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
#351of 654 stacks
47 / 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.
#652 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.
#621 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.
#291 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.
#15 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.
#514 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 | 82% | 2.44 s | 100% |
| Home services | 79% | 2.31 s | 90% |
| Insurance | 70% | 2.26 s | 93% |
| Customer support | 84% | 2.60 s | — |
By caller
| Caller | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| US English, quiet line | 81% | 2.32 s | 100% |
| Spanish-accented English, city street | 78% | 2.36 s | 86% |
| Indian English, busy café | 78% | 2.47 s | — |
| Southern US, flustered, crying baby | 78% | 2.66 s | — |
| Fast and interrupting, construction outside | 81% | 2.15 s | 100% |
The calls behind the numbers
Customer support
AGENTInternet support, this is Alex. What’s going on with your service?
CUSTOMERHi Alex, my internet has been down for two hours now. I work from home, so it's a problem. There's a road crew digging up the street outside and I'm wondering if that might be the cause. I already rebooted the gateway twice and checked all the cables. The internet light on the gateway is blinking orange. Can you check what's going on? My account number is BW883017. Sorry, last digit is 1.
AGENTGood news! I found your account BW-883011 and ran a line test. Your gateway wasn't registered with our network, so I sent a remote reset. It's now back online and registered. Give it about two minutes for your devices to reconnect. The road work outside likely caused a temporary service interruption. If you don't see your internet come back in the next couple minutes, let me know and I can schedule a technician.
AGENTGreat! With the gateway light solid white, your devices should start reconnecting within 2-3 minutes. Sometimes it can take up to 5 minutes for everything to fully sync up. I can apply a service credit for the two hours of downtime. Let me process that for you now.
CUSTOMERRight. The light just went solid white on the gateway. How long will it usually take for my devices to reconnect? Also, since I lost two hours of work time, can you give me a credit for the downtime?
AGENTPerfect! Your devices should reconnect within 2-3 minutes now that the gateway light is solid white. Sometimes it can take up to 5 minutes for everything to fully sync back up. I've also applied a $10.00 service credit to account BW-883011 for the two hours of downtime. It will appear on your next bill. Is there anything else I can help you with?
AGENTI understand the frustration of losing work time, but the $10.00 credit is our standard compensation for two hours of downtime. If you'd like to discuss additional compensation, you would need to contact our billing department directly at 1-800-BRIGHTWAVE. Is your internet working properly now, or is there anything else I can help you with regarding your service?
CUSTOMERI lost two hours of work. Can you make it more than that?
AGENTYou're
CUSTOMERNo, that's all. Thanks for your help, Alex. Goodbye.
Healthcare
AGENTHealth Clinic, this is Alex. How can I help you today?
AGENTI understand, Wade! Let me help you reschedule that dermatology appointment. First, I'll need to verify your account.
CUSTOMERHey, uh, hi. Sorry, I've got a baby crying in the background, so I might sound a little frazzled. Um, my name's Wade Carter. I need to move my dermatology appointment next Wednesday morning. I just found out I got a work shift then, so I can't make it. Can y'all help me reschedule? I can only come after 3 p.m.
AGENTI found
CUSTOMERYeah, that... Oh, sorry. Did you need my date of birth? It's, uh, March 17, 1948. Wait, no, sorry. 1984. March 17, 1984. Is that okay?
AGENTPerfect! I found your appointment, Wade. You have a dermatology appointment with Dr. Okafor next Wednesday at 9:15 AM at Main Campus, Building B. I can reschedule you for after 3 PM. I have these available slots: - Tuesday at 3:45 PM - Thursday at 4:10 PM Which of these would work better for you?
AGENTYes, Thursday at 4:10 PM works! And I do see a Friday slot at 11:30 AM, but that's before 3 PM. So your only option after 3 PM would be Thursday at 4:10 PM. Would you like me to reschedule your appointment for Thursday at 4:10 PM with Dr. Okafor?
