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
#428of 654 stacks
46 / 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.
#243 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.
#1 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.
#417 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.
#353 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.
#514 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.
#592 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.
#494 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.
#12 of 661 on the board · best 0.45 s
Latency spikes
Turns where the response took far longer than the rest of the conversation.
#391 of 661 on the board · best 0.0 per call
By industry
| Industry | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| Healthcare | 91% | 4.43 s | — |
| Home services | 87% | 4.83 s | — |
| Insurance | 97% | 4.20 s | — |
| Customer support | 98% | 4.21 s | — |
By caller
| Caller | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| US English, quiet line | 94% | 5.18 s | — |
| Spanish-accented English, city street | 98% | 4.40 s | — |
| Indian English, busy café | 88% | 4.05 s | — |
| Southern US, flustered, crying baby | 96% | 4.85 s | — |
| Fast and interrupting, construction outside | 90% | 3.98 s | 100% |
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 two hours. There is a road crew digging up the street outside, so I'm wondering if that could be the cause. I already rebooted the gateway twice and checked every cable. The internet light is blinking orange. My account number is BW883017. Sorry, last digit is 1. Can you check what is happening?
CUSTOMERHello?
AGENTThanks for hanging in there. I’ve checked everything on my side. First, just to confirm: I’ve got you under account BW883011 (you mentioned BW883017 but the system shows BW883011 for you). If that’s not right, tell me and I’ll re-check. Here’s what I found and did:
- There is no area outage reported at your address. - The line test showed your signal to the gateway is good, but the gateway wasn’t registered. - I sent a remote reset to your Brightwave BX7, and it’s now showing as registered and online on my end. At home, you should see
- The internet light turning solid (not blinking orange) within a minute or two. - Your devices may take up to 2 minutes to reconnect to Wi‑Fi. When you get back home,
CUSTOMERYour cable.
CUSTOMERYes, account number is BW883011. That is right. I see, no area outage. Thanks for checking. The internet light just went solid white after your reset. How long until my devices reconnect? And can you give me a credit for the downtime? I lost two hours of work.
AGENTGood, glad the light is solid now—that means the connection is back up. 1) Device reconnect time - Most devices will reconnect within 1–2 minutes on their own. - If anything doesn’t come back online after about 2–3 minutes, toggle Wi‑Fi off/on on that device or briefly forget/rejoin the network. 2) Credit for downtime I’ve applied a $10.00 service credit on account BW883011 for the 2 hours of interruption. - It will appear on your next bill.
CUSTOMERI lost two hours of work. Can you make it more than that?
AGENTI understand why you’re asking, especially if it cost you real work time. I do have a limit on what I can apply directly, and I’ve already given the standard $10.00 credit for an interruption of this length on account BW883011. I can’t increase it beyond that from here. What I can suggest, if you feel that doesn’t reflect the impact on your business, is contacting our billing team directly from your home connection or by phone—they can review your history and the specifics of today and see if anything additional is possible beyond what frontline support can grant.
CUSTOMERAlright, that's clear. Thank you for your help, Alex. Goodbye.
AGENTYou’re welcome—glad
AGENTwe got you back online. Credit is set for $10.00 on account BW883011 and will show on your next bill. Take care, and have a good rest of your day.
Healthcare
AGENTRiverbend Health Clinic, this is Alex. How can I help you today?
CUSTOMERHi Alex, I'm having one dermatology appointment next Wednesday morning but I have a work shift now. I need to move that appointment to a later time. My name is Arjun Mehta. Can you help me with that?
AGENTI can help with that. To pull up your chart, I need to verify you first. Please provide your full date of birth (YYYY-MM-DD), and I’ll look up your
CUSTOMERMarch 17, 1948, sorry, no, 1984. March 17, 1984.
AGENTThanks, Mr. Mehta. I’ve found your dermatology visit with Dr. Okafor next Wednesday at 9:15 AM, at Main Campus, Building B. You mentioned you now have a work shift and need a later time. With Dr. Okafor, the next available slots I can move you to are:
- Tuesday at 345 PM - Thursday at 4:10 PM - Friday at 11:30 AM Which of these would you prefer?
CUSTOMERYeah, Tuesdays do not work after all. I have to pick up my daughter then. What about Friday? Is it possible to get a slot after 3 p.m. on Friday?
AGENTRight now, with Dr. Okafor, the only Friday slot I’m seeing is 11:30 AM.
CUSTOMEROkay, then kindly book me for Thursday at 4.10pm. That works for me.
AGENTYou’re all set, Mr. Mehta. I’ve moved your dermatology appointment with Dr. Okafor to:
- DayThursday - Time: 4:10 PM - Location: Main Campus, Building B - Confirmation code: RVB-3M8P2
Please arrive about 15 minutes early with a photo ID and your insurance card. Would you like me to repeat any of those details or help with anything else today?
CUSTOMERYes. Is there any fee for changing the appointment?
