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
#67of 654 stacks
56 / 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.
#78 of 661 on the board · best 99%
Flow adherence
How closely the agent followed the intended conversation flow, judged per call. 5 = fully followed.
#1 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.
#360 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.
#447 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.
#313 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.
#449 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.
#404 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.
#537 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.
#566 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.
#463 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.
#136 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 | 98% | 4.20 s | — |
| Home services | 97% | 3.99 s | — |
| Insurance | 98% | 4.27 s | — |
| Customer support | 91% | 4.14 s | — |
By caller
| Caller | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| US English, quiet line | 98% | 4.26 s | — |
| Spanish-accented English, city street | 95% | 3.87 s | — |
| Indian English, busy café | 98% | 3.81 s | — |
| Southern US, flustered, crying baby | 100% | 4.63 s | — |
| Fast and interrupting, construction outside | 89% | 4.51 s | 100% |
The calls behind the numbers
Customer support
AGENTBrightwave Internet support, this is Alex. What’s going on with your service?
CUSTOMERHi Alex, my internet is down for the last two hours. I'm working from home, but there's a road crew digging up the street outside. Already rebooted the gateway twice. Checked every cable, but the internet light is blinking orange. Can you check if the road work is the cause? My account number is BW883017. Sorry, last digit is 1.
AGENTThanks. I'll use the corrected account number, BW883011, and look it up now.
AGENTFound
CUSTOMERYeah, that's right.
CUSTOMEROkay, so can you tell me if the road crew outside is causing the outage? I need to get back online quickly.
AGENTI haven't checked for an outage yet, so I'll do that now.
CUSTOMERSure.
AGENTThe remote reset worked. Your gateway now shows as registered and online on account BW-883011. Your devices may take about two minutes to reconnect, and the internet light should go from blinking orange to solid. Please check whether you're back online. If the light stays orange or you still have no connection after a few minutes, let me know and I'll schedule a technician. Since you've been down for two hours, I can also
CUSTOMERYeah, the light just went solid white now. How long until my devices actually reconnect to the internet? And I'd like a credit for the downtime, please. I lost two hours of work.
AGENTSolid white means you're connected. Devices usually reconnect within about two minutes of the reset. If one doesn't, restart that device's Wi-Fi or reboot it. I'll apply the credit now.
AGENTI've applied a $10.00 service credit to account BW-883011 for the two hours of downtime. It will show up on your next bill. If anything drops again, contact us and we'll take another look. Is there anything else I can help with?
CUSTOMERCan you make it more than that? I lost two hours of work, so I'm hoping for a bit extra if possible.
AGENTI understand, two hours of lost work is frustrating. I can't increase the credit, though. The tool I have applies a set downtime credit, and I can't change the amount. The $10.00 is already on account BW-883011 and will show up on your next
CUSTOMERThanks for watching!
CUSTOMERAlright, that's fine. Thank you for your help, Alex. Goodbye.
AGENTYou're welcome. To recap, your gateway is back online and a $10.00 credit will appear on your next bill for account BW-883011. If the orange light comes back, contact us and we'll send a technician. Goodbye, and good luck with the rest of your workday.
Healthcare
AGENTRiverbend Health Clinic, this is Alex. How can I help you today?
CUSTOMERuh-huh Hey, I need to reschedule my dermatology appointment next Wednesday morning. I just got assigned a work shift then, so I can't make it. Can you move it to sometime after 3pm?
AGENTI can help with that. First I need to verify your identity. Can I have your full name and date of birth?
CUSTOMERYeah, Nate Brooks. March 17th, 1948. Sorry, no, 1984. March 17th, 1984.
AGENTThanks, Nate. I'll use 1984.
CUSTOMERRight, that's correct. Go ahead.
AGENTLet me check Dr. Okafor's openings.
AGENTI also need to look at availability.
CUSTOMERAll right, that's fine. Just needs something after 3 p.m.
AGENTYour current appointment is with Dr. Okafor next Wednesday at 9:15
CUSTOMERYeah.
