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
#101of 654 stacks
55 / 100
Leader AssemblyAI Universal-3.6 Pro + OpenAI GPT-6.1 Sol + Cartesia Sonic-3.6, 68 / 100
Where it ranks
Its models
Task completion
Task completion
How much of the caller’s task the agent completed: the share of the scenario’s checks a judge model found met in the transcript, averaged over calls. Before v1.3, the share of calls judged 4 or better out of 5.
#81 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.
#421 of 661 on the board · best 0.0%
Agent self-cutoffs
Times the agent cut its own sentence off mid-word.
#1 of 661 on the board · best 0.0 per call
Understanding & effort
Caller effort
How hard the caller had to work: repeating themselves, correcting, re-explaining. 1 = effortless.
Not scored by this sweep.
Repeated-info loops
Times per call the agent re-asked for something the caller had already given.
#180 of 493 on the board · best 0.0 per call
Speech-to-Text
Semantic word error rate
The caller's words the agent's transcriber got wrong in a way that changes the meaning, as a judge reads each difference.
#304 of 648 on the board · best 0.0%
Word error rate
The caller's words the agent's transcriber missed, changed or invented, against what the caller actually said.
#359 of 648 on the board · best 1.2%
Text-to-Speech
Readback accuracy
Share of the codes, amounts, dates and phone numbers the agent wrote that its voice read out correctly. One wrong digit is a miss.
#554 of 603 on the board · best 100%
Word error rate
The share of the words the agent wrote that its voice dropped or changed, read back from Roark’s transcription of the recording; the same transcriber hears every voice.
#634 of 648 on the board · best 1.9%
Voice
Human-likeness
How human the agent's voice sounds, scored by an audio model on intonation, rhythm and expressiveness. 5 = hard to tell from a person.
#292 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.
#308 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.
#429 of 661 on the board · best 0.45 s
Latency spikes
Turns where the response took far longer than the rest of the conversation.
#391 of 661 on the board · best 0.0 per call
By industry
| Industry | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| Healthcare | 91% | 3.71 s | — |
| Home services | 98% | 3.46 s | — |
| Insurance | 95% | 3.38 s | — |
| Customer support | 100% | 3.67 s | — |
By caller
| Caller | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| US English, quiet line | 98% | 3.41 s | — |
| Spanish-accented English, city street | 94% | 3.48 s | — |
| Indian English, busy café | 98% | 3.17 s | — |
| Southern US, flustered, crying baby | 98% | 3.74 s | — |
| Fast and interrupting, construction outside | 92% | 3.96 s | 100% |
The calls behind the numbers
Customer support
AGENTBrightwave Internet support, this is Alex. What’s going on with your service?
CUSTOMERHey. Uh, sorry. I got a baby crying here, so I'm a little flustered. My internet's been down for about two hours now, and I work from home, so this is stressful. There's a road crew digging up the street outside. Could that be the reason? I already rebooted the gateway twice and checked all the cables. The internet lights blinking orange. Can you check what's going on? My account number is BW883017. Oh, sorry, last digit is one, is that okay?
AGENTNo problem at all. I'll use the corrected number, BW883011, and check it now.
AGENTFound it. Now I'll check for an outage and run the line test.
AGENTI made the outage check and line test at the same time, but the system instructions say the outage check should come first. I'll run it now.
CUSTOMERAll righty, that's fine. Sorry. Is this going to take long? I just need to know if that road crew is the reason for the outage.
AGENTGood news: the remote reset worked, and the gateway now shows as registered and online. Your devices may take about two minutes to reconnect. The orange light should change once it's connected. Can you check whether you're back online? If it's still down after a few minutes, let me know
CUSTOMERI. Wait. The light just went solid white now. Is that normal? How long till my devices start connecting? I just want to make sure everything's all right.
AGENTYes, solid white is what you want to see. It means the gateway is connected and online, which matches what I saw on my end. Your devices should reconnect within about two minutes of the reset. If a laptop or phone doesn't pick it up on its own, toggle its Wi-Fi off and on, and that usually does it. Since you work from home and lost about two hours, I can also apply a service credit for the downtime if you'd like. And if anything drops again or the light goes back to orange, reach out and we'll get a technician out. Do you want the credit?