AGENTThere’s no fee for the change you just made. Our policy is: changes made more than 24 hours before the appointment are free; changes within 24 hours of the appointment have a $40 fee. Your reschedule to Thursday at 4:10 PM was done well in advance, so there’s no charge.
CUSTOMERAha, that's clear. Where should I park for main campus building B?
AGENTFor Main Campus, Building B, you can park in our free patient parking:
- Lot C, entrance on River Road
From Lot C, just follow the signs for Building B’s main entrance. If you’d like, I can also tell you our clinic hours for the day of your appointment.
CUSTOMERGot it. One more thing. Does Dr. Okafor accept Aetna insurance?
AGENTYes—Dr. Okafor does accept Aetna insurance at Riverbend Health Clinic. Be sure to bring your Aetna insurance card to your Thursday, 4:10 PM appointment, along with a photo ID. If you’d like, I can note Aetna as your primary insurance on your chart so you don’t have to repeat it at check‑in.
CUSTOMERThat's right. Please note Aetna as my primary insurance. And can you read back the new appointment time and confirmation code for me? Certainly, Mr. Mehta. Your new dermatology appointment is Thursday at 4.10pm, main campus building B, with Dr. Okafor. Your confirmation code is RVB3M8P2. Is there anything else I can assist with today?
AGENTThanks, I’ll clarify a couple of details. First, your appointment details, read back clearly:
- DayThursday - Time: 4:10 PM - Location: Main Campus, Building B - Provider: Dr. Okafor - Confirmation code: RVB-3M8P2 (that’s R-V-B–3–M–8–P–2, with a dash after RVB)
Also, I need to correct one thingI actually can’t update your insurance information (like setting Aetna as your primary) from here. That has to be done either at the front desk when you check in, or through the patient portal if you use it.
CUSTOMERAha, understood. I will bring my insurance card and take care of it at check-in. Thank you, Alex. Goodbye.
AGENTYou’re welcome,
AGENTMr. Mehta. Just to recap: your dermatology appointment is on Thursday at 4:10 PM at Main Campus, Building B with Dr. Okafor, confirmation code RVB-3M8P2. Please bring your Aetna card and photo ID to check-in. Goodbye, and take care.
Home services
AGENTCopperline Home Services, this is Alex. What can I help you with?
CUSTOMERI need an emergency plumber right now. A pipe under my kitchen sink just burst and water is spreading across the floor toward a power strip. I haven't shut the water off yet. Can you send someone out?
AGENTFirst stay safe and kill the water—then I can get someone out. 1. Stay away from that power strip. - Do not touch it or any plugs
CUSTOMERGot it!
CUSTOMERYep, I'm doing that. I just shut the wire off. It stopped. What's the fastest way to get someone here?
AGENTNice work getting the water shut off—that’s the most important part. Fastest we can get someone there is our emergency window today from 2:00 PM to 4:00 PM. Before I book it, I need to go over pricing and get your info. For an emergency visit:
- Emergency callout fee$149
- If you’re a Copperline
CUSTOMERYeah.
CUSTOMERYep, heard all that already. I just need to know if anyone can come before 2. If not, book that emergency window. My address is 14 Indy Larkspur Avenue, Unit 4D. I moved from 4B last month. The building gate code is £2719. My callback number is 503-555-0139. No, 0193. Who's coming and how will I know they're on the way? Give me the ticket number when you've got it.
CUSTOMERhello
AGENTYou’re all set—I’ve got you on the emergency schedule. Here’s what’s booked:
- Arrival windowToday, 2:00 PM to 4:00 PM (emergency visit) - Ticket number: CPL-58210 - Emergency callout fee for Copperline Care members: $99
CUSTOMEROkay.
AGENTI’ve got you taken care of—just want to confirm a couple of details and answer your questions. First, for safety until we arrive:
- Keep the water main off. - Unplug or switch off that power strip at the wall only if you can do it without stepping in water or touching anything wet. - If any outlet or the strip looks wet, stay away from it and wait for the plumber; we may recommend an electrician follow-up
CUSTOMERThank you for watching!
AGENTYou’re welcome.
CUSTOMERRight, I understand. I'll keep clear and wait if anything's wet. Who's coming and how will I know they're on the way? Just need that info.
CUSTOMERThanks for booking this. Goodbye!
AGENTYou’re welcome—and
AGENTI’ll keep it quick:
- TechnicianOne of our emergency plumbers will be dispatched on your ticket; you’ll get their name in the update before arrival. - How you’ll know they’re on the way: You’ll get a text to 503-555-0193 about 30 minutes before they arrive, within your window of today, 2:00 PM–4:00 PM. - Ticket number: CPL-58210. For the record, I have you at: 14 Indy Larkspur Avenue, Unit 4D, gate code £2719. If anything changes or the water starts again, call us back and reference that ticket number.
Insurance
AGENTNorthgate Mutual claims, this is Alex. Are you somewhere safe right now?
CUSTOMERYes, I'm safe. I'm standing next to my car, but it's not drivable. I really need to get this sorted quickly.
