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
#48of 654 stacks
57 / 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.
#629 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.
#4 of 6 on the board · best 100%
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.
#589 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.
#1 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.
#73 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.
#508 of 661 on the board · best 0.45 s
Latency spikes
Turns where the response took far longer than the rest of the conversation.
#55 of 661 on the board · best 0.0 per call
By industry
| Industry | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| Healthcare | 84% | 2.87 s | 90% |
| Home services | 82% | 2.64 s | 80% |
| Insurance | 90% | 2.78 s | 78% |
| Customer support | 84% | 2.83 s | 90% |
By caller
| Caller | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| US English, quiet line | 87% | 2.88 s | — |
| Spanish-accented English, city street | 83% | 2.88 s | — |
| Indian English, busy café | 86% | 2.96 s | — |
| Southern US, flustered, crying baby | 85% | 2.70 s | — |
| Fast and interrupting, construction outside | 84% | 2.58 s | 83% |
The calls behind the numbers
Customer support
AGENTBrightwave
AGENTInternet support, this is Alex. What's going on with your service?
CUSTOMERSorry, um I'm a little flustered right now. I got a baby crying in the back
AGENTNo problem.
AGENTGo ahead.
CUSTOMER- Thanks. I appre
CUSTOMER... Alright, so uh my internet's been out for about 2 hours now I work from home
AGENTMm-hmm.
AGENTSure.
CUSTOMERYeah, so uh
CUSTOMER- [sigh ]Sorry, I just want to make sure you got all the details. [sigh ]The internet's been down for two hours, and there's a road crew digging up the street outside. I already rebooted the gateway twice and checked every cable, but the internet light's blinking orange. My account number is B
CUSTOMERW eight eight three zero one seven
CUSTOMER. Sorry, last digit is one. Is the road work maybe what's causing this
AGENTChecking your account and the area right now.
AGENTOkay, I checked account BW 8830171. No outage is reported in your area. I ran a line test, and the signal looks good to your gateway. So I've sent a remote reset. It's showing as online again on my end. Can you give it up to two minutes and then try your usual device again?
CUSTOMEROkay, um, the light just went solid white so, I guess that's a good sign How long does it usually ta
AGENTYep, that's promising.
CUSTOMER- Alright, thanks. That's a relief! Uh, could you let me know when my devices will reconnect, or should I just keep trying every few minutes
CUSTOMER? Also, since I lost two hours of work- Is there any way you could give me a credit for the downtime
CUSTOMER? [sigh] Sorry to be a bother. I just want to make sure everything's squared away
AGENTI'll check the credit policy on this, just a second.
AGENTYour gateway is back online. Your devices should reconnect within a minute or two. And if any don't, just tap your Wi-Fi name and reconnect. No need to wait any longer. For the downtime, I've added a ten dollar service credit to account BW 8830171, and you'll see that on your next bill. Go ahead and try your work device whenever you're ready.
CUSTOMER[sigh] . Okay, I'll do that
CUSTOMER. Um, I appreciate the credit, but I lost two hours of work. Is there any way you could make it more than ten dollars
AGENTChecking.
AGENTI hear you. That's a tough hit, especially when you work from home. I've already applied the ten dollar credit and that's the maximum I can issue for about two hours on a single outage. If this happens again or lasts longer, we can review more. Would you like me to note your account about today's lost work time, and is your device back online now?
CUSTOMERYeah, uh, please go ahead and note the account about today's lost work time
CUSTOMER. And let me check real quick
CUSTOMER- [sigh] [chuckle ]O- okay, yep. My laptop just reconnected
CUSTOMER! Thank you for helping me out
AGENT[chuckle]
CUSTOMERI'm all set now
AGENTGreat! I'm glad it's back. I'll make sure today's issue is noted on account BW 8830171. Before we go, anything else on your connection you'd like me to check?
CUSTOMERGood- bye
CUSTOMERNo, I- think that's everything. [sigh ]Sorry for the trouble, and thank you for being patient with me today. [sigh ]Have a good day
AGENTYou too, and no worries at all. I'm glad we got you back online. Take care.
Healthcare
AGENTRiverbend Health Clinic. This is Alex. How can I help you today?
CUSTOMERHi, yeah, this is Jordan Ellis. I have a
CUSTOMERdermatology appointment next Wednesday morning, but I need to move it. I just found out I have a work shift then. Can you help me reschedule? I can only come after 3 PM
AGENTMm. Sure, Jordan. I can help! I'll check your appointment and see what options we have. Yep! I can help with that. Could you tell me your full name as it appears on your record and your date of birth?
