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
#36of 654 stacks
57 / 100
Leader AssemblyAI Universal-3.6 Pro + OpenAI GPT-6.1 Sol + Cartesia Sonic-3.6, 68 / 100
Where it ranks
Task completion
Task completion
How much of the caller’s task the agent completed: the share of the scenario’s checks a judge model found met in the transcript, averaged over calls. Before v1.3, the share of calls judged 4 or better out of 5.
#616 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.
#3 of 6 on the board · best 100%
Incorrect interruptions
How often the agent interrupted the caller when it should have kept listening.
#1 of 661 on the board · best 0%
Overtalk
Share of the conversation where both sides were speaking over each other.
#656 of 661 on the board · best 0.0%
Agent self-cutoffs
Times the agent cut its own sentence off mid-word.
#1 of 661 on the board · best 0.0 per call
Understanding & effort
Caller effort
How hard the caller had to work: repeating themselves, correcting, re-explaining. 1 = effortless.
Not scored by this sweep.
Repeated-info loops
Times per call the agent re-asked for something the caller had already given.
#1 of 493 on the board · best 0.0 per call
Voice
Human-likeness
How human the agent's voice sounds, scored by an audio model on intonation, rhythm and expressiveness. 5 = hard to tell from a person.
#2 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.
#564 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.
#59 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.
#510 of 661 on the board · best 0.45 s
Latency spikes
Turns where the response took far longer than the rest of the conversation.
#55 of 661 on the board · best 0.0 per call
By industry
| Industry | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| Healthcare | 80% | 2.77 s | 92% |
| Home services | 85% | 2.60 s | 92% |
| Insurance | 92% | 2.74 s | 90% |
| Customer support | 87% | 2.89 s | 88% |
By caller
| Caller | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| US English, quiet line | 85% | 2.92 s | — |
| Spanish-accented English, city street | 87% | 2.88 s | — |
| Indian English, busy café | 89% | 2.79 s | — |
| Southern US, flustered, crying baby | 84% | 2.72 s | — |
| Fast and interrupting, construction outside | 84% | 2.39 s | 91% |
The calls behind the numbers
Customer support
AGENTBrightwave Internet support, this is Alex. What’s going on with your service?
CUSTOMERHi Alex. I'm calling from a cafe because my home internet has been down for about two hours now. There is a road crew digging up the street outside. So I'm wondering if that's causing this issue. I work from home, so it's urgent. I already rebooted the gateway twice and checked every cables. The internet light is blinking
AGENTMm.
CUSTOMERorange. My account number is
CUSTOMERBW-88
CUSTOMER-30-17
CUSTOMER, sorry, last digit is one. Can you check what's going on
CUSTOMER, please
AGENTMm. I’m on it.
CUSTOMEROkay
AGENTOkay, I’ve got your account, BW eighty-eight, thirty, seventeen, one. Thanks for catching that last digit. I checked your area, and there’s no outage reported, and the line to your home looks good, but your gateway had dropped off our system. I just sent a remote reset, and it’s back online now. When you’re home, the internet light should go solid within a couple of minutes, and your devices should reconnect. Can you check that when you get in?
CUSTOMERRight. The light just went solid white now. How long until my devices reconnect
AGENTGreat. Usually they’ll come back within another minute or two. If they don’t, just toggle Wi-Fi off and on, on one device and see if it reconnects. If it's still not working once you’re home and checked, give us a call back and we’ll take the next step.
CUSTOMEROkay, one more thing. I lost two hours of work because of this. Can you give me a credit for the downtime, please
AGENTSure, I’ll see what I can do.
AGENTI can do that for you. I’ve added a ten dollar credit to your account BW eighty-eight, thirty, seventeen, one, for the downtime, and you’ll see it on your next bill. Anything
CUSTOMERAlright
AGENTelse you want me to check while I’ve got your account open?
