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
#56of 654 stacks
57 / 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.
#88 of 661 on the board · best 99%
Flow adherence
How closely the agent followed the intended conversation flow, judged per call. 5 = fully followed.
#1 of 661 on the board · best 5.0 / 5
Instruction following
How well the agent stuck to the instructions in its own prompt. 5 = followed throughout.
#12 of 661 on the board · best 5.0 / 5
Turn-taking
Barge-in handling
When the caller interrupts, how often the agent made the right call: yielding to a real correction, and holding the floor through a filler.
Not scored by this sweep.
Incorrect interruptions
How often the agent interrupted the caller when it should have kept listening.
#1 of 661 on the board · best 0%
Overtalk
Share of the conversation where both sides were speaking over each other.
#587 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
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.
#464 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.
#432 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.
#342 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.
#340 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.
#476 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.
#319 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.
#335 of 661 on the board · best 0.45 s
Latency spikes
Turns where the response took far longer than the rest of the conversation.
#248 of 661 on the board · best 0.0 per call
By industry
| Industry | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| Healthcare | 95% | 3.58 s | — |
| Home services | 88% | 3.45 s | — |
| Insurance | 100% | 3.61 s | — |
| Customer support | 100% | 3.73 s | — |
By caller
| Caller | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| US English, quiet line | 96% | 3.50 s | — |
| Spanish-accented English, city street | 98% | 3.70 s | — |
| Indian English, busy café | 94% | 3.49 s | — |
| Southern US, flustered, crying baby | 98% | 4.06 s | — |
| Fast and interrupting, construction outside | 94% | 3.32 s | 100% |
The calls behind the numbers
Customer support
AGENTBrightwave Internet support, this is Alex. What’s going on with your service?
CUSTOMERHey. Sorry. I'm a little flustered. Got a baby crying in the background, and my Internet's been out for two hours now. I work from home, so this is kinda urgent. There's a road crew digging up the street right outside, and I'm wondering if that's the reason for the outage. I already rebooted the gateway twice and checked every cable. But the Internet lights blinking orange. Can you check what's going on for me? My account number is BW883017. Sorry. Last digit is one. Is that enough?
AGENTThanks for your patience. I’ve checked your account (BW883011) and there’s no area outage, even with that roadwork. Your gateway wasn’t connecting correctly, so I’ve sent a reset from my end. Give it about two minutes—your devices should reconnect automatically. If it doesn’t come back online, let me know right away and I’ll get a technician out. Otherwise, you should be good to go!
CUSTOMEROkay. Sorry. But the light just went solid white now. Does that mean it's working again? How long should it take for my computer in phone to reconnect? And since I lost two hours of work time, can I get a credit for the downtime? I just wanna make sure I'm not missing anything here.
AGENTA solid white light means your internet is back online—your computer and phone should reconnect automatically within a minute or two. If they don’t, a quick restart of your device should do the trick. I've also applied a $10.00 credit to your account (BW883011) for the two hours of downtime. You’ll see that on your next bill. You’re all set, but if anything else comes up or your connection drops again, just let me know.
CUSTOMERThank you. I appreciate that, but I did lose two hours of work. Is there any way you could make the credit a little more than $10? Sorry to be a bother. I just wanna make sure it's fair.
AGENTYou’re not a bother at all—I completely understand wanting fair compensation for lost work time. I’ve already applied the standard $10.00 credit for the downtime, which is our maximum for a two-hour interruption per our policy. I wish I could increase it, but that’s the limit I’m able to authorize. If you have more frequent outages, let us know and we can review your account further. For today, you’ll see the $10.00 credit on your next bill. If you need any supporting documentation for your employer about this outage, I can provide that as well—just ask. Is there anything else I can help you with right now?
CUSTOMERNo. That's alright. Thank you for helping me out. I appreciate you taking care of it. Have a good day.
AGENTYou’re very welcome, and thank you for your patience—especially with everything going on at home. I’m glad we got you back online. If you ever need anything else, just reach out. Have a great day!
Healthcare
AGENTRiverbend Health Clinic, this is Alex. How can I help you today?
CUSTOMERHi. Oh, yes. I need to reschedule my dermatology appointment next Wednesday morning. I just got a work shift, so I can't make it. My name is Lucia Ramirez.
AGENTThanks, Lucia. To help you reschedule, could you please provide your date of birth for verification?
CUSTOMERHello?
AGENTI'm here! I just need your date of birth, Lucia, to verify your identity before I can access your appointment details. Could you please provide your date of birth?