CUSTOMERYep. That's the one I need to move only available after 3 p.m.
AGENTDr. Okafor has two openings after 3 p.m.:
- Tuesday at 345 p.m.
- Thursday at 410 p.m. Neither is on Wednesday,
CUSTOMERYeah, got it. Just tell me the earliest slot after 3 p.m. I'm short on time.
AGENTThe earliest is Tuesday at 3:45 p.m. Do you want me to move
CUSTOMERUh-huh.
CUSTOMERNo, Tuesdays don't work. I'd pick up my daughter then. What about Friday?
AGENTFriday's only opening with Dr. Okafor is at 11:30 a.m., which is before your 3 p.m. cutoff, so it doesn't fit. The only after-3 p.m. slot left is Thursday at
CUSTOMERAll right. Thursday at 4 10 p.m. then. Book me for that!
AGENTLet me move that now.
AGENTYou're all set,
CUSTOMEROkay, go ahead
CUSTOMERAlright, is there any fee for changing the appointment?
AGENTYour dermatology appointment with Dr. Okafor is now Thursday at 4:10 p.m., at Main Campus, Building B. Your confirmation code is R-V-B-3-M-8-P-2
CUSTOMERYeah, guys, is there a fee for switching the appointment, and where should I park?
AGENTOn the fee: changes made more than 24 hours before the appointment are free, and later changes carry a $40
CUSTOMERYep, I know that part. Just tell me, is there a fee or not, and what's the best place to park? And does Dr. Okafor take Aetna insurance?
AGENTfee.
AGENTFee: Your original appointment was next Wednesday at 9:15 a.m., so this change was made well over 24 hours ahead. That means there's no fee. Parking: Free patient parking is in Lot C, with the entrance on River Road. Aetna: I don't have any insurance or network information, so I can't say whether Dr. Okafor
CUSTOMERThank you for watching!
AGENTYou're welcome,
CUSTOMERGot it on the...
CUSTOMERThanks for your help. Goodbye!
AGENTGoodbye,
CUSTOMERThanks for your help. Goodbye!
AGENTGoodbye, Nate! A quick recap of the essentials,
AGENTsince a few of my earlier messages got cut off:
- New appointmentThursday at 4:10 p.m. with Dr. Okafor, Main Campus, Building B
- Confirmation codeR-V-B-3-M-8-P-2
- ArrivalCome 15 minutes early with photo ID and your insurance card
- FeeNone, since the change was made more than 24 hours ahead
- ParkingFree in Lot C, entrance on River Road
- AetnaI don't have insurance network details, so please check with the clinic's front desk or Aetna directly
Take care!
Home services
AGENTCopperline Home Services, this is Alex. What can I help you with?
CUSTOMERHi, uh, yes. I have a pipe under my kitchen sink that just burst. There's water spreading all over the floor, and it's getting close to a power strip. I need someone to come right away.
AGENTSafety first. Please do these right now:
1. Stay away from the power strip and don't touch it or anything plugged into it while water is near it. 2. If you can do it safely without stepping in water, turn off power to the kitchen at your breaker panel. 3. Shut off the water. Under the sink, turn the small valves clockwise. If the water is still flowing, shut off the main valve, usually where the water line enters your home or near the water meter. Is anyone in danger or hurt? And are you able to get the water shut off? While you do that, what's the best phone number or street address for me to look up your account?
CUSTOMEROkay, I'm going to do that now. Turning off the water. Yes, it's stopping now. No one is hurt. Nobody is in danger right now. My address is 1480 Larkspur Avenue. The best number to reach me is 503 555 0139. No, sorry, 0193.
AGENTGlad the water is stopping. I'll look up your account now.
AGENTLet me finish pulling up your account.
AGENTI found your account. I have you at 1480 Larkspur Avenue, unit 4B,
CUSTOMERAh, yeah. Actually, I moved last month. It's still 1480 Larkspur Avenue, but now I'm in Unit 4D, not 4B.
