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
#97of 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.
#206 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.
#575 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.
#256 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.
#213 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.
#391 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.
#369 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.
#123 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.
#338 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.
#580 of 661 on the board · best 0.45 s
Latency spikes
Turns where the response took far longer than the rest of the conversation.
#535 of 661 on the board · best 0.0 per call
By industry
| Industry | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| Healthcare | 93% | 3.71 s | — |
| Home services | 87% | 3.79 s | — |
| Insurance | 97% | 3.35 s | 100% |
| Customer support | 98% | 4.00 s | — |
By caller
| Caller | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| US English, quiet line | 98% | 3.62 s | — |
| Spanish-accented English, city street | 90% | 3.71 s | — |
| Indian English, busy café | 90% | 3.36 s | — |
| Southern US, flustered, crying baby | 100% | 3.92 s | — |
| Fast and interrupting, construction outside | 91% | 3.92 s | 100% |
The calls behind the numbers
Customer support
AGENTBrightwave Internet support, this is Alex. What’s going on with your service?
CUSTOMERYeah. Hi. I work from home, and my Internet's been down for two hours now. The road crew digging up the street outside. Is that what's causing the outage? Already rebooted my gateway twice, checked all the cables, the Internet light is blinking orange. We figure this out quickly?
AGENTI hear you—that's frustrating with work from home. Let me pull up your account and check if there's an outage in your area.
CUSTOMERYep. I know. Just need it fixed fast. Account number is BW883017 Sorry. Last digit is 1. Can you check if the road works the cause?
AGENTGot it—account BW883017.
CUSTOMERMhmm.
CUSTOMERRight. But it's BW883011. Yeah. Go ahead and check. Really need this sorted. I'm losing work time.
AGENTThanks for the correction—BW883011.
CUSTOMEROkay.
CUSTOMERYeah. That's right. BW883011. Can you see if the road crew outside is causing this? I need Internet backup as soon as possible.
AGENTNo known outage in your area—the road work isn't showing up on our end. The line test shows your signal is good, but your gateway isn't registering.
CUSTOMERGot
CUSTOMERit. But the light's still blinking orange. I already rebooted and checked cables. Did all the basics twice. What else can you do from your end?
AGENTThe reset just went through on our end—your gateway shows registered and online now. Give it about two minutes for your devices to reconnect.
CUSTOMERYeah. Light just went solid white. How long till my devices reconnect? I want a credit for the downtime. I lost two hours of work.
AGENTGreat—solid white means you're back online. Devices should reconnect within the next minute or so. Let me apply a credit for that two-hour downtime.
CUSTOMERAlright.
CUSTOMERHow much will the credit be? I lost two hours of work. Can you make it more than the standard amount?
AGENTThe credit applied is $10.00 and it'll show on your next bill. I can't adjust it further from here, but if you feel the impact was greater, you can reach out to billing to discuss it with them.
CUSTOMERReally lost two hours of work. Can you bump it up at all right now or escalate for a higher credit? If not, just let me know so I can move on.
CUSTOMERMhmm.
CUSTOMERAlright.
AGENTI can't bump it up or escalate from my end—$10.00 is what the system applied. You'd need to contact billing directly if you want to discuss a higher amount. But you're back online now, so you can get back to work.
Healthcare
AGENTRiverbend Health Clinic, this is Alex. How can I help you today?
CUSTOMERHey. Yeah. I sure hope so. Sorry. I got a baby crying in the background.
CUSTOMERMy name is Wade Carter. I've got a dermatology appointment next Wednesday morning. But I just found out I got a work shift then, and I really need to move it. Can you help me reschedule?
AGENTOf course, I can help you reschedule! No problem at all. Let me pull up your account first. I need your date of birth to verify your information. Can you give me that in the format month-day-year or year-month-day?
CUSTOMEROh, sure. It's 03/17/1948. Sorry. No. 1984. 03/17/1984. Is that okay?
AGENTPerfect, no problem! Let me look that up for you.
AGENTGreat! I've got you verified, Wade. Now let me pull up your appointments to find that dermatology appointment for next Wednesday.
AGENTGot it! You have a dermatology appointment with Dr. Okafor next Wednesday at 9:15 AM at the Main Campus, Building B. So you need to move that because of a work shift. What day and time would work better for you? And do you have any preferences—like morning, afternoon, or any specific days that won't work?
CUSTOMERYeah. Sorry. I can only do after 3PM. Because I got work before that, and, Tuesdays don't work after all. Because I pick up my daughter those days. Is there anything open after 3PM on another day? Maybe Friday?
AGENTLet me check Dr. Okafor's availability after 3 PM on Friday and other days that might work for you.
AGENTLooking at Dr. Okafor's open slots, I have one option that fits what you need: Thursday at 4:10 PM. That's after 3 PM and doesn't conflict with your daughter pickup on Tuesdays. Friday only has an 11:30 AM slot available, which wouldn't work with your work schedule. Would Thursday at 4:10 PM work for you?
CUSTOMERYeah. I think so. Sorry. Thursday at 04:10PM should be good. Can you please confirm that's all set and nothing gets lost?