CUSTOMERYeah. Of course. March 17.
AGENTThank you. Could you confirm the year of your birth as well? I need the full date (YYYY-MM-DD) to proceed with verification.
CUSTOMERRight. The full date is nineteen eighty four March seventeen.
AGENTYou have a dermatology appointment with Dr. Okafor next Wednesday at 9:15 AM. I’ll check for available slots to reschedule this appointment. Is there a specific day or time that works best for you, or should I look for any upcoming open times with Dr. Okafor?
CUSTOMERYeah. I can only come after 3PM. Anything after that,
AGENTDr. Okafor has the following available times after 3 PM: - Tuesday at 3:45 PM. - Thursday at 4:10 PM. Would you like to reschedule for either of these options?
CUSTOMERTuesdays don't work. After all. I pick up my daughter then. What about Friday?
AGENTOn Friday, the only available slot with Dr. Okafor is at 11:30 AM, which is before 3 PM. Would you like to book Thursday at 4:10 PM, or should I look further ahead for other options after 3 PM?
CUSTOMERThere's at 04:10PM works. Let's do that.
AGENTYour dermatology appointment has been rescheduled to Thursday at 4:10 PM with Dr. Okafor. Your new confirmation code is RVB-3M8P2. Please arrive 15 minutes early with a photo ID and your insurance card. Is there anything else I can help you with today?
CUSTOMEROh, yes. Just a few things. Is there any fee for changing the appointment?
AGENTThere’s no fee for changing your appointment since you made the change more than 24 hours in advance.
CUSTOMEROkay. Where should I park when I come?
AGENTYou can park for free in Lot C, with the entrance on River Road.
CUSTOMERAlright. Does doctor Okafor accept Aetna insurance?
AGENTI don't have direct access to insurance acceptance details for specific providers. For the most accurate answer, I recommend calling Riverbend Health Clinic’s main line or checking with your insurance provider. Would you like the clinic’s contact number?
CUSTOMERNo. That's okay. I have it already. You for your help. Goodbye.
AGENTYou're welcome, Lucia! If you need anything else, just let us know. Have a great day! Goodbye.
Home services
AGENTCopperline Home Services, this is Alex. What can I help you with?
CUSTOMERHi. Yes. I have a burst pipe under my kitchen sink. Water is spreading across the floor, and it's heading toward a power strip. Can you send someone urgently?
AGENTThank you for calling, and I’m sorry you’re dealing with this. Your safety is the top priority. First, please do the following
CUSTOMERGo ahead. What do I need to do?
AGENTPlease follow these steps right away: 1. If you can safely reach your main water shutoff valve, turn it off immediately to stop the water flow. 2. Stay away from the area
CUSTOMEROh,
AGENTTake
CUSTOMERRight. I have shut off the water now. It has stopped. What next?
AGENTGreat job turning off the water—that’s the most important step.
CUSTOMEROh,
CUSTOMERRight. So can you send someone out?
AGENTAbsolutely—we’ll get a technician out to you as soon as possible. I just need a few quick details: 1. Can you please confirm your address and the best phone number to reach you? 2. Is there a unit number,
CUSTOMERYes.
AGENTThank
CUSTOMERMy address is 1480 Larkspur Avenue, Unit 4 D. I moved from 4 B last month so it's 4 D now. My phone number is 503-0139. No. Zero one nine three.
AGENTThank you for confirming your address and phone number, and for letting me know about the unit change. I have your service address as 1480 Larkspur Avenue, Unit 4 D, and your callback number as 503-0139. Since you’re a Copperline Care member, the emergency callout fee will be $99. The earliest emergency window available is today between 2:00 PM and 4:00 PM. Would you like me to book this emergency visit for you at that time for $99?
CUSTOMERCan anyone come before 2PM? The water has been spreading. And it's an urgent situation.
AGENTI understand how urgent this is, and I agree it’s important to address the situation quickly. I’ve checked our emergency schedule, and the earliest available arrival window is today between 2:00 PM and 4:00 PM—there are no earlier emergency slots showing right now. Would you like to confirm this 2:00 PM to 4:00 PM emergency visit for $99, or should I keep looking for cancellations and update you if something sooner opens up?
CUSTOMERGo ahead and book the 2PM to 4PM emergency visit for $99. Who will be coming, and how will I know they are on the way? Also, may I have the ticket number, please?
