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
#9of 654 stacks
61 / 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.
#94 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.
#328 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
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.
#507 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.
#531 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.
#150 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.
#334 of 648 on the board · best 1.9%
Voice
Human-likeness
How human the agent's voice sounds, scored by an audio model on intonation, rhythm and expressiveness. 5 = hard to tell from a person.
#160 of 659 on the board · best 4.3 / 5
Voice consistency
Share of calls where every line the agent spoke was the same speaker, with no change of speaker, gender or language voice partway. It checks who is speaking, not how they sound.
Not scored by this sweep.
#1 of 659 on the board · best 100%
Latency
Response time
The silence between the caller finishing and the agent starting to speak, measured from the recording on every turn of the call.
#288 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.
#269 of 661 on the board · best 0.45 s
Latency spikes
Turns where the response took far longer than the rest of the conversation.
#220 of 661 on the board · best 0.0 per call
By industry
| Industry | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| Healthcare | 98% | 3.73 s | — |
| Home services | 90% | 4.14 s | — |
| Insurance | 100% | 3.33 s | — |
| Customer support | 95% | 3.32 s | — |
By caller
| Caller | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| US English, quiet line | 93% | 3.38 s | — |
| Spanish-accented English, city street | 98% | 4.01 s | — |
| Indian English, busy café | 98% | 3.14 s | — |
| Southern US, flustered, crying baby | 92% | 3.48 s | — |
| Fast and interrupting, construction outside | 98% | 3.52 s | 100% |
The calls behind the numbers
Customer support
AGENTBrightwave Internet support, this is Alex. What’s going
AGENTon with your service?
CUSTOMERHi, Alex.
CUSTOMERMy internet has been down for about two hours.
CUSTOMERI work from home, so it's quite urgent.
CUSTOMERThere is a road crew digging up the street outside.
CUSTOMERCould that be the cause?
CUSTOMERI already rebooted the gateway twice and checked every cable. The internet light is blinking orange.
CUSTOMERCan you check what's happening on your end?
CUSTOMERMy account number is BW883.
CUSTOMER017.
CUSTOMERSorry, last digit is one.
AGENTI’ve pulled up your account, BW-883011. First, I checked for any known area outages or maintenance near you, and there are none reported, so the road crew hasn't disrupted the main line. I ran a line test which showed good signal to your location, but the gateway wasn't registered on the network. I sent a remote reset to your gateway, and it has successfully registered and is now back online. It may take about two minutes for your devices to fully reconnect. Could you take a look at the gateway's lights to
AGENTsee if the internet light has stabilized?
CUSTOMERYeah. One moment.
CUSTOMEROkay. The internet lights just went solid white
AGENTSolid white means your connection is fully restored and ready to go. Give your work devices just a minute or two to reconnect to the Wi-Fi. Is there anything else I can assist you with today?
CUSTOMERnow. How long until my devices reconnect? Right.
CUSTOMERThanks.
CUSTOMERSince the internet was down for two hours.
CUSTOMERCan you apply a credit to my account for the downtime?
AGENTI've applied a $10.00 service credit to your account, BW-883011, for the downtime, which will appear on your next bill. Your devices should be connecting right about now. Is there anything else I can help
AGENTyou with today?
CUSTOMERI lost two hours of work.
CUSTOMERCan you make it more than that?
AGENTI understand how disruptive that is. I was able to apply an additional $10.00 credit to your account, BW-883011, bringing the total credit to $20.00, which will appear on your next billing cycle. That is the maximum credit my system allows me to apply. Are your devices connecting properly now?
CUSTOMERYes. They're coming back online now. Thank you for your help, Alex.
AGENTThat's great
CUSTOMERGoodbye.
AGENTYou're very welcome!
AGENTHave a great rest of your day, and thanks for choosing Brightwave. Goodbye!
Healthcare
AGENTRiverbend Health Clinic, this is Alex. How can I help you
AGENTtoday?
CUSTOMERI need to reschedule my dermatology appointment next Wednesday morning.
CUSTOMERI just got assigned a work shift then, so I can only come after 3 p.m.. Can you help me move it? My name's Nate Brooks.
AGENTI can certainly help you reschedule that dermatology appointment, Nate. To get started and locate your record,
AGENTcould you please provide your date
CUSTOMERYeah, sure.
CUSTOMERMarch 17th, 1948.
CUSTOMERSorry. No. 1984.
CUSTOMERMarch 17th, 1984.
AGENTThank you for confirming your date of birth as March 17th, 1984, Nate Brooks. I see your appointment with Dr. Okafor in dermatology scheduled for next Wednesday at 9:15 a.m. Looking for openings after 3:00 p.m., Dr. Okafor has an open slot on Thursday at 4:10 p.m. Would that work for you?