CUSTOMERYeah. I lost two hours of work. Can you make the credit more than ten dollars
AGENTMm. I hear you. Hang on. I’ll see if I can adjust that. I totally get why you’re asking, especially losing work time. I’ve added the standard ten dollar credit I’m allowed to apply for this kind of downtime on account BW eighty-eight, thirty, seventeen, one. I can’t bump it higher myself, but if you want to request a larger credit, I can note what happened on your account and you’d be able to follow up through your online account messages or by asking a supervisor to review it. Do you want me to add that note now?
CUSTOMERYes, kindly add the note about the
CUSTOMERdowntime and the lost work. That would be helpful. And that's all I needed. Thanks
AGENTI can do that, yeah. Got it. I’ll add that note to account BW eighty-eight, thirty, seventeen, one, about today’s two-hour downtime and the work impact. You’re all set. Thanks for calling Brightwave, and I hope your connection stays solid.
CUSTOMERGoodbye
Healthcare
AGENTRiverbend
AGENTHealth Clinic, this is Alex. How can I help you today?
CUSTOMERHi Alex. I need to move my dermatology appointment next Wednesday morning. I've got a work shift then, so I need something after 3 p.m .Can we do that
AGENTHmm. Let's see. I can help with that. First, I need to verify your record. Please tell me your full name and your date of birth.
CUSTOMERMay birth . March 17th, 1948. Sorry, no .1984
CUSTOMER. March 17th, 1984
AGENTOkay, one sec. Got it
AGENT. Thanks for clarifying the year. I have your date of birth as March 17th, 1984. Now, could you tell me your full name as it appears on your records?
CUSTOMERAlright. It's Nate Brooks. That should be how it's listed
AGENTHmm. Okay, checking that now.
AGENTThanks, Nate. I have you verified. I see your dermatology appointment with Dr. Okafor next Wednesday morning. There aren't any after 3 openings that day, but the next ones are Tuesday at 3:45 or Thursday at 4:10, both with Dr. Okafor. Which of those would you prefer?
CUSTOMERTuesday doesn't work after all. I pick up my daughter then. What about Friday? Any slots after 3 p.m
AGENTOkay, let's take a look.
AGENTI checked Friday. No luck after 3 with Dr. Okafor. The latest I can do with him is Thursday at 4:10. If that doesn't fit, I can see if another dermatologist has anything after 3 on Friday. Want me to check?
CUSTOMERYeah
CUSTOMER. No, Thursday at four ten works. Let's book that
AGENTAlright, hang on. I'm taking care of that now. Got it. You're now moved to Thursday at 4:10 p.m. with Dr. Okafor. That's at Main Campus,
CUSTOMERYep, that's fine. I get it. Thursday at four ten. Just need to know if there's a fee for changing the appointment. Yep ,got it. Is there a fee for changing the appointment
AGENTGood question. Checking that. No fee for this change. We only charge if it's within 24 hours of the appointment.
CUSTOMEROkay. Where should I park for this appointment
AGENTGreat question. One second.
AGENTFor the main campus, free patient parking is in Lot C. The entrance is off of River Road.
CUSTOMERAlright, Lot C off River Road. Does Doctor Okafor accept Aetna insurance
AGENTOne moment. I'm checking that now. He does. Yep. Just make sure you bring your insurance card with you.
CUSTOMERGot it. Good. That's what I needed. Can you read back the new time and give me the confirmation code for Thursday, just to be sure
AGENTSure. One second. Absolutely. You're scheduled for Thursday at 4:10 p.m. with Dr. Okafor in dermatology at the main campus. Your confirmation code is R V B 3 M 8 P 2. Please arrive 15 minutes early with your photo ID and Aetna insurance card.
CUSTOMERGot it
CUSTOMERMm-hmm
CUSTOMERAlright, I'll be there. Thanks for your help. Goodbye
AGENTNo problem. Take care, Nate.
Home services
AGENTCopperline
AGENTHome Services, this is Alex. What can I help you with?
CUSTOMERHi
CUSTOMER, u-yeah, I need help. A pipe under my kitchen sink just burst. Water is spreading across the floor and it's heading toward a power strip. I haven't shut the water off yet. Can you send someone out right away
AGENTHmm.
AGENTThat is urgent. Okay. Let me see what I can do.