AGENTThanks for catching that, I'll use Unit 4D. Do you need a gate code or any special access instructions for the technician?
CUSTOMERYes, you'll need the gate code. It's pound 2719.
AGENTGot it. I'll open the request now.
AGENTYour request
CUSTOMEROkay.
CUSTOMERBefore you send anyone, can you tell me how much the visit will cost?
AGENTYour request is open, and nothing is booked yet. The earliest emergency window is today from 2:00 PM to 4:00 PM. Your account shows you as a Copperline Care member,
CUSTOMEROkay, so as a Copperline care member, is there a charge for this emergency visit or is it covered? And can anyone come before 2pm? The water is pretty bad.
AGENTAs a Copperline Care member, the emergency callout fee is $99. It isn't waived, and it's the member rate, down from the standard $149. I don't have any information on what repairs or parts would cost, only the callout fee. The earliest emergency window is today from 2:00 PM to 4:00 PM. Nothing earlier is open. The only other option is tomorrow morning, and that's a standard visit, so today is the soonest. Since the water is off, the main thing until then is to keep the area contained. Use towels or a mop to keep water away from the power strip and outlets. If you can, unplug the strip only if your hands are dry and you're not standing in water, or leave the breaker off. Would you like me to book the 2:00 to 4:00 PM window today at the $99 member callout fee?
CUSTOMERYes, go ahead and book the 2 p.m. to 4 p.m. window today for me. Who will be coming and how will I know when they're on the way? Also, can you give me the ticket number for this?
AGENTBooking that now.
AGENTYou're all booked, and your ticket number is CPL-58210. Dana will be your technician, arriving today between 2:00 and 4:00 PM, and she'll text you at 503 555 0193 about 30 minutes before she gets there. Let me read it back: 1480 Larkspur Avenue, Unit 4D, gate code pound 2719,
CUSTOMERYes, that's right. Thank you for confirming. I'll watch for Dana's text and I have the ticket number CPL58210. Thanks for your help. Goodbye.
AGENTYou're welcome. Keep the water off and the power strip clear of any water until Dana arrives, and the $99 member callout fee is what you'll pay for the visit. Take care, goodbye.
Insurance
AGENTNorthgate Mutual claims, this is Alex. Are you somewhere safe right now?
CUSTOMERuh-huh
CUSTOMERYeah, I'm fine. I just need to get this sorted fast. My car got rear-ended about 20 minutes ago. I'm standing next to it, but it's not drivable. Can we get started?
AGENTYes, we can get started, and I'm glad you're safe. Before anything else, I want to double-check that nobody involved is hurt, including you, any passengers, and the other driver.
CUSTOMERGot it!
CUSTOMERRight, nobody's hurt, just me here. And the other driver's fine too. Can we move on? I need to get a tow and start the claim.
AGENTThanks for confirming. To verify your policy, I need your policy number and your last name. Also, tell me exactly where the car is right now (street address or nearest intersection) so I can set up the tow, along with a phone number where the tow driver can reach you.
CUSTOMERGot it!
CUSTOMERAlright, I'm Nate Brooks. Policy number is NG4471903. No, sorry, last one is 2. I'm at SC 5th Avenue and Alder Street. My phone number is 907-759-4456. Let's move forward.
AGENTThank you, Nate. You corrected the last digit, so I'll use NG4471902. I'll verify the policy now.
CUSTOMERNo, hang on, last digit is 2, but you said 9-0-2, not 9-9-0-2, it's N-G-4-4-7-1-9-0-2. Yep, that's right. Go ahead.
AGENTYour policy includes towing, so I'll dispatch the truck now.
CUSTOMERMhm. Good, that's what I need. Where's the car going to be taken? I also need to know about a rental. Do I get one while mine's being repaired?
AGENTHere are the details:
TowEastside Towing is on the way to SC 5th Avenue and Alder Street, with an estimated arrival of about 45 minutes. They'll take your CR-V to our partner repair shop at 9th
CUSTOMERYep, got it. That's clear. Now, do I get a rental car while mine's in the shop? And how much am I going to have to pay for all this?