CUSTOMERYeah. Please. I lost two hours of work, so I'd really appreciate that credit. Can you tell me how much it'll be? Sorry. Will it show up on my next bill?
AGENTI'll apply it now, and the confirmation should show the details.
AGENTThe credit
CUSTOMERAll right. Thanks.
CUSTOMERSorry. Did you say something? I just want to make sure the credit goes through. Can you tell me how much it is? I lost two hours of work. Can you make it more than that?
AGENTSorry, my last message got cut off. The credit went through: $10.00 on account BW883011, and it will show up on your next bill. I can't make it larger. That's the amount the system applied for this outage, and I don't have a way to adjust it. I know it doesn't cover two hours of lost work. If you want to ask for more, you can raise it with our billing team, but I can't promise what they'll decide. If the light goes back to orange or your connection drops again, contact us right away and we'll send a technician. Is everything back up on your end?
CUSTOMERI. Yeah. Uh, seems like everything's coming back online now. Thanks for getting that sorted. And for the credit. I'll reach out if anything else happens. Goodbye.
Healthcare
AGENTRiverbend Health Clinic, this is Alex. How can I help you today?
CUSTOMERHi. Yes, this is Lucia Ramirez. I have a dermatology appointment next Wednesday morning, but I just got assigned a work shift. Then I need to move it. Can you help me reschedule?
AGENTI can help with that, Lucia. First I need to verify your identity. Could you give me your full name as it appears on your record and your date of birth?
CUSTOMERYes. Of course. My full name is Lucia Ramirez. My date of birth is March 17th, 1948. Oh, sorry. No. 1984. March 17th, 1984.
AGENTThanks, Lucia. I'll use the corrected date: March 17th, 1984.
CUSTOMERYeah. That's right. So I can only come after 3 p.m.. Is there anything open, then?
AGENTLet me pull up your appointment first.
CUSTOMEROkay.
AGENTI found your appointment with Dr. Okafor in dermatology, next Wednesday at 9:15 AM. Since you can only come after 3 PM, two of Dr. Okafor's open slots work:
- Tuesday at 345 PM
- Thursday at 410 PM
Changes made more than 24 hours before the appointment are free, and later ones carry a $40 fee. Moving a Wednesday appointment to Tuesday or Thursday is well outside that window. Would you like one of those?
CUSTOMERTuesdays don't work after all. I pick up my daughter then. What about Friday? Is there anything available?
AGENTDr. Okafor's only Friday opening is at 11:30 AM, which is before your 3 PM cutoff, so it doesn't fit. Since Tuesday is out too, the only option that works is Thursday at 4:10 PM. Would you like me to move your appointment to Thursday at 4:10 PM? If not, I can check whether other days open up.
CUSTOMERThursday at 4:10 p.m.. Works. Yes. Please move it to that time. And can you tell me where to park?
AGENTI'll move that now and pull up the parking info.
CUSTOMEROkay.
AGENTYou're all set, Lucia Ramirez. Here are the details:
- AppointmentDermatology with Dr. Okafor
- New timeThursday at 4:10 PM, Main Campus, Building B
-
CUSTOMERThat's right. Oh. Could you read back the confirmation code for the new appointment? And it is Doctor Okafor covered by Aetna insurance.
AGENTYour new confirmation code is RVB-3M8P2, that's R-V-B, 3, M, 8, P, 2. As for Aetna, I don't have any insurance coverage or network information in my system, so I can't confirm whether Dr. Okafor takes it. Please call your insurance provider or check with the clinic's billing office before your visit. A few reminders for Thursday:
- Arrive 15 minutes early with a photo ID and your insurance card. - Parking is free in Lot C, with the entrance on River Road. Is there anything else I can help you with?
CUSTOMERNo that covers everything. Thanks for your help. Goodbye.
AGENTYou're welcome, Lucia. Your appointment with Dr. Okafor is Thursday
Home services
AGENTCopperline Home Services, this is Alex. What can I help you with?
CUSTOMERYeah. I need some help. Fast. There's a pipe under my kitchen sink that just burst. Water is spreading everywhere, and it's heading right for a power strip. I haven't shut the water off yet. What do I need to do?