CUSTOMERSorry, Tuesdays don't work after all. I have to pick up my daughter then. Could I do Thursday at 4.10 p.m.? Is that alright? Or actually, do y'all have anything on Friday after 3 p.m.? Yeah, that's fine. Thursday at 4.10 p.m. Sorry, is there any fee for changing the appointment?
AGENTGreat! Let me reschedule you for Thursday at 4:10 PM.
AGENTPerfect! I've rescheduled your appointment. Here are the details: **New Appointment:** - Thursday at 4:10 PM - Dr. Okafor, Dermatology - Main Campus, Building B - Confirmation code: RVB-3M8P2 **About the fee:** Changes made more than 24 hours before your appointment are free, so there's no charge for this change. **Please arrive 15 minutes early** with your photo ID and insurance card. Is there anything else I can help you with?
CUSTOMERYeah, sorry. Where should I park for the appointment? Is there a lot nearby, or do I need a permit or anything?
AGENTGreat news! There's free patient parking available in Lot C, with the entrance on River Road. You don't need a permit - just park in the patient area of Lot C for your appointment on Thursday at 4:10 PM.
AGENTI don't have access to Dr. Okafor's insurance information in my system. For questions about which insurance plans are accepted, I'd recommend calling our main office at the clinic - they'll be able to tell you definitively whether Dr. Okafor accepts Aetna and can check your specific coverage details. Is there anything else I can help you with regarding your appointment scheduling?
CUSTOMERAll right, that helps. Thanks. One more thing. Does Dr. Okafor accept Aetna insurance? I just want to make sure my coverage is good.
AGENTAbsolutely! Your new appointment is: **Thursday at 4:10 PM** with Dr. Okafor at Main Campus, Building B **Confirmation code: RVB-3M8P2** And remember to arrive 15 minutes early with your photo ID and insurance card. You're all set!
CUSTOMERNo, that covers it for now. Could you just read back the new appointment time and confirmation code so I know I got it right? Sorry, I just want to make sure.
AGENTYou're welcome, Wade! Your appointment is all set for Thursday at 4:10 PM with Dr. Okafor. Have a great day!
CUSTOMEROkay. Thank you for your help. Goodbye.
Home services
AGENTHome Services, this is Alex. What can I help you with?
AGENTOkay, let's get you safe first. **Turn off your water main immediately** - it's usually where the water line enters your home, often in a basement, utility room, or garage. If you can't find it, shut off the cold water valve under the sink (it's the right-hand knob). **Keep everyone away from the power strip and any outlets near water** - don't step in water if you can avoid it. I'm getting help to you right away. What's your address so I can dispatch a technician?
CUSTOMERHey, uh, sorry, I'm pretty flustered right now. I got a pipe burst under my kitchen sink, and there's water spreading across the floor. It's heading toward a power strip, and I got a baby crying in the background, so I really need someone out here fast. Can you help?
AGENTYou're doing the right thing calling right away. First, let's get you safe - **shut off your water main immediately** if you can, and keep everyone away from that power strip and any outlets near water. Don't step in water if you can avoid it. I'll get someone out to you fast. Let me look up your account and get emergency service scheduled.
CUSTOMEROkay, thank you. I'm at 1480 Larkspur Avenue. Sorry, is that going to be a problem? I just moved from Unit 4B to Unit 4D last month, so it's 4D now. Can you make sure that's the right one?
AGENTI can help you right away.
AGENTSince you're a Copperline Care member, the emergency callout fee is $99. That covers the technician coming out today to stop the water and assess the damage. Any parts or additional repairs would be quoted separately before work begins. The earliest I can get someone to you is today between 2:00 PM and 4:00 PM. Do you want me to book that emergency window for $99?
CUSTOMERAlright, I'm sh- Okay, that's good to hear. Before we go further, can you tell me how much the visit will cost? Sorry, I just want to make sure before I agree to anything.
