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
#130of 654 stacks
53 / 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.
#191 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
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.
#115 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.
#307 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.
#497 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.
#452 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.
#390 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.
#322 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.
#112 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 | 100% | 3.60 s | — |
| Home services | 81% | 3.65 s | — |
| Insurance | 98% | 3.55 s | — |
| Customer support | 96% | 3.22 s | — |
By caller
| Caller | Task completion | Latency P50 | Barge-in handling |
|---|---|---|---|
| US English, quiet line | 100% | 3.73 s | — |
| Spanish-accented English, city street | 80% | 3.32 s | — |
| Indian English, busy café | 96% | 3.50 s | — |
| Southern US, flustered, crying baby | 96% | 3.96 s | — |
| Fast and interrupting, construction outside | 98% | 3.31 s | 100% |
The calls behind the numbers
Customer support
AGENTBrightwave Internet support, this is Alex. What’s going on with your service?
CUSTOMERHi Alex, my internet's been down for about 2 hours now. I work from home, so it's a bit of a problem. There's a road crew digging up the street outside. I already rebooted the gateway I've reset the gateway twice and checked all the cables. The internet light is blinking orange. Could you check if the roadwork is causing this? My account number is BW883017. Sorry, last digit is 1.
AGENTThanks for the details, and thanks for already rebooting everything—that helps. I’ve got you under account BW8830171. Here’s what I’m seeing and what I did:
- There’s no area outage showing for your address, so nothing official tied to the roadwork right now. - The line test showed the signal to your gateway was good, but the gateway wasn’t registered. - I’ve just sent a remote reset to your Brightwave BX7, and it now shows as registered and online on our side. Your devices may take 1–2 minutes to reconnect. Please check
1. Is the internet light on the gateway now solid (not blinking orange)? 2. Can you get online on a work device (try a quick website or your VPN)? If it’s still blinking orange or you’re not online after a couple of minutes, tell me what the light is doing and whether you’re on Wi‑Fi or ethernet, and I’ll take the next step.
CUSTOMERThe internet light just went solid white. How long until my devices reconnect?
AGENTGood—that solid white light means the connection is back up. Most devices will reconnect within 1–2 minutes on their own. If you need to get working right away:
- Wi‑Fi devicesWait about a minute, then toggle Wi‑Fi off and back on, and reconnect to your usual network. - Wired (ethernet) devices: They should come back within seconds; if not, unplug and replug the ethernet cable at the computer end. If anything is still offline after a couple of minutes, tell me which device and how it’s connected (Wi‑Fi or cable), and I’ll walk you through a quick fix.
CUSTOMEROkay, so I should be good in a minute or 2. While I have you, could I get a credit for the downtime? I lost 2 hours of work today.
AGENTYes, I can do that. I’ve applied a $10.00 credit on your account BW8830171 for the 2 hours of interruption. You’ll see it on your next bill. If the crew outside causes any more drops or if the light starts blinking orange again, contact us right away and we’ll recheck the line and, if needed, get a tech out.
CUSTOMERI lost 2 hours of work. Can you make it more than that?
AGENTI understand why you’re asking, especially if it cost you work time. I’ve already applied a $10.00 credit on account BW8830171, and that’s the maximum I’m able to issue for this kind of short, single interruption. I can’t increase it further from here. However, if you see more instability today or over the next few days—especially with that roadwork going on—contact us again right away. If we confirm repeated issues or a longer outage window, we can review additional credit based on the extended impact.
CUSTOMERAlright, I appreciate you checking and applying the credit. Thanks for your help, Alex. Goodbye.
AGENTYou’re welcome—glad
AGENTwe got you back online. The $10.00 credit is set on account BW8830171 and will show on your next bill. If anything acts up again, just reach out. Take care.
Healthcare
AGENTRiverbend Health Clinic, this is Alex. How can I help you today?
CUSTOMERHi, I need to reschedule my dermatology appointment next Wednesday morning. I just got assigned a work shift, ma'am, so I need to move it to a later time. Can we do this quickly? I'm on my lunch break.
AGENTI can help with that. First, I need to verify your identity. Please tell me:
- Your full name (first, middle if you have one, and last), and
CUSTOMERYep, got it. My name's Nate Brooks. Let's move on.