AGENTStay away from that power strip and don't touch anything electrical while you're standing in or near the water. If you can safely get to the breaker panel without walking through
CUSTOMERGot it.
CUSTOMERYep.
CUSTOMERI know about the power strip. I'm keeping clear. How do I shut the water off? Where's the shut off? Usually.
AGENTGood. Here's where to look:
1. Under the sink first. There
CUSTOMEROkay. Right. I see the valves under the sink. I'm turning them now. Okay. Water stopped. What's next? I really need someone out here. How soon can you get someone?
AGENTGood work, that's the hardest part done. Let me pull up your account and check the earliest
CUSTOMEROkay, I really need the earliest emergency slot, so just go ahead and check.
AGENTChecking now. I also need your phone number or street address to pull up your account.
AGENTThe earliest
CUSTOMERRight. Sorry.
CUSTOMERYeah. Got it. My address is 1480 Larkspur Avenue, and my phone number is (503) 555-0139.
CUSTOMERNo. 0193, and move to unit 4D. Last month, not four B. Can you make sure that's updated?
AGENTThanks. I'll use unit 4D and the phone number 503-555-0193. Let me pull up your account.
AGENTI
CUSTOMERRight. That's correct. Go ahead. What's the earliest slot you can get someone here. How much will this visit cost.
AGENTI found your account, and you're a Copperline Care member. I'm using unit 4D, not the 4B on file. The earliest emergency slot is today, 2:00 PM
CUSTOMERYeah.
CUSTOMERCan anyone come before two? I really need someone as soon as possible.
AGENTI only see one emergency window today, 2:00 PM to 4:00 PM. Nothing
CUSTOMERGot it.
CUSTOMERAll right. Just book that earliest. Emergency window. 2 to 4 p.m.. Who's coming? And how will I know they're on the way? And what's the ticket number?
AGENTI'll open the ticket now. I haven't been given a technician name or a way to track them, so I won't guess at either. One thing first: do you have a gate code or any access instructions for the building?
CUSTOMERYep. I just need to know when someone's. On the way. And the ticket number? That's all.
AGENTYour ticket number is CPL-58210. Before I lock in the 2:00 to 4:00 PM window, I need your OK on the fee. As a Copperline Care member, the emergency callout is $99. It's
CUSTOMERGate code is pound. 2719. So what's the fee for this visit? All right, 99 is fine. Go ahead and lock it in. The gate code is pound 2719, confirm when it's booked.
AGENTYou've agreed to the $99 fee, so I'm booking it now.
CUSTOMERRight, thanks. Someone's coming between. 2 and 4. Ticket number CPL58210. I'll keep an eye out. Appreciate it. Goodbye.
AGENTYou're booked, and here's everything:
- TicketCPL-58210
- Windowtoday,
CUSTOMERGot it. Thanks. That's all I needed. Goodbye.
AGENTYou're all set. Quick recap:
- Ticket
AGENTCPL-58210
- Windowtoday, 2:00 PM to 4:00 PM
- TechnicianDana, who will text (503) 555-0193 about 30 minutes before arriving
- Address1480 Larkspur Avenue, Unit 4D
- Gate codepound 2719
- Fee$99 emergency callout (Copperline Care rate), payable by card or check when the work is done
I can't update your unit on the account myself, since I have no tool for that. I put 4D and your new number on this ticket, so Dana will have them. Please also mention the unit change to the office so your file gets corrected. Keep the water off at the sink valves and stay clear of that power strip until Dana arrives. Take care.
Insurance
AGENTNorthgate Mutual claims, this is Alex. Are you somewhere safe right now?
CUSTOMERHi. Yes. I'm safe. I'm just standing next to my car. But there is a lot of traffic around.
AGENTI'm glad you're safe. Please stay well clear of the traffic while we talk, ideally on the shoulder or sidewalk, away from moving vehicles. Is anyone hurt, including you, any passengers, or the other driver? Even minor pain or soreness is worth mentioning.
CUSTOMEROh, no. Nobody's hurt. I'm okay. And the other driver is fine, too. There were no passengers.