AGENTYour emergency visit is confirmed for today between 2:00 PM and 4:00 PM at 1480 Larkspur Avenue, Unit 4 D. The callout fee will be $99. Your ticket number is CPL-58210. Our technician will text your callback number (503-0139) about 30 minutes before arrival so you’ll know when they’re on the way. If you need to update anything or have further questions, just let me know. Is there a gate code or any special access instructions you’d like us to pass along?
CUSTOMERYes. The building gate code is Pound 2719. That's 2719. Please make sure the technician has it. Thank you, Alex. Goodbye.
AGENTThank you for the gate code—Pound 2719. I’ve added that to your ticket so the technician can access your building. To recap: - Service address: 1480 Larkspur Avenue, Unit 4 D. - Callback number: 503-0139. - Emergency visit window: Today, 2:00 PM to 4:00 PM. - Gate code: #2719. - Ticket number: CPL-58210. - Fee: $99.
CUSTOMERYes.
AGENTThank
CUSTOMER$9. That's right.
AGENTThank you for confirming. Just to clarify, the emergency callout fee for Copperline Care members is $99—not $9. Your visit is set for today between 2:00 PM and 4:00 PM at 1480 Larkspur Avenue,
CUSTOMERUh-huh. That's correct. Thank you for your help, Alex. Goodbye.
AGENTYou’re very welcome. We’ll see you between 2:00 PM and
AGENT4:00 PM, and the technician will text you before arrival. If anything changes, just give us a call. Take care and goodbye!
Insurance
AGENTNorthgate Mutual claims, this is Alex. Are you somewhere safe right now?
CUSTOMERYes. I'm in a safe spot. I'm standing next to my car off the road.
AGENTThank you for letting me know. Was anyone injured in the accident, or does anyone need medical attention?
CUSTOMERNo. Nobody was hurt. We're both okay, and nobody needs medical attention.
AGENTI'm glad to hear everyone is okay. To start your claim, I’ll need to verify your policy. Could you please provide your policy number and your last name?
CUSTOMERSure. My policy number is NG4471903. No. Sorry. Last one is 2. My last name is Ellis.
AGENTThank you, Mr./Ms. Ellis. Just
CUSTOMERIt's miss
CUSTOMERyeah. That's right.
AGENTThank you, Miss Ellis. I have your policy number as NG4471902, and your last name is Ellis. Next, I’ll need a few details about the accident: - When did the accident happen? - Where did it take place? - What happened? - The other driver’s name, insurer, and license plate (if you have them). - Did the police
CUSTOMERThe I
AGENTNo problem,
CUSTOMERSorry. Let me okay. So the accident happened about twenty minutes ago. I was stopped at a red light at SE 5th Avenue and Alder Street. Another car hit the back of my car. The back bumper is crushed, and the rear wheel rubs, so I can't drive it. The other driver is Kevin Lund. Insured with Pacific Shore. His license plate is 7KX like X-ray, R214. The police did not come.
AGENTMiss Ellis, let me confirm the details: - Your policy number is NG4471902. - Your license plate is not needed, but the other driver's plate is 7KXR214. - Your claim number is NGM-904417. A tow truck from Eastside Towing is on its way to SE 5th Avenue and Alder Street. It should arrive in about 45 minutes and will take your car to Northgate’s partner repair shop at 9th and Morrison. Your assigned adjuster is Priya Shah. She will contact you within one
CUSTOMEROkay.
AGENTThank you, Miss
CUSTOMERAlright.
CUSTOMERThanks. Can you tell me if I have to pay anything for this? Is there a deductible? And since he hit me, I shouldn't have to pay that. Right? Also, do I get a rental car while mine's being fixed? Will my premium go up?
AGENTMiss Ellis, here’s what your policy covers: - There is a $500 collision deductible. Whether you have to pay it depends on how the claim is resolved. If the other driver’s insurer accepts fault, they may reimburse your deductible. - You have rental coverage: up to $40 per day for up to 30 days, starting today. - Towing is covered up to 25 miles, which applies to your situation. I can’t speak to whether your
CUSTOMEROh.
AGENTIf you have questions about the deductible or about your premium, your adjuster Priya Shah will review the details with you. She’ll contact you soon and help with next steps, including any reimbursement if the other driver is found at fault. Is there anything else I can help with right now?
CUSTOMERNo. That covers it. Thank you for your help. Goodbye.
AGENTYou’re welcome, Miss Ellis. Your claim number
AGENTis NGM-904417, and a tow is on the way. If you need anything else, please call us. Take care. Goodbye.