AGENTPerfect! Let me reschedule that for you right now.
AGENTExcellent! All set, Wade. Here's your confirmation:
Dermatology Appointment - Rescheduled
- PatientWade Carter, DOB 03/17/1984
- ProviderDr. Okafor
- New Day & TimeThursday at 4:10 PM
- LocationMain Campus, Building B
- Confirmation CodeRVB-3M8P2
Please arrive 15 minutes early with your photo ID and insurance card. You're all good—nothing gets lost!
CUSTOMERThank goodness. I'm real relieved to hear that.
CUSTOMERSorry. Can I just ask is there any kind of fee for changing the appointment? I just wanna make sure.
AGENTThat's a great question. Let me pull up our change policy for you.
AGENTGood news! There's no fee for your change. According to our policy, changes made more than 24 hours before your appointment are free. Since you're rescheduling well in advance of next Wednesday, you're covered at no charge.
CUSTOMEROh, that's such a relief. Sorry. I know I worry too much. Next thing, where should I park when I come for the appointment? Is there a lot for Building B?
AGENTNo worries at all! For parking, we have free patient parking in Lot C, with the entrance on River Road. That should work for your appointment at Building B on the Main Campus. Just head to Lot C when you arrive!
CUSTOMERAlright. Got it.
CUSTOMERSorry. I just wanna make sure. Does doctor Okafor accept Aetna insurance? That's what I have.
AGENTThat's a good question, but I don't have access to information about which insurance plans specific providers accept. I'd recommend calling the clinic directly during business hours—we're open Monday through Friday from 8 AM to 6 PM, and Saturdays from 9 AM to 1 PM. They can confirm that Dr. Okafor accepts your Aetna insurance right away. You can also bring your insurance card when you come in on Thursday, and our staff can verify it then.
CUSTOMEROkay. Thanks for explaining. You keep asking. I just want everything to go smooth. So just to double check, my new appointment is Thursday at 04:10PM with confirmation code RVB dash 3M8P2. That's right, isn't it?
AGENTThat's exactly right! Thursday at 4:10 PM with confirmation code RVB-3M8P2. You've got it all set. And remember to arrive 15 minutes early with your photo ID and insurance card. You're all good, Wade!
CUSTOMERAlright. Thank you so much. I really appreciate it. Y'all have a good day now. Bye.
Home services
AGENTCopperline Home Services, this is Alex. What can I help you with?
CUSTOMERHey, Alex. Sorry. I'm a little frazzled right now. My baby's screaming, and I've got a pipe under my kitchen sink that just burst. Water's running everywhere. It's getting close to a power strip on the floor. I haven't shut anything off yet. Can you help me out, please?
AGENTHey, take a breath—I'm here to help. This is urgent, and we need to act fast to keep everyone safe. Here's what to do right now:
1. Shut off the water main immediately. It's usually in your basement, crawlspace, or outside near the street. Turn the valve clockwise until it stops. This will stop the water from flowing. 2. Move that power strip away from the water or unplug it right now. Water and electricity don't mix. 3. Move your baby to a dry, safe area away from the water. Once you've done those three things, let me know and we'll get a technician out to you as soon as possible. Have you got access to your water main?
CUSTOMERYeah. I think so. Sorry. I'm just a bit shaky right now, but I'm gonna go turn it off. Let me check. Okay. I've got it, and it's off now. The water stopped. Is that alright?
AGENTPerfect. You did exactly right. Take another breath—the immediate danger is over. The water's stopped, and your baby is safe. You handled that well. Now let's get a technician out to fix this. I need a few quick details:
1. What's your phone number? 2. What's your street address, including the unit number if you have one? Once I have those, I can look up your account, open a service request, and get you booked for an emergency visit.
CUSTOMERSure. My phone number is (503) 555-0139. Wait. Sorry. That's (503) 555-0193. Please tell me that's okay. My address is 1480 Larkspur Avenue, Unit 4 D. I just moved from 4 B last month, so it's 4 D now. Is that alright?
AGENTThat's perfect—I've got it. (503) 555-0193 and 1480 Larkspur Avenue, Unit 4D. No problem at all; I'll make sure that's what we have on file. Let me look up your account now.
AGENTGreat—I found your account. You're a Copperline Care member, which is good news. I see we have Unit 4B on file, but you just told me you moved to 4D last month. Let me update that to 4D for this service request. Now let me check emergency availability and open your ticket.
AGENTExcellent.
CUSTOMERAlright. So could you tell me how much the visit will cost before I agree to it? I just wanna make sure I understand everything. And is it possible for someone to come before 2PM?
AGENTAbsolutely—I want you to know exactly what this costs before we book. Since you're a Copperline Care member, your emergency callout fee is $99. That covers the technician coming out today. Any parts or additional work beyond the callout fee would be quoted separately when the technician assesses the damage, but the visit itself is $99 for you. As for timing: the earliest emergency window we have available today is 2:00 PM to 4:00 PM. I don't have anything earlier than that, but that's still pretty quick given this just happened. Does that window work for you, or would you prefer to wait until tomorrow morning at 8:00 AM to 10:00 AM (which would be a standard visit at $79)?
