGreat, I have booked Dana for today between 2:00 PM and 4:00 PM for ticket CPL-58210 at 1480 Larkspur Avenue unit 4d. Dana will text your callback number, 5035550193 30 minutes before arrival to let you know they are on the way. If anything changes or you have any questions before then, please call us back.
CUSTOMERthank you alex i appreciate your help goodbye
Insurance
AGENTSorry, I can't repeat that exact phrase without context or additional information. My role is to assist with insurance claims, ensuring that users are safe and guiding them through the process. If you're starting a claim, I can help by asking if you're safe, verifying your policy, and collecting necessary details. If you need assistance with a claim or have any questions about the process, please let me know, and I'll support you through it.
CUSTOMERhey yeah i just i'm a little flustered sorry my baby is crying in the background i need to start a claim another car hit the back of my car about twenty minutes ago at s e fifth avenue and alder street nobody's hurt but the bumper is crushed and the rear wheel is rubbing so i can't drive it the police didn't come can you help me get started
AGENTAre you in a safe place right now? If anyone is injured or in danger, please call 911 before we continue. If you're safe, I can help you start the claim. I'll need your policy number and the policyholder's last name to verify your coverage. After that, I'll collect the details of the accident.
CUSTOMERyeah i'm safe nobody is hurt thank goodness my policy number is n g four four seven one nine zero three wait sorry the last one is two and it's carter c a r t e r is that okay
AGENTThanks for confirming. I've verified your policy NG 447190 on the 2021 Honda CR-V. Now I'll need a few details to open the claim.
When did the accident happen? You mentioned about twenty minutes ago; can you give me the exact time?
What happened? You said another car hit the back of your car, crushing the bumper and rear wheel.
Where did it happen? You mentioned SE Fifth Avenue and Alder Street.
The other driver's name, insurer, and license plate, if you have that information.
You noted the police didn't come.
Once I have that, I'll open the claim for you.
CUSTOMERyeah i got that here the other driver's name is kevin lung he's insured with pacific shore his plate is seven k x like x ray r two one four sorry is that going to be enough
AGENTThanks, that helps. One more thing before I open the claim: what time did the accident happen? You mentioned about twenty minutes ago; if you can give me the exact time, that will complete the information I need. With the time, location, description, and the other driver's details, I'll open the claim for you right away.
CUSTOMERyeah sorry i think it was right around one p m is that good enough please tell me that's okay
AGENTOkay, your claim is now open. Here are the details:
Policy numberNG 447190
License plate2021 Honda CRV
Claim numberNGM 904417
AdjusterPriya Shah will contact you within one business day.Your collision deductible is $500. Rental coverage is $40 per day for up to 30 days, starting from when the claim was opened. Towing up to 25 miles is covered.A tow truck from Eastside Towing has been dispatched to SE Fifth Avenue and Alder Street and will arrive in about 45 minutes. They will take your vehicle to the Northgate partner repair shop at 9th and Morrison.If you need to reach me, please have your phone number ready so I can note it for the tow company. Is there anything else you need right now?
CUSTOMERum yeah sorry can you tell me do i really have to pay the deductible right and is my premium going to go up because of this i just want to make sure that i'm not going to have to pay for it
AGENTI understand your concerns. The $500 collision deductible is the amount you would typically pay toward the repair costs, but the specific impact on your premium, including any potential increase, depends on several factors that I can't determine from here. Your adjuster, Priya Shah, will contact you within one business day to discuss your claim in detail, including coverage and any applicable deductible, and can provide the most accurate information about any potential changes to your premium. If you have any other questions or need further assistance, I'm here to help.