AGENTI also need your date of birth to look you up before I can change anything. What’s your date of birth, Nate?
CUSTOMERMarch 17th, 1948. Sorry, no, 1984. March 17th, 1984. Let's keep going.
AGENTYou’re verified, Nate—thanks for the info. You’re currently booked with dermatology, with Dr. Okafor, next Wednesday at 9:15 AM at Main Campus,
CUSTOMERI need to move it. Can only come after 3 PM. What do you have open?
AGENTRight now with Dr. Okafor, the first openings after 3 PM are:
- Tuesday at 345 PM - Thursday at 4:10 PM Your current appointment is next Wednesday
CUSTOMERUh-huh.
CUSTOMERTuesdays don't work after all. I pick up my daughter then. What about Friday? Any spots after 3?
AGENTFor Friday with Dr. Okafor, the only opening I see is at 11:30 AM—nothing after 3 PM. The late-day options currently available with Dr.
CUSTOMERJust give me Thursday at 4:10 PM. That'll work. Let's confirm it.
AGENTYou’re all set, Nate. I’ve moved your dermatology appointment with Dr. Okafor from next Wednesday at 9:15 AM to:
- Thursday at 410 PM - Location: Main Campus, Building B - Confirmation code: RVB-3M8P2
Please arrive 15 minutes early with a photo ID and your insurance card. Can I help you with anything else before you head back from lunch?
CUSTOMERYeah, a couple of quick things. Is there a fee for changing the appointment? Where do I park? Also, does Dr. Okafor take Aetna insurance?
AGENTHere’s the rundown, Nate:
1) Fee for changing the appointment Your change is free. The clinic only charges a $40 fee if you change less than 24 hours before the appointment. You rescheduled well in advance, so there’s no fee. 2) Where to park Parking is free for patients in Lot C.
CUSTOMEROkafor accept Aetna insurance? Just want to double-check before I head in.
AGENTI don’t have access to Dr. Okafor’s current insurance panel, so I can’t safely say yes or no. To double-check Aetna coverage for your visit, you have two good options:
1)
CUSTOMERRight, that's fine. I'll check with billing. Just to confirm, Thursday at 4:10 PM, Main Campus, Building B, and the code is RVB-3M8P2, correct?
AGENTThat’s correct, Nate: - Thursday at 4:10 PM - Dermatology with Dr. Okafor - Main Campus, Building B - Confirmation code: RVB-3M8P2
CUSTOMERGreat, that's all I needed. Thanks for your help. Goodbye.
AGENTYou’re
AGENTwelcome, Nate. Have a good rest of your day—goodbye.
Home services
AGENTCopperline Home Services, this is Alex. What can I help you with?
CUSTOMERHi, I've got an emergency. A pipe under my kitchen sink just burst. There's water spreading across the floor toward a power strip. I haven't shut the water off yet. Can you help?
AGENTYes, I can help. First let’s keep you safe and stop the water. 1. Stay away from that power strip. - Do not touch it or anything plugged into it. - If water has already reached it or is very close, do not step into the water near it. 2. Shut off the water right away: - Look under the kitchen sink for two small shutoff valves (one for hot, one for cold) on the pipes going up to the faucet. - Turn both knobs clockwise until they stop. - If you can’t find or turn those, go to your main water shutoff:
- Often inbasement, garage, utility room, or a closet near the front of the house; outside near the street in a ground box for some homes. - Turn the main valve clockwise to shut it off. Can you tell me:
- Has the water already reached the power strip, or is it still a little distance away? - Are you able to see and turn the shutoff valves under the sink or get to the main shutoff safely without walking through water near that strip? I’ll stay with you step‑by‑step on the shutoff, then we’ll get a plumber dispatched on an emergency ticket.
CUSTOMERYeah, I can reach the valves under the sink without going near the power strip. I'm turning both knobs clockwise now. Okay, the water's stopped. So about getting someone out here— How much will the visit cost? And can anyone come before 2?