AGENTI’m really glad you called. Yes, we can get someone out. But first we need to keep you safe and slow the water. Okay, please stay away from that power strip and any outlets the water might reach. If you can do so safely, unplug the power strip by pulling the plug from the wall without stepping in the water. If any part of the outlet area is wet, do not touch it, just move away. Next, we need to shut off the water. Do you know where your main water shutoff is, or is there a shutoff valve right under the sink you can turn clockwise? Tell me where you are in the house right now, and whether you can reach either of those safely.
CUSTOMEROoh .Yeah, I'm in the kitchen, right next to the sink. I see the va
AGENTOkay.
AGENTMm-hmm.
CUSTOMERAlright, I'm turning the valve now
CUSTOMER. Yeah, okay. The water has stopped
AGENTOkay, that's great. Well done. Please keep clear of that power strip for now. And if you've got a dry pair of shoes on, that's good. Yep, or just stay on a dry area. Is the water still spreading?
CUSTOMERNo, it's stopped now. The water isn't spreading anymore
AGENTOkay, perfect. That helps a lot. Let's get your details so I can dispatch someone. Are you an existing Copperline customer, or is this your first time with us?
CUSTOMERI'm an existing customer. I moved last month, same building, just from unit four B to four D
AGENTOkay, tracking, unit change.
AGENTGreat, thanks. I have you listed as a Copperline Care member at 1480 Larkspur Avenue, unit 4B, and you said you've moved to 4D, so I will update that. With your membership, the emergency call-out fee is 99 dollars, which you can pay by card or check on completion, and any repair work is quoted on site before we proceed. The earliest emergency arrival window I can offer is today between 2 and 4 p.m. Can I confirm the service address as 1480 Larkspur Avenue, unit 4D, and get the best phone number to reach you and any gate codes as well?
CUSTOMERRight
CUSTOMERYes, that's right. The address is 1480 Larkspur Avenue, unit four D
AGENT'kay.
CUSTOMERMy phone number is five zero three, five five five, zero one three nine No, zero one nine three
AGENTMm-hmm.
AGENTOkay.
CUSTOMERAnd the gate code
CUSTOMERis pound two seven one nine. Oh, I wanted to ask, can anyone come before two? It's pretty urgent
AGENTHmm. Checking for earlier availability. 'Kay.
AGENTAbsolutely hear you. This is urgent. Thanks for that info. I've opened an emergency ticket for you. The number is CPL 58210. That's for 1480 Larkspur Avenue, unit 4D, gate code pound 2719, callback 503-555-0193. I've secured the earliest emergency window we're showing today between 2 and 4 p.m. And I'm not seeing anything sooner than 2 for an emergency plumber today. The good news is the water's off, and while you're waiting, you can mop up or use towels to soak up standing water, especially near any outlets or cords if you feel safe doing that. Your emergency Copperline Care call-out fee is 99 dollars, payable by card or check on completion, and the tech will quote any repair costs on-site. Does that all sound okay?
CUSTOMERYes, that's exactly right. That works for me. Ooh, who will be coming, and how will I know when they're on the way
AGENTGood question. Okay, just checking.
AGENTYou're all set. Thank you for confirming. Here's what'll happen next. We'll assign the first available emergency plumber for your 2 to 4 p.m. window. They'll call or text you at 503-555-0193 when they're on the way, and you'll see a Copperline caller ID. If anything changes on your end before then, like the leak starts again, you can give us a call and mention ticket CPL 58210. While you wait, keep the water off and stay clear of any outlets or cords near damp areas if you can.
CUSTOMEROkay, I will. Thank you for your help
CUSTOMER. Goodbye
AGENT'Bye.
Insurance
AGENTNorthgate Mutual claims. This is Alex. Are you somewhere safe right now?
CUSTOMERYeah, I'm safe. I'm standing next to my car. Can we get started? I really need to get this sorted
CUSTOMERquickly
AGENTI'm glad you're safe. Before we get into details, is anyone injured or in danger right now?
CUSTOMERNo, no one's hurt. There's no danger. It's just the car. Rear bumper's crushed and the wheel's rubbing
AGENTGot it. Thanks for confirming. Let's verify your policy.