AGENTI understand your concern, but 2:00 PM to 4:00 PM is the earliest emergency window available today. In the meantime, please make sure you've shut off the water main and keep everyone away from that power strip and any outlets near water.
CUSTOMERIs there any way someone can come before 2 p.m.? Sorry, I'm just really worried about the water spreading. If that's the soonest, then yes. Go ahead and book the earliest emergency window for me.
CUSTOMERI just shut off the water. It stopped. Thank goodness. Sorry. Can you confirm you have me at 1480 Larkspur Avenue, Unit 4D? And the building gate code is pound 2719. Is that all okay? I've shut off the water now, and we'll stay clear of the power strip. Sorry, is there anything else I should do while I wait? And who will be coming out? How will I know they're on the way?
AGENTPerfect! I've booked your emergency service for today between 2:00 PM and 4:00 PM. The fee is $99 since you're a Copperline Care member. Here's what I have: - **Service address:** 1480 Larkspur Avenue, Unit 4D - **Gate code:** #2719 - **Ticket number:** CPL-58210 - **Technician:** Dana will be coming out - **Updates:** Dana will text you 30 minutes before arrival While you wait: - Keep the water main off - Stay clear of the power strip and any outlets near water - If you can safely move items off the floor, that would help - Mop up what you can, but don't use electrical equipment near the wet areas You did great shutting off the water! Dana will text you before arriving, so keep your phone handy. If anything changes or gets worse, call us right away.
AGENTYour
CUSTOMEROkay, thanks. That's a relief. Sorry, can you repeat the ticket number just one more time? I want to make sure I got it right. And my callback number is 503-555-0139. No, 0193. Is that what you have? Thanks again for all your help. I really appreciate it. Goodbye.
Insurance
AGENTHello! I'm Alex with Northgate Mutual Insurance. I'm here to help you with your auto claim. First, are you safe right now? Is anyone injured or in any danger? If anyone is hurt or you're in an unsafe situation, please call 911 immediately before we continue. If everyone is safe, I can help you start your claim. I'll need to verify your policy first. Could you please provide your policy number?
CUSTOMERThank you.
AGENTMutual claims, this is Alex. Are you somewhere safe right now?
AGENTThank you for confirming you're safe and no one is injured. I have your policy number as NG44719032. To verify your policy, I'll need your last name as well. Could you please provide the policyholder's last name?
CUSTOMERYes, I am safe. Nobody is injured. My policy number is NG4471903. No, sorry, last one is 2. My last name is Mehta.
AGENTGreat, I've verified your policy. You're covered for collision, rental reimbursement, and roadside towing on your 2021 Honda CR-V. Now, to get your claim started, can you tell me: - When did the accident happen? - Where did it happen? - What happened? - Do you have the other driver's name, their insurance company, and license plate? - Did police attend, and if so, do you have a report number?
CUSTOMERYes, so accident happened about 20 minutes ago. I was stopped at a red light at SE 5th Avenue and Alder Street. Another car hit the back of my car. The back bumper is crushed and the rear wheel is rubbing, so I cannot drive it. The other driver is Kevin Lund, insured with Pacific Shore. His plate is 7. K, X like X-ray, Police did not come so there is no report number.
AGENTHello! I've just opened your claim. Here are the details: **Claim Number:** NGM-904417 **Policy Number:** NG44719032 **Other Driver's License Plate:** 7KXR214 Your claim has been assigned to adjuster Priya Shah, who will contact you within one business day. Since your car can't be driven safely, would you like me to arrange a tow truck for you? If so, I'll need to know your current location and a callback phone number for the tow truck driver.
CUSTOMERHello? Yes, kindly arrange the tow truck. I am at SE 5th Avenue and Alder Street, standing next to my car. My phone number is 191-230-26283.
AGENTPerfect! I've arranged a tow truck for you. Here are the details: - **Tow company