CUSTOMERYeah. That's fine.
CUSTOMERThursday at 4:10 p.m.. Works.
CUSTOMERGo ahead and book it.
CUSTOMERIs there a fee for changing the appointment?
AGENTYou're all set! I've rescheduled your dermatology appointment with Dr. Okafor to Thursday at 4:10 p.m. Your new confirmation code is RVB-3M8P2. Please arrive 15 minutes early with your photo ID and insurance card. As for your question,
CUSTOMERYeah. I got.
CUSTOMERIt. Where should I park when. I come in?
AGENTThere is free patient parking located in Lot C, with the entrance on River Road. Is there anything else I can help you with today?
CUSTOMERRight. Thanks. One more thing.
CUSTOMERDoes Doctor. Okafor accept Aetna insurance?
AGENTI don't have access to specific insurance network details for Dr. Okafor. I recommend contacting our billing department or checking
CUSTOMERYeah that's fine.
CUSTOMERI'll check with my insurance then.
CUSTOMERThanks for your help. Goodbye.
AGENTYou're very welcome! Have a great day, and goodbye.
Home services
AGENTCopperline Home Services, this is Alex. What can I help
AGENTyou with?
CUSTOMERHey.
CUSTOMERI need someone out here right away. A pipe under my kitchen sink just burst, water spreading across the floor toward a power strip.
CUSTOMERIt's getting worse by the minute.
CUSTOMERI haven't shut the water off yet. Can you tell me how? Or can you send someone fast?
AGENTFirst, please stay completely clear of the water near that power strip and do not touch any cords or outlets. If your electrical panel is safely accessible without stepping in water, you can shut off the breaker for the kitchen. To stop the water immediately: 1. Look under the sink for the oval shut-off valves on the pipes and turn them clockwise until
CUSTOMERGot
AGENTGreat. Did you manage to shut it off, and are you safely away from the water? Also, what's your address or phone number so I can look
CUSTOMERit. Right.
AGENTup your account?
CUSTOMERYeah.
CUSTOMERThe water is off now, and I'm not near the power strip.
CUSTOMERMy address is 1480 Larkspur Avenue.
CUSTOMERMy phone number is (503) 555-0139. No, 0193.
CUSTOMERI moved to unit 4D last month, not. Four B.
CUSTOMEROkay.
AGENTI'm glad you're safe and the water is off. I have updated your address to Unit 4D. Because you are a Copperline Care member, your emergency callout fee is $99 instead of the standard $149. We have an emergency window open today between 2:00 PM and 4:00 PM. Do you agree to the $99
CUSTOMERNo.
AGENTI have your record pulled up and updated to Unit 4D. Because you're a Copperline Care member, your emergency callout fee is discounted to $99. We have an emergency arrival window today between 2:00 PM and 4:00 PM. Does that work for you, and do you agree to the $99 fee to book that slot? Also, are there any gate codes or special instructions to get into your building?
CUSTOMERNeed someone sooner.
CUSTOMERIs there any way someone can come before 2 p.m.? And yes, I agree to the $99 fee.
CUSTOMERThe gate code is pound 2719.
AGENTI completely understand, but 2:00 PM to 4:00 PM is our
CUSTOMEROkay.
CUSTOMERAll right. Just book the. Earliest emergency. Window, then. Who's coming out? And how will I know they're on the way?
CUSTOMERI need the ticket number two.
AGENTYou are all booked. Technician Dana will be coming out today between 2:00 PM and 4:00 PM, and she will
CUSTOMERRight.
CUSTOMERThat.
AGENTtext
CUSTOMERWorks. What's the ticket number?
CUSTOMERWill Dana text me at (540) 328-8007?
AGENTYes, I have updated your callback number so Dana will text you at (540) 328-8007 about 30 minutes before arriving. Your ticket number is CPL-58210.
CUSTOMERYeah, that's my number.
CUSTOMERJust need the ticket. Number, then I'll let you go.
AGENTYour ticket number is CPL-58210. To confirm all the details: - Service Address: 1480
CUSTOMERRight.
CUSTOMERGood. I've got the ticket number. Thanks for your help. Goodbye.
AGENTYou're very welcome!
AGENTStay safe, and Dana will see you this afternoon. Goodbye!
Insurance
AGENTNorthgate Mutual claims, this is Alex. Are you somewhere
AGENTsafe right now?
CUSTOMERYeah, I think so.
CUSTOMERI'm at home.
CUSTOMERThe cars in the driveway, but I can't drive it.
CUSTOMERSorry. My baby's crying in the background, so I'm a little flustered. Is it okay that I'm not at the accident scene anymore?