CUSTOMERNo, I can't drive it. Can we go ahead
AGENTYes. Let’s go ahead. Could you give me your policy number so I can pull it up?
CUSTOMERYeah, it's
CUSTOMERNG 447-1903. No, sorry. Last one is 2
AGENTOkay, just a second.
AGENTI have NG 447-1902. To make sure I'm in the right file, could you please spell your last name for me letter by letter?
CUSTOMERSure, it's Brooks. That's B-R-O-O-K-S
AGENTThanks. Checking now.
AGENTThank you, Ms. Brooks. I have your policy confirmed for your 2021 Honda CR-V. Great. Now, when did the accident happen, and where are you right now?
CUSTOMERRight
CUSTOMERThe accident was about 20 minutes ago. I'm at SE 5th Avenue and Alder Street, still by the car
AGENTOkay. Could you tell me what happened, in your own words?
CUSTOMERYeah, it was pretty simple. Another car hit the back of my car while I was stopped at a red light. My rear bumper is crushed, and the rear wheel is rubbing, so I can't drive it. No one's hurt, and the police didn't come out
AGENTThanks. That's clear. Now, what about the other driver? Do you have their name, insurer, and license plate?
CUSTOMERYeah, it's Kevin Lund. He's insured with Pacific Shore. His plate is 7KXR 214 . X like X-ray
AGENTAlright, Kevin Lund, Pacific Shore, plate 7 K X R 214. And you said the police didn't respond, correct?
CUSTOMERThat's right. They didn't come
AGENTOkay, let me get the claim started. One moment.
AGENTAlright. Thank you. Yes, I'm here. Your claim number is NGM-904417. NGM nine zero four four one seven. Your adjuster is Your adjuster is Priya Shah, and you'll hear from her within one business day. You mentioned your car can't be driven, so I can arrange a tow. Where exactly is the car for pickup, and what's the best number for the driver to call?
CUSTOMERHello
CUSTOMEROkay
CUSTOMERR
CUSTOMERYeah
CUSTOMER. Yeah, I need a tow truck right away. Where will the car be taken
AGENTChecking that for you.
AGENTWe can get a tow started right away. Your policy covers towing up to 25 miles. Yes, the tow driver will ask where you'd like the car taken. You can choose your home or a shop you trust. For now, can you confirm the exact corner at Thank you. What's the exact spot they're picking up from, and the best phone number for the driver to reach you?
CUSTOMERHello
CUSTOMERRight
CUSTOMERMhm
CUSTOMER. Yeah. SE 5th Av
AGENTMm-hmm.
CUSTOMERUh-huh
CUSTOMERSE 5th Avenue and Alder Street. And my phone is 907 -3 0 8-4 3 8 0
AGENTThanks. Arranging the tow now.
AGENTOkay. I’ve set the pickup at Southeast Fifth and Alder.
CUSTOMERRight. So, can you tell me how much I have to pay? And do I get a rental car while mine's in the shop
AGENTGood questions. Let me look that up on your policy. I’ve got that. You have a 500 dollar collision deductible. Yes, you do have rental coverage. Up to 40 dollars per day, for up to 30 days, Good. Your deductible is 500 dollars for collision. Your adjuster, Priya Shah, can walk you through how the rental works and confirm everything with you. Is there anything else you’re worried about paying for?
CUSTOMEROkay, can you check on the ren
CUSTOMERGood. That's right Now, what about my deductible
CUSTOMEROkay
CUSTOMERWait, but he
AGENTGo ahead.
CUSTOMERGot it
AGENTIs there anything else you’d like me to go over before we wrap up?
CUSTOMERYeah, is my premium going to go up because of this
AGENTLet me check what I can.
AGENTThat’s an important question, and it really depends on how the claim is handled. That’s the main piece for today. You’re all set for the tow and the claim. And when you speak with - You’re welcome, and I’m glad you’re safe. Your claim number again is NGM-904417,
CUSTOMERRight. So, is there anything else I need to know about the process ? Am I set
CUSTOMERNo, that covers it. Thanks for your help
CUSTOMERYeah