AGENTNice work getting the water stopped—that’s the most important part. On cost and timing:
- For an emergency visit today, the callout fee is $149 before any repair work or parts. - We take card or check on completion. - The earliest emergency arrival window I can see is today from 200 PM to 4:00 PM. There’s nothing available earlier than 2:00 in the system right now. If you’d like, I can also check you in as a standard (non‑emergency) visit for another time, which would use the $79 standard visit fee instead—but that would not be for today before 2. Given that the pipe burst and you’ve had water on the floor near a power strip, I strongly recommend we keep this as an emergency. A couple of quick safety notes while we set this up:
- Unplug the power strip only if the area around it is completely dry and you can reach it without stepping in any dampness. - If there’s any moisture nearby, leave it alone until a professional can check it. - Mop up or towel up as much water as you safely can, especially near cabinets and baseboards. Next stepI can book that 2:00–4:00 PM emergency window at $149 for the callout. Are you okay with that fee and time window? If yes, I’ll grab your address and callback number to open the ticket and get you on the schedule.
CUSTOMERThat's fine. I'll take the earliest emergency slot. My address is 1480 Larkspur Avenue, Unit 4D. I moved from 4B to 4D last month, so make sure Sure, it's 4D. My callback number is 503-555-0139. No, 0193. Who will be coming, and how will I know they're on the way? I also need to hear the ticket number before we finish.
AGENTYou’re all set—I’ve got you on the schedule. Let me walk through everything and answer your questions. First, your pricing as a Copperline Care member: - Emergency callout fee: $99 (member rate), due on completion. - Any repair work or parts would be additional, and the tech will go over options and pricing before proceeding. We’ve opened an emergency service request and booked the earliest slot: - Ticket number: CPL-58210 - Arrival window: today, 2:00 PM to 4:00 PM Contact and address details on the ticket (using what you just gave me, not the old info): - Service address: 1480 Larkspur Avenue, Unit 4D - Callback number: 503-555-0193 Please confirm: that’s 1480 Larkspur Avenue, Unit 4D, callback 503-555-0193. On “who” and how you’ll know they’re coming: - A Copperline plumber will be dispatched on your ticket. - You’ll get a text to 503-555-0193 about 30 minutes before arrival so you know they’re on the way. - They’ll also have your ticket number CPL-58210 if you need to reference it. Until the tech arrives: - Keep the under-sink valves closed and avoid turning water back on. - Keep clear of the power strip area—don’t plug it back in or use that outlet until a pro says it’s safe. - Blot up as much water as you safely can with towels, especially near cabinets and baseboards, to limit damage. If anything changes—like water starting again or you notice electrical issues (sparks, burning smell, outlets not working)—call us right away and mention ticket CPL-58210.
CUSTOMERThat's all correct, yeah. Thanks for your help. I'll watch for the text and keep the valves closed until the plumber gets here. Goodbye.
Insurance
AGENTNorthgate Mutual claims, this is Alex. Are you somewhere safe right now?
CUSTOMERYes, I'm in a safe spot, just standing next to my car.
AGENTI’m glad you’re safe. Is anyone injured at the scene—either in your car or the other vehicle—or is anyone in any danger right now? If anyone is hurt or in danger, please call 911 immediately before we continue.
CUSTOMERNo, nobody is hurt and there's no danger. It was just a fender bender.
AGENTUnderstood, thanks for letting me know. Let’s get your policy pulled up so we can open a claim. What’s your auto policy number, and your last name as it appears on the policy?
CUSTOMERMy policy number is NG4471903. Oh, sorry, last one is 2. My last name is Ellis.
AGENTThanks, Ms. Ellis. I’ve located your policy ending in 1902 for your 2021 Honda CR‑V. Let’s get the details of the accident so I can open the claim. 1) When did the accident happen? 2) Where did it happen? 3) Can you briefly describe what happened? 4) Do you know the other driver’s name, their insurance company, and their license plate number? 5) Did the police come to the scene, and is there a report number, if you were given one?
CUSTOMERSure. The accident happened about 20 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's name is Kevin Lund. He's insured with Pacific Shore. The license plate is 7KX, like x-ray, R214. Police did not come, and there's no report number.