AGENTTake your time, and don't worry about the baby at all. Yes, it is completely okay that you're home. First, was anyone hurt or injured in the accident, including you or your baby? If anyone needs medical attention,
AGENTplease call 911 right away.
CUSTOMERThank you for saying that. I was worried about leaving the scene, but nobody was hurt. Not me or the baby.
CUSTOMERWe're both okay.
CUSTOMERUm.
CUSTOMERI guess I just need to figure out what to do next.
CUSTOMERSorry. Is there anything else I should tell you?
AGENTI'm so glad to hear that you and your baby are okay. That's the most important thing. We will take care of everything step by step. To start, could you please share your policy number and your last name so I can look up your account?
CUSTOMERSure. Uh, my policy number is NG4471903.
CUSTOMERNo, sorry, last one is two.
CUSTOMERAnd my last name is Carter. Is that what you need?
AGENTThank you, Ms. Carter.
CUSTOMERHello.
CUSTOMERUm.
CUSTOMERSorry.
AGENTI have your policy
CUSTOMERActually.
CUSTOMEROkay. Uh. Is it showing up? Right.
AGENTTake
CUSTOMERI just want to make sure you've got the right account for me. What do you need next?
AGENTYes, I have your policy verified: policy number N-G-4-4-7-1-9-0-2 for your 2021 Honda CR-V. Next, could you tell me: 1. When and where did the accident happen? 2. What happened? 3. Did the police attend the scene? 4. Do you have the other driver’s name, insurance company, and license plate?
CUSTOMERAll right. Um.
AGENTTake your time. Whenever you're ready.
CUSTOMERSo. The accident happened about 20 minutes ago.
CUSTOMERThank you, I. Appreciate that.
CUSTOMEROh, it was at SW fifth Avenue and Alder Street.
CUSTOMERI was stopped at a red light and another car hit the back of mine.
CUSTOMERThe back bumper was crushed and the rear wheel is rubbing, so I can't drive
AGENTI'm sorry you had to go through
CUSTOMERit. The police didn't come. Nobody was hurt. The
CUSTOMERother. Thank
AGENTI understand.
CUSTOMERyou. All right. Um.
AGENTthat. Did you happen to get the other driver's name, their insurance company,
AGENTor their license plate?
CUSTOMERYeah, I did.
CUSTOMERThe other. Driver's name is Kevin Lund. He's insured with Pacific Shore.
CUSTOMERHis license plate is 7KX, like X-ray of. R two one. Four.
CUSTOMERIs that enough info for
AGENTThank you, Ms. Carter. I have opened your claim. To confirm the details: - Policy number: NG4471902 - Other driver's license plate: 7KXR214 - Claim number: NGM-904417 - Assigned adjuster: Priya Shah, who will contact you within one business day. Regarding your coverage: - Collision deductible: $500 - Rental reimbursement: $40 per day for up to 30 days, starting today - Towing: Covered up to 25 miles Since your car is in the driveway and cannot be driven safely, would you like me to arrange a tow truck for you now? If so, what is your home address and the best callback phone number?
CUSTOMERyou?
CUSTOMERYes, please.
CUSTOMERI really. A tow truck right
CUSTOMERnow. Sorry. I just want to make sure. Where
AGENTCould you please provide your home
CUSTOMERwill they.
AGENTaddress
AGENTCould you give
CUSTOMEROh. Yeah. Sorry.
AGENTme
AGENTwhere the car is,
CUSTOMERI'm a
AGENTand the best phone number for the tow driver to reach you? Then I will dispatch them right away.little scattered. My address is 1422 Elmwood Street, Apartment 4B. Wait, the car is parked in the driveway downstairs, so it's accessible.And my cell number is 503-555-0184.
CUSTOMERlittle flustered. It's.
CUSTOMERThink so? Please tell me that's the right number. I just want to make. Sure the tow driver can reach
CUSTOMERme. Is that okay?
AGENTI have dispatched Eastside Towing to 1422 Elmwood Street. They are expected to arrive in about 45 minutes and will tow the car to our partner repair shop at 9th and Morrison. The driver will call you at 503-555-0184. To recap your key details: - Policy number: NG4471902 - Claim number: NGM-904417 - Other vehicle license plate: 7KXR214 Adjuster Priya Shah will be in touch within one business day. Is there anything else I can assist you with right now?
CUSTOMERSorry. I just want to double
AGENTYes, that is completely fine! You gave me 503-555-0184, and that is the exact number the driver from Eastside Towing will call when they arrive. You don't have to worry about a thing—take care of yourself and your baby, and everything is taken care of
CUSTOMERcheck. About the deductible. You said $500.
AGENThere.