CUSTOMERJordan Ellis, March 17
CUSTOMER1948
CUSTOMER. Sorry, no .1984. March 17
CUSTOMER,1984
AGENTOkay! Jordan Ellis, March 17th, 1984. I'll look that up now. Thanks! I see your dermatology visit next Wednesday at 9:15 AM with Dr. Okafor. We have two openings after 3 PM: Tuesday at 3:45 PM, or Thursday at 4:10 PM. Would either of those work better for you?
CUSTOMERTuesdays don't work for me after all
CUSTOMER, because I pick up my daughter. What about Friday
AGENTOkay, let's look at Friday then. Friday
AGENTonly has an 11:30 AM opening. So after 3 PM, the only one I see is Thursday at 4:10 PM. Would that work?
CUSTOMERYeah
CUSTOMER, Thursday at 4:10 PM works for me. Can you change it to that, please
AGENTSure. I'll take care of it.
AGENTDone. You're OH You're [chuckle] Okay. While I've got you, Go ahead.
CUSTOMEROkay. While you're
CUSTOMERIs there a fee for changing the appointment
AGENTLet's check that.
AGENTThere's no fee for this change. We only charge if you change within 24 hours of the appointment. So you're now set for Thursday at 4:10 PM with Dr. Okafor at Main Campus Building B. Your new confirmation code is R V B 3 M 8 P 2. Please arrive fifteen minutes early with photo ID and your insurance card. And just to confirm, we're canceling your original Wednesday morning slot, right?
CUSTOMERYes, that's right .And, where should I park when I come in
AGENTI'll check the parking info for that building. Perfect, I've canceled the original appointment. You can park for free in Lot C, entrance on River Road.
CUSTOMERGot it. One more thing. Does Dr. Okafor accept Aetna insurance
AGENTChecking insurance acceptance now. I'm not able to see provider insurance panels from here. The safest way is to call the main Riverbend line on your insurance card or check your Aetna portal for “Riverbend Health Clinic, Dr. Okafor, Dermatology” in the directory. You can also ask our front desk to double-check for you when you arrive. If you'd like, I can also note on your appointment that you have Aetna and want to verify coverage at check-in.
CUSTOMERYes, please note that on my
CUSTOMERappointment .And, just to confirm, the new time is
CUSTOMERThursday at 4:10 PM. And the code is RVB
CUSTOMER3 N 8 P 2, right
AGENTLet me take care of that, and then I'll confirm. Noted, and yes- your new time is Thursday at 4:10 PM with Dr. Okafor at Main Campus Building B. Your confirmation code is R V B 3 M 8 P 2. M as in "Mary".
CUSTOMEROkay [sigh] , thanks for all your help. Goodbye
AGENTYou're welcome! Take care,
Home services
AGENTCopperline Home Services, this is Alex. What can I help you with?
CUSTOMERHi. I've got an emergency. A pipe under my kitchen's sink just burst, and water's spreading across the floor toward a power
CUSTOMERstrip. I haven't shut the water off yet. Can you talk me through what I should do, and send someone out
AGENTOne moment, please.
AGENTFirst, let’s keep you safe and stop the water. Stay away from that power strip. Don’t touch it, and don’t step in any water near it. If you can reach the
AGENTplug without stepping in or reaching through water, unplug it. If that doesn’t feel completely safe, leave it and go turn off the breaker for that area or the main breaker if you’re not sure. Next, shut off the water
AGENT. Look under the kitchen There should be two
CUSTOMERAll right
AGENTsmall valves, one for hot, one for cold. Turn them clockwise
CUSTOMEROkay
AGENTuntil they stop. If you can’t turn or find them, go to your main water shutoff, usually in a basement or garage, and turn it off clockwise. While you do that, I’ll get someone out to you as an emergency. Can you tell me the service address, any unit number or gate code, and the best callback number?
CUSTOMERSure. The address is
CUSTOMER1480 Larkspur Avenue. I moved from unit 4B to unit 4D last month, so it's unit 4D now. The gate code is pound ,2-7-1-9. My callback number is 503 -555
CUSTOMER-013
CUSTOMER9. No, zero one nine three
AGENTGot it, 1480 Larkspur Avenue, Unit 4D, gate code pound 2719, and your callback number is 503-555-0193.
AGENTOne moment, I’m creating the service request.
