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
#202of 654 stacks
52 / 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.
#116 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.
#591 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.
#80 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.
#151 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.
#530 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.
#602 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.
#160 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.
#602 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.
#597 of 661 on the board · best 0.45 s
Latency spikes
Turns where the response took far longer than the rest of the conversation.
#613 of 661 on the board · best 0.0 per call
By industry
| Industry | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| Healthcare | 88% | 4.43 s | — |
| Home services | 95% | 5.22 s | — |
| Insurance | 100% | 4.89 s | — |
| Customer support | 98% | 5.21 s | — |
By caller
| Caller | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| US English, quiet line | 100% | 5.03 s | — |
| Spanish-accented English, city street | 87% | 1.85 s | — |
| Indian English, busy café | 98% | 4.92 s | — |
| Southern US, flustered, crying baby | 98% | 5.66 s | — |
| Fast and interrupting, construction outside | 94% | 5.29 s | 100% |
The calls behind the numbers
Customer support
AGENTBrightwave Internet support, this is Alex. What’s going on with your service?
CUSTOMERHi, Alex. Oof, yeah, so I work from home and my internet has been down for about 2 hours now. I'm actually calling from a busy street. There's a road crew digging up the The street right outside my building. I already rebooted the gateway twice and checked every cable, but the internet light is still blinking orange. My account number is BW886. Sorry, last digit is 1. Can you check if the roadwork is causing this?
AGENTThe account number is BW886 with a last digit of 1, so I'll look up BW8861.
CUSTOMERAh, no, sorry, it's BW883011. The last digit is 1, so BW883011. Yeah, could you check that please?
AGENTUnderstood, BW883011. I'll look that up directly.
CUSTOMERYeah, that's right. Thanks. So, um, do you see if the road crew outside might be the cause? There's a lot of digging going on.
CUSTOMERHello?
AGENTSorry about the pause, I'm here. The remote reset worked, and the gateway now shows as registered and online. Devices can take about two minutes to reconnect, so the orange light should clear and your connection should come back shortly. Can you check whether the light has changed and you're back online? If it hasn't come back after a few minutes, let me know and I'll set up a technician visit. Since you were down for about 2 hours, I can also apply a service credit once you're confirmed back online.
CUSTOMERAll right, I just checked. The light has gone solid white now. How long until my devices reconnect fully? And, uh, about the credit, I lost 2 hours of work. Can you make it more than that?
AGENTA solid white light means the gateway is fully connected. Your devices should reconnect on their own within about two minutes of the reset. If one doesn't, turn its Wi-Fi off and on, or restart that device. On the credit, I can apply a service credit for the downtime on account BW883011. I don't set the amount, and I can't make it larger than the system calculates. I'll apply it now and read back exactly what's posted.
CUSTOMEROkay, that's fine. Can you let me know the amount when it's done?
AGENTThe credit is done. A $10.00 service credit has been applied to account BW883011 for the downtime, and it will appear on your next bill. Your service is back online with a solid white light, so no technician visit is needed.
AGENTIf the connection drops again, especially while the crew is still digging, call back and we'll look at it right away. Is there anything else you need?
CUSTOMERNo, that's everything. Thank you for your help. Goodbye.
AGENTYou're welcome. Good luck with the rest of your workday. Goodbye!
Healthcare
AGENTRiverbend Health Clinic, this is Alex. How can I help you today?
CUSTOMERHi, this is Arjun Mehta. I'm having one dermatology appointment next Wednesday morning, but I got scheduled for a work shift then. Kindly help me move the appointment. I can only come after 3 PM.
AGENTHappy to help, Arjun. First I need to verify you. Could you please give me your full name as it appears on your record and your date of birth?
CUSTOMERYes, my full name is Arjun Mehta. My date of birth is March 17th, 1948. Sorry, no, 1984. March 17th, 1984.
AGENTThanks for the correction.
CUSTOMERRight, so about the appointment, can you check for any slot after 3 PM?
AGENTThanks, Arjun, you're verified: Arjun Mehta, date of birth March 17th, 1984. Let me pull up your appointment.
CUSTOMEROkay.
AGENTI found your appointment: dermatology with Dr. Okafor, next Wednesday at 9:15 AM, Main Campus, Building B. Let me check Dr. Okafor's openings.
CUSTOMERHello?
AGENTSorry about the wait, Arjun.