AGENTYes, your policy includes rental reimbursement. It pays $40 per day for up to 30 days, starting from the day the claim is opened. If a rental costs more than $40 a day, you would cover the difference. Your collision deductible is $500. I can't tell you the total repair cost or how the claim will be settled, since
CUSTOMERuh-huh
CUSTOMERBut he hit me, so I shouldn't have to pay that deductible, right? He rear-ended me at a red light! Will my premium go up, or stay the same?
AGENTI understand why you're asking, Nate. I can't tell you how fault will be decided or whether your deductible will be recovered. That's the adjuster's call after
CUSTOMERYeah, I get it. Look, just tell me if my premium's gonna change and what happens next.
AGENTI can't tell you whether your premium will change. Nothing in your policy lookup covers that, and I don't want to guess. Your adjuster is the right person to ask once
CUSTOMERAlright, got it. Just give me the plane number so I can keep track.
AGENTI think you mean the claim number. I don't have one yet, because I still need to open the claim. I'll need a few details first:
1. The other driver's name, their insurer,
CUSTOMERThat's fine. The other driver's name is Kevin Lunn, insured with Pacific Shore. Plate is 7, K, X like X-ray, R, 2, 1, 4. Go ahead and open the claims.
AGENTI have the plate as 7-K-X-R-2-1-4. Two quick things I still need before I
CUSTOMERokay Yep, that's correct. What else do you need?
AGENTThanks, Nate. I have two things left to ask:
1. Did police come to the scene, and if so, do you have a report number? 2. Roughly what time did the accident happen? You said about 20 minutes ago,
CUSTOMERNope.
AGENTThanks.
CUSTOMERPolice came, so there's no report. What's the other thing?
AGENTI want to make sure I record this correctly. Do you mean police did come to
CUSTOMERGot it!
CUSTOMERNo, they did not come. Nobody showed up, so there's no report. Let's move on.
AGENTThanks, Nate. I'll note that police did not attend and there's no report. The other thing I need is the time of the accident. You said it was about 20 minutes ago. Do you know
CUSTOMERright that's correct what else do you need
AGENTI don't have the exact time of the accident, and I don't want to guess at it. What
CUSTOMERyeah
AGENTtime
CUSTOMERIt was about 20 minutes ago. I was stopped at the red light at SE 5th and Alder. That's all I remember. Is that enough?
AGENTThat's enough, Nate. One correction to check: earlier I had the location as "SC 5th Avenue and Alder Street," and you just said "SE
CUSTOMERAlright, just tell me what needs correcting, then I need the claim number and I'll be done.
AGENTThe only thing to correct is the location. Earlier I took it as "SC 5th Avenue and Alder Street," and just now you said "SE 5th and Alder." Which one is right: SC or SE? The tow is already dispatched to the "SC" address. If it should be SE, I'll need to let the tow company know so the driver goes to the right place.
CUSTOMERIt's S-E, not S-C. See 5th Avenue and Alder Street. Please update the tow company so they come to the right spot. Can you confirm?
AGENTThanks, Nate. The location is SE 5th Avenue and Alder Street. I don't have a way to change the existing tow
CUSTOMERMm-hmm.
CUSTOMERAlright, just let the tow driver know as soon as you can. Now, can you give me the claim number so I can track this? Then I'm done.
AGENTYour claim is open. The claim number is NGM-904417: N-G-M, 9-0-4-4-1-7.
CUSTOMERRight.
AGENTI owe you a
CUSTOMERyep I've got it thanks for your help goodbye
AGENTBefore you go, Nate, there's
AGENTone thing I haven't done right. The tow was dispatched to "SC 5th Avenue and Alder Street," and I can't change an existing dispatch. If I don't fix it, the driver could go to the wrong spot. I'll send a new request now with the correct location, SE 5th Avenue and Alder Street, and your number.