CUSTOMERYeah
AGENTI’ve got everything set up. Service address is 1480 Larkspur Avenue, Unit 4D, gate code pound 2719. Callback number, 503-555-0193. You’re in our system as a Copperline Care member, so your emergency call-out fee is 99 dollars today, payable by card or check when the tech finishes. I’ve opened emergency ticket CPL-58210. You’re booked for today between 2 and 4 p.m., and the technician will text you about 30 minutes before arrival. Were you able to shut off the water under the sink
AGENTor at the main? And is the area near that power strip dry now, or do you have the breaker off?
CUSTOMERYes, I was able to shut off the water under the sink, and the area near the power strip is dry now. I didn't touch the plug, but I turned off the breaker for that area. Is there any way someone can come before 2pm? I'd really appreciate the earliest possible window. Who's coming out? And how will I know they're on their way
AGENT[sigh] Okay, let me check. Thanks for taking care of the water and power. That’s exactly right. Right now, the earliest emergency window we have is today from 2 to 4, which you’re already booked into. I’m sorry, I can’t move you sooner than that. Your ticket number is CPL-58210. The technician is Dana, and you’ll get a text about 30 minutes before arrival at 503-555-0193. While you’re waiting, keep the water to that sink off, and, if you can, mop up any remaining water so the area’s dry and clear. If anything
AGENTchanges, like the leak starts again, tell me right away and I’ll note it on the ticket.
CUSTOMERUnderstood. I'll keep the water off and make sure the area stays dry. I've got the ticket number
CUSTOMERCPL -58210
CUSTOMER. Thanks for your help
CUSTOMER. Goodbye
AGENTYou’re welcome,
Insurance
AGENTNorthgate
AGENTMutual claims, this is Alex. Are you somewhere safe right now?
CUSTOMERYeah, I'm safe. I'm just standing next to my car
CUSTOMER. Listen, I really don't have a lot of time. I was hit about twenty minutes ago, rear-ended while stopped at a light at Southeast Fifth Avenue and Alder Street. Back bumper's crushed, rear wheel's rubbing, so I can't drive it. No one's hurt. Police didn't come I need to get a claim started, a tow truck, and know what I'm going to have to pay My policy number is NG four four seven, one nine zero three. No ,sorry, last one is two
AGENTOkay.
AGENTRight.
CUSTOMERSo, the other driver is Kevin Lunn. He's insured with
AGENTGo on.
CUSTOMERPacific
AGENTHmm.
CUSTOMERHis plate is seven K X like X-ray, R two, one, four. So, what's the deductible here? How much do I have to pay? He hit me, so I shouldn't have to pay that, right? Do I get a rental car? Is my premium going to go up
CUSTOMER? Also, I need a tow truck. Where's the car going to be taken
AGENTLet's sort that out.
CUSTOMERYep, let's move fast. I need a claim number and I wanna know about the tow, deductible, rental, and my premium. Can you walk methrough
AGENTOkay. I can walk you through it. I've started
CUSTOMERwhat happens next
AGENTthe claim. Your claim number is NGM nine zero four four one seven, and the adjuster is Priya Shah. You'll hear from her within one business day. Let me read that policy number back to make sure I have it right. NG four four seven one nine zero two.
CUSTOMERYes, that's correct. So, about the deductible and rental. Can you go over those? I need to know if I have to pay anything, and if I get a rental right away
AGENTYour collision deductible is five hundred dollars. That would apply up front on your policy, but if the other driver is found at fault, we work to recover from their insurance, and you may get that back. You also have rental coverage, forty dollars per day, up to thirty days, starting from when the claim is opened today. On your premium, I can't say for sure if it would change. Your adjuster will review fault and your policy history and explain any impact at renewal. I have dispatched a tow, ETA is about forty five minutes. They're taking the car to our partner repair shop at Ninth and Morrison. If you'd rather choose a different shop, you can tell the driver when they arrive and let your adjuster know. What's the best callback number for the tow truck and the adjuster?
CUSTOMERAll right
CUSTOMER. Yeah, just use the number I'm calling from. It's eight zero two, two two seven, eight seven one one. That's my cell
AGENTGot it. Eight zero two, two two seven, eight seven one one. Just to confirm, the other driver's plate is seven K X, like X-ray, R two one four, correct?
CUSTOMERYeah, that's right. Seven K. X like X-ray, R
CUSTOMERtwo, one, four
AGENTThanks. So,
AGENTto recap, your policy is NG four four seven one nine zero two, and the claim number is NGM nine zero four four
CUSTOMERYep, I got all that. I just need the claim number one more time, please
AGENTSure.
CUSTOMERPerfect, got it
AGENTIt's NGM