CUSTOMERNo problem.
CUSTOMERYes?
AGENTDr. Okafor has two openings after 3 PM:
- Tuesday at 345 PM
- Thursday at 410 PM
The other openings are Tuesday at 900 AM and Friday at 11:30 AM, which are before 3 PM. Would either of the afternoon slots work for you?
CUSTOMERThose days do not work for me after all, because I pick up my daughter. What about Friday? Are there any slots after 3 PM on Friday? If not, I can take Thursday at 4:10 PM.
CUSTOMERAll right, then please book Thursday at 4:10 PM for me. That works.
AGENTFriday's only opening is 11:30 AM, so there's nothing after 3 PM that day. I'll book Thursday at 4:10 PM for you now.
CUSTOMERYes, that's good. Before we finish, can you tell me if there is any fee for changing the appointment?
AGENTYour appointment is moved. Let me check the fee policy.
CUSTOMEROkay.
AGENTYour appointment is confirmed:
- PatientArjun Mehta, date of birth March 17th, 1984
- ProviderDr. Okafor, dermatology
- New timeThursday at 4:10 PM, Main Campus, Building B
- Confirmation codeRVB-3M8P2
Please arrive 15 minutes early with a photo ID and your insurance card. On the fee, changes made more than 24 hours before the appointment are free, and later changes carry a $40 fee.
AGENTYour change was made well ahead of the appointment, so there's no charge. Any further changes would need to be made more than 24 hours before Thursday to stay free. Is there anything else I can help you with?
CUSTOMERYes, where should I park for the appointment?
AGENTFree patient parking is in Lot C, with the entrance on River Road. Is there anything else I can help with?
CUSTOMEROne more thing. Does Dr. Okafor accept Aetna insurance?
AGENTI don't have any information about which insurance plans Dr. Okafor or the clinic accepts, and I don't want to guess. Please call the clinic's front desk or Aetna directly to confirm coverage before Thursday. Bring your insurance card with you either way. Is there anything else I can help with?
CUSTOMERNo, that covers everything. Thank you for your help. Goodbye.
AGENTYou're welcome, Arjun. Your appointment with Dr. Okafor is Thursday at 4:10 PM, confirmation code RVB-3M8P2. Goodbye!
Home services
AGENTCopperline Home Services, this is Alex. What can I help you with?
CUSTOMERHi Alex, I've got a plumbing emergency. A pipe under my kitchen sink just burst and there's water spreading across the floor toward a power strip. I haven't shut off the water yet. Can you help?
AGENTLet's make this safe first. 1. Stay away from the power strip and anything plugged in near the water. Don't touch it or step in the water near it. If you can reach the breaker panel without walking through water, switch off the kitchen circuits or the main breaker. 2. Shut off the water. First try the shutoff valves under the sink, turning them clockwise. If they don't work or you can't reach them safely, turn off the main water valve. It's usually where the water line enters the house, near the water heater, in a basement or utility area, or by an outside wall. 3. If the water is still spreading, move valuables off the floor, but stay out of any water that's near outlets or cords. Is the water off now, and is everyone safe? While you do that, tell me the phone number or address on the account so I can pull you up.
CUSTOMERYeah, the water's off now. Everyone's safe. My address is 1480 Larkspur Avenue. I moved from unit 4B to unit 4D last month, so make sure that's updated. My phone number is 503-555-0139. No, 0193.
AGENTGlad you're safe.
CUSTOMEROkay, so about getting a plumber out, can you tell me how much the visit will cost, and is there any way someone can come before 2?
AGENTLet me pull up your account first so I can quote the right fee.
AGENTFound you.
CUSTOMERRight, go ahead. Yes, that's right. I'm in unit 4D now.
AGENTThanks.
CUSTOMEROkay, so what's the emergency visit fee, and can you get someone here before 2?
AGENTYou're a Copperline Care member, so your emergency callout fee is $99 instead of the standard $149. The earliest emergency window I have is today, 2:00 PM to 4:00 PM. Nothing earlier is available. The only other opening is tomorrow morning, 8:00 to 10:00 AM, but that's a standard visit, and with water spreading in your kitchen I'd recommend today.
AGENTI'll put your address down as 1480 Larkspur Avenue, unit 4D. Do you want me to book the 2:00 to 4:00 PM window today for the $99 callout fee? Also, is there a gate code or any access instructions the technician should know about, and is 503-555-0193 the best number to reach you?