AGENTUnderstood; we’ll move quickly and step by step. First, is anyone injured or in any danger—either in your vehicle or the other vehicle? - If anyone
CUSTOMERNo.
CUSTOMERNobody's hurt in either car. I just need to file the claim and get my car towed.
AGENTI’m glad to hear no one’s hurt. We’ll get your claim started and arrange a tow. First, I need to locate your policy. 1) What’s your auto policy number? 2)
CUSTOMERRight, let's move ahead. My policy number is NG4471903. No, sorry, last one is 2.
AGENTThanks for confirming. I’ve got your policy pulled up. Next, I’ll get the details so we can open the claim and then I’ll set up a tow. First, I’ll need a few basics about the accident:
1) When did the accident happen? 2) Where
CUSTOMERRight.
CUSTOMERYeah, so about 20 minutes ago, I was stopped at a red light at Southeast 5th Avenue and Alder Street when another car hit me from behind!
AGENTGot it, thanks for explaining what happened. Let me confirm a couple of details and fill in the gaps so I can open the claim and then arrange a tow:
1) Just to pin
CUSTOMERYeah, you don't need to explain. Nobody's hurt, police didn't come, and I can't drive the car. The rear bumper's crushed. Rear wheel is rubbing. I'm still at the scene.
AGENTI’ve got everything started for you and a tow is on the way. Let me walk you through what’s set up and confirm the key details. First, your verified policy number is NG4471902.
CUSTOMERAlright, so what's the claim number? And where is the car going to be taken? I need to know if I get a rental, and how much I have to pay.
AGENTHere’s what I have for you:
1) Claim details - Claim numberNGM-904417 - Policy number:
CUSTOMERuh-huh
CUSTOMERAlright, got it. That's NG-447-1902, right?
AGENTYes, that’s right — your policy number is NG4471902. Let me read back the key details you asked for:
- Claim numberNGM-904417 - Tow destination: A Northgate partner repair shop at 9th and Morrison - Towing coverage: Covered up to 25 miles - Collision deductible: $500 (that’s the amount you’d be responsible for if your car is repaired under collision coverage) - Rental coverage: Up to $40 per day for
CUSTOMERGot it!
CUSTOMERBut he hit me, so I should not have to pay that, right? This was his fault. Also, will my premium go up because of this?
AGENTFrom your policy’s standpoint, the collision deductible still applies even if you were rear‑ended and not at fault. How it usually works in a not‑at‑fault rear‑end:
- Northgate would handle your repairs under your collision coverage and apply your $500 deductible.
CUSTOMERYeah, I understand. But since he hit me and it was his fault, is there any way I don't have to pay that deductible? His insurance is Pacific Shore. Plate 7KXR214. X, like X-ray.
AGENTThanks, that’s helpful info. First, I’ll update what we have on file:
- Other driver’s
CUSTOMERRight.
AGENTThere are
CUSTOMERSo the other
CUSTOMERGo ahead.
AGENTYou’re on the right track giving me his info—that’s exactly what we need. Here’s how your deductible might end up not costing you out of pocket:
1) How the deductible works in this situation - If we repair your car under your collision coverage, your $500 deductible applies up front. - Northgate
CUSTOMERyeah Alright, I get it. So, if Pacific Shore accepts Fault, do they-
CUSTOMERuh-huh
AGENTIf Pacific Shore accepts fault, there are two main ways this can play out:
CUSTOMERMm-hmm.
CUSTOMERYeah, got it. Main thing is, I need to know if I get that deductible back if they accept fault. Do I get a rental car while mine's in the shop?
AGENTYes to both, with some conditions. Here’s how it works on your policy:
1) Your deductible - Right now, your collision coverage has a $500 deductible.
CUSTOMERAlright, just tell me what I need to do next and how long the rental is covered. I'm really short on time.
AGENTHere’s what you need to know, as simply and quickly as possible:
1) Your deductible - Your collision deductible is $500.
CUSTOMERRight.
CUSTOMERYeah, 500. I got it. What about the rental? How many days? Where's my car going?
AGENTHere’s the short version:
1) Claim, tow, and destination - Claim numberNGM-904417 - Policy number: NG4471902 - Your car is being towed to a Northgate
CUSTOMERAlright, thanks. That's all I needed. Goodbye!
AGENTYou’re all set. Key details before you go: - Policy number: NG4471902 - Claim number:
CUSTOMERGot it!
AGENTTake care,
CUSTOMERYep, that's right.
AGENTBefore you go,
AGENTquick recap so you have it all straight:
- Policy numberNG4471902 - Claim number: NGM-904417 - Your car: Being towed to the Northgate partner repair shop at 9th and Morrison - Rental coverage: Up to $40 per day, for up to 30 days, while your claim is open Your adjuster, Priya Shah, will contact you within one business day about next steps, including the deductible and any recovery from the other driver’s insurer. Goodbye, and drive safe when you’re back on the road.