AGENTThat's good to hear. Let's get your claim started. Could you give me your policy number and the last name of the policyholder so I can verify the policy?
CUSTOMERSo the policy number is ND4471903. No, sorry, last one two. And the last name is Ramirez.
AGENTThanks. I'll use the corrected number, ND4471902, and verify it with the last name Ramirez.
CUSTOMERThat's right. So, um, I was stopped at a red light at SW fifth Avenue and Alder Street, and another car hit me from behind about 20 minutes ago. The back bumper is crushed and the rear wheel is rubbing, so I can't drive it. Police did not come. Can you tell me how much I have to pay for this?
AGENTThank you, Ms. Ramirez. Your policy ND4471902 is verified for the 2021 Honda CR-V. I'll look up your coverage details so I can answer your question about what you'd pay.
CUSTOMEROkay, sure.
AGENTHere is what your policy shows:
- Collision deductible$500
- Rental$40 per day for up to 30 days, starting from the day the claim is opened
- Towingcovered up to 25 miles
I can't say yet what your final out-of-pocket cost will be. That depends on the adjuster's review and
CUSTOMERBut he hit me, so I should not have to pay that, right? Oh, I was stopped and he ran into the back of my car. Will I get a rental car right away? And in my premium going to go up because of this also. I need a tow truck. Where will my car be taken?
AGENTI'll get the tow started now. I'll also open the claim so your adjuster can answer the fault and deductible questions. I need a few details first. What's the other driver's name, their insurer, and their license plate? And what's the best phone number to reach you? While you find that, I'll dispatch the tow to SW 5th Avenue and Alder Street.
CUSTOMEROkay, the other driver is having Lund. His insurance is with Pacific Shore. The plate is 7KX, like X-ray R two one four. The best phone number is 18025218062.
CUSTOMERHello?
AGENTSorry about the pause, Ms. Ramirez. Your claim is open. Let me read back the key details:
- Claim numberNGM-904417
- Policy numberND4471902
- Other driver's plate
CUSTOMERYes, that's right. I have the claim number now. Thank you. So, uh, when will the tow truck get here? And do I need to pay anything up front?
AGENTThe tow truck from Eastside Towing should arrive in about 45 minutes. It will take your CR-V to our partner repair shop at 9th and Morrison. Please stay safely away from traffic while you wait. I have no information on any up-front payment for the tow. Your policy covers towing up to 25 miles, and I can't promise anything beyond that. Your adjuster can confirm any costs. Before we go further, I need to correct something. I cut off while reading back your details, and I need to confirm the plate. I recorded the other driver's plate as 7KXR214, which is 7, K, X, R, 2, 1, 4. Is that correct? I also only have the last name Lund for the other driver and no first name, so please give me that if you have it. Here are the answers to your other
CUSTOMERYeah that's correct. 7KX like X-ray R two one for. The first name is Kevin. Last name Lund.
CUSTOMERHello.
CUSTOMERAre you still there?
AGENTI'm here, Ms. Ramirez. Thank you, the plate 7KXR214 is confirmed. I'll note the other driver as Kevin Lund, and the claim record currently shows only "Lund". I don't have a tool to edit a claim once it's open, so please give Priya Shah his first name when she calls. On your other questions:
- RentalYour policy covers $40 per day for up to 30 days, counted from the day the claim was opened, which is today. I can't tell you whether a car will be ready right away. Priya Shah can help with that. - Who pays: Your deductible is $500. I can't say whether you'll owe it, because fault is decided in the claim review. You told me you were stopped and he hit you from behind, and that is in the claim. - Premium: I don't have any information on how this will affect your premium, so I won't guess. - Next steps: Your adjuster, Priya Shah, will contact you within one business day at the number you gave me, 1-802-521-8062. While you wait, you can take photos of both cars and the scene, and get Mr. Lund's contact information if you haven't already. Is there anything else I can help with?
CUSTOMERNo, that's all I have the claim. Number and everything I need. Thank you for your help. Goodbye.
AGENTYou're welcome, Ms. Ramirez. Your claim number is NGM-904417, and the tow truck should reach you in about 45 minutes. Priya Shah will call within one business day. Take care, and stay safe near the traffic. Goodbye.