CUSTOMERYes, please book the 2 to 4 PM window for today. The gate code is #2719, and 503-555-0193 is the best best number to reach me? Who will be coming, and how will I know they're on the way? Also, can you give me the ticket number?
AGENTI'll open the request and book it now.
AGENTYou're booked.
CUSTOMERGreat! Who's coming, and will I get any notification when they're on the way? Also, what's the ticket number?
AGENTYour technician is Dana.
CUSTOMERGot it.
CUSTOMERRight, go ahead.
CUSTOMERHello?
CUSTOMEROkay, all right.
AGENTSorry, I got cut off there.
CUSTOMERThanks.
CUSTOMEROkay, Dana's the technician. What's the ticket number, and will I get a notification when they're on the way?
AGENTYour ticket number is CPL-58210. Dana will text 503-555-0193 about 30 minutes before arriving. Here is everything I have booked:
- Address1480 Larkspur Avenue, unit 4D. I put a note on the request that you've moved from 4B. - Gate code: #2719
- Callback number503-555-0193
- VisitToday, 2:00 PM to 4:00 PM, with Dana
- Fee$99 emergency callout, the Copperline Care member rate
Keep the water off and stay clear of that power strip and any wet outlets until Dana arrives. If the water starts again, or you see sparks or smell burning, call us right away. If it ever feels unsafe, call 911. Is there anything else I can help with?
CUSTOMERNo, that covers everything. Thanks for your help, Alex. Goodbye.
AGENTYou're welcome. Dana will see you this afternoon. Take care, and goodbye.
Insurance
AGENTNorthgate Mutual claims, this is Alex. Are you somewhere safe right now?
CUSTOMERYeah, I'm safe. I'm standing next to my car, but there's construction right outside, so it's loud. Can we do this quickly?
AGENTGlad you're safe.
CUSTOMERRight, thanks. Another car hit me from behind about 20 minutes ago. I'm at SE 5th Avenue and Alder Street. Rear bumper's crushed, wheel rubs, can't drive it. Nobody's hurt. I need to file a claim. My policy Policy number is NG4471903. Wait, sorry, last one is 2. The other driver is Kevin Lund, insured with Pacific Shore, plate 7KX like X-ray R214. What do I have to pay for this?
AGENTThanks for the details.
CUSTOMERSure, do
CUSTOMERYeah, go ahead. What do you need?
AGENTHere is your coverage, from your policy:
- Collision deductible$500
- Rental$40 per day for up to 30 days, starting the day the claim is opened
- Towingcovered up to 25 miles
Since your car can't be driven, I can send a tow truck to SE 5th Avenue and Alder Street. Do you want me to do that? I'll also need a phone number the driver can call you on. I have two more questions before I open the claim
1.
CUSTOMERUh-huh.
CUSTOMERRight, nobody came. There wasn't a police report. Can you get the tow started? My number's 320-490-8329.
AGENTI'll start the tow and open the claim now.
CUSTOMERAnd where will the car be taken? Also, can you tell me the claim number? I need to know if I get a rental car and whether my premium is going to go up.
CUSTOMERYep, got it. Just need the claim number and the tow location. Is the rental car available right away? And will my premium change since I wasn't at fault?
AGENTYour claim is open and the tow is on the way. Claim number: NGM-904417.
CUSTOMERYeah, that's all I needed. Thanks, bye.
AGENTBefore you go, here are the answers to your questions. - Tow: Eastside Towing is on the way and should arrive in about 45 minutes.
CUSTOMERAll right.
CUSTOMERFine, I got what I needed. Thanks, goodbye.
CUSTOMERRight.
AGENTQuick recap, since it's loud there:
- Claim numberNGM-904417
- Policy numberNG4471902
- Other driver's plate7KX R214 (Kevin Lund, Pacific Shore)
- TowEastside Towing, about 45 minutes out. They'll take the car to our partner repair shop at 9th and Morrison. They'll call you at 320-490-8329. - Rental: $40 per day for up to 30 days, counting from today, the day the claim was opened. I can't say from my end whether a car is ready right away, so ask your adjuster. - Premium: I don't have any information on whether your premium will change, so I can't tell you either way. Your adjuster, Priya Shah, can answer that. She'll contact you within one business day.
AGENTTake care, and stay clear of the traffic while you wait.