CUSTOMERI'm a little worried about waiting that long, so I'll take the 2PM to 4PM window today for the emergency visit. $99 is okay? I just wanna get it fixed before things get worse. Who's coming out, and how will I know they're on their way?
AGENTPerfect—you're making the right call. Let me book that for you now.
AGENTGreat! You're all set. Here's what you need to know:
Your Service Request Summary
- Ticket NumberCPL-58210
- Service Address1480 Larkspur Avenue, Unit 4D
- Callback Phone(503) 555-0193
- Visit WindowToday, 2:00 PM to 4:00 PM
- TechnicianDana
- Emergency Callout Fee$99 (Copperline Care member rate)
Dana will text you at (503) 555-0193 about 30 minutes before arrival, so you'll know exactly when to expect them. Is there a gate code or any access notes I should put on the ticket so Dana can get in easily?
CUSTOMERSorry. There is a gate code. It's pound 2719. Can you make sure Dana has that? And, could you repeat the ticket number just to double check? I just wanna be sure I've got everything right.
AGENTAbsolutely. Let me make sure I get that gate code on file for Dana. Unfortunately, I don't have a way to update the ticket after it's been created, but I'm noting it here: the gate code is #2719. When Dana texts you 30 minutes before arrival, you can give them that code, or I can note it in my system for the record. Your ticket number is CPL-58210. Let me read back everything to confirm:
- Ticket NumberCPL-58210
- Service Address1480 Larkspur Avenue, Unit 4D
- Callback Phone(503) 555-0193
- Gate Code#2719
- Visit WindowToday, 2:00 PM to 4:00 PM
- TechnicianDana
- Emergency Callout Fee$99
Does all of that look correct?
CUSTOMERYeah. That's all exactly right. Thank you so much for walking me through it. I really appreciate it. Goodbye.
AGENTYou're very welcome! You did great staying calm and handling this the right way. Dana will be there between 2 and 4 PM today, and they'll text you 30 minutes before. Keep that water main off until Dana arrives. If anything changes or you need to reach us, just call back with your ticket number CPL-58210. Take care, and hang in there!
Insurance
AGENTNorthgate Mutual claims, this is Alex. Are you somewhere safe right now?
CUSTOMERYes. I am safe. I'm standing next to my car at the cafe. Nobody is hurt.
AGENTGood to hear you're safe and that no one is hurt. I'm going to help you get this claim started.
CUSTOMEROkay.
CUSTOMERSure. Go ahead.
AGENTI'll need to verify your policy first. Can you give me your policy number and your last name?
CUSTOMERYes. My policy number is NG4471903. No. Sorry. Last one is two. My last name is Mehta.
AGENTGot it. So your policy number is NG4471902, and your last name is Mehta. Let me verify that for you.
CUSTOMERYes. That's right.
AGENTPerfect.
CUSTOMEROkay.
CUSTOMERUh-huh. Go ahead.
AGENTNow I need to gather some details about what happened. Can you tell me:
1. When did the accident happen? 2. What exactly happened? 3. Was there another vehicle involved? If so, do you have the other driver's name, their insurance company, and their license plate? 4. Did police come to the scene? If so, do you have a report number?
CUSTOMERYes. The accident happened about twenty minutes ago. I was stopped at a red light at SE 5th Avenue and Alder Street, and another car hit the back of my car. The back bumper is crushed, and the rear wheel rubs. So I cannot drive it. Yes. Another vehicle was involved. The other driver is Kevin Ludd. Insured with Pacific Shore. His plate is 7KX like X-ray, R214. Police did not come. So there is no report number.
AGENTThank you.
CUSTOMERRight.
CUSTOMEROkay.
AGENTLet me create your claim now with the information you've provided.
CUSTOMERSure.
CUSTOMERGo ahead.
AGENTYour claim has been opened.
CUSTOMERAlright. I've got the PIN number down. NGM904417. Can you tell me how much do I have to pay for this? Will I get a rental car, and is my premium going to go up? Also, I need a tow truck. And where will the car be taken?
AGENTLet me check your coverage details first.
CUSTOMEROkay.
AGENTHere's your coverage:
Collision deductible$500. That's what you'll pay out of pocket toward the repair. Rental car: You're covered for $40 per day for up to 30 days, starting from today when the claim opened. Towing: Covered up to 25 miles. As for your premium and where the car will be taken, those are questions for your adjuster, Priya Shah. She'll contact you within one business day. Now, for the tow truck—where would you like your car taken? Do you have a preferred repair shop, or would you like me to arrange the tow?
CUSTOMERBut he hit me. So I should not have to pay that deductible. Right? And I do not have a preferred repair shop. Kindly arrange the tow for me.
AGENTYou're right that the other driver hit you. However, I can only tell you what your policy covers—those are questions about fault and whether the deductible applies that your adjuster Priya Shah will sort out with you.
CUSTOMEROkay. So I will wait for Priyasha