AGENTThanks for all that information, Ms. Ellis. I’ve opened a claim for you and arranged a tow since the car isn’t drivable. Here’s what I have documented:
- Policy numberNG4471902 - Accident time: About 20 minutes ago - Location: SE 5th Avenue and Alder Street - What happened: You were stopped at a red light when you were rear‑ended by another vehicle driven by Kevin Lund. Your rear bumper is crushed and the rear wheel is rubbing, so the vehicle can’t be driven. - Other driver: Kevin Lund, insured with Pacific Shore, license plate 7KXR214 - Police: Did not attend; no report number - Injuries: None reported at the scene Your claim is now open: - Claim number: NGM-904417 - Assigned adjuster: Priya Shah – you’ll hear from her within one business day. Coverage on your policy for this vehicle includes: - Collision deductible: $500 (this is the amount you’d be responsible for on collision repairs, subject to liability decisions) - Rental coverage: $40 per day for up to 30 days, starting today (the day the claim is opened) - Towing: Covered up to 25 miles
I’ve also set up a tow for you- Tow company: Eastside Towing - ETA: About 45 minutes from now - Destination: A Northgate partner repair shop at 9th and Morrison
Let me read back the key numbers so you can double‑check me
- Policy number I haveNG4471902 - Other driver’s license plate I have: 7KXR214 - Your new claim number: NGM-904417 Please tell me if any of that is not correct. While we wait for the tow, do you have any questions about what happens next—repairs, rental car, or how the adjuster will handle the claim?
CUSTOMERThe policy number is NG4471902. That's right. The other driver's plate: 7KX, like x-ray. R214. Yes, that's correct. And the claim number? NGM-904417. Got it. So, So, I have a couple of questions. First, you mentioned a $500 deductible, but since he hit me, am I supposed to pay that? Also, will my premium go up because of this? And for the rental car, do I just go pick it up, or will someone arrange it for me?
AGENTOn the details: - Policy number: NG4471902 - Other driver’s plate: 7KXR214 - Claim number: NGM-904417 All confirmed as you repeated them. On your questions:
1) Do you have to pay the $500 deductible if he hit you? Your policy’s collision deductible is $500, but whether you actually end up paying it depends on how liability is decided. Typical process (in plain terms)- We’ll investigate who’s at fault (usually rear‑end collisions are on the other driver, but your adjuster has to confirm based on statements, any photos, and insurance info). - If we repair your car under your collision coverage, the shop may initially apply your $500 deductible. - If the other driver is found fully at fault and their insurer pays us back for the loss, your deductible may be reimbursed to you. I can’t decide fault or guarantee the outcome here—that will be up to your adjuster, Priya Shah—but you do have collision coverage with a $500 deductible, and reimbursement is possible if the other company accepts liability. 2) Will your premium go up because of this? I don’t have access to rating or future premium decisions, so I can’t say if your rate will go up or by how much. That depends on: - The final fault decision - Your state’s rating rules - Your overall driving/claims history Your adjuster or a Northgate billing/agent representative can walk you through how this specific claim might affect your premium once more facts are known. I’m limited to what I see in the coverage lookup and can’t quote or predict premium changes. 3) How does the rental car work? Your rental coverage is: $40 per day for up to 30 days, starting today (the day the claim was opened). Here’s how it’s usually handled: - Your adjuster will either: - Set up a direct bill with a preferred rental company (you pick up the car, and the rental company bills Northgate up to your limits), or - If you choose your own rental company, you may pay them and then submit receipts for reimbursement up to $40/day. - Because your car is being towed to a partner repair shop, it’s common for the adjuster to coordinate rental through a nearby rental office. Since it’s just been reported, the exact rental arrangements will come from Priya Shah when she contacts you (within one business day). She’ll tell you: - Which rental company to use - Whether they’ll contact you directly or you should call them - What to do if the daily rate is higher than $40
If you’d like, I can note in your claim that you need a rental right away so Priya makes that a priority when she reaches out. Before we wrap up- Is your phone number on file the best number for the adjuster to reach you today and tomorrow? - Do you have any other questions about the deductible, fault, or the next steps with the repair shop or rental?
CUSTOMERYes, my phone number is 907-416-9084. That's the best number to reach me today and tomorrow. I'd appreciate if you could note that I need a One more thing, where exactly is the repair shop at 9th and Morrison? And will Priya contact me directly, or should I expect a call from the rental company first?
AGENTI’ll add that phone number and your rental need to the claim notes, and then I’ll answer your questions. I have your best contact as:
- 907-416-9084
I’ll note
- You need a rental car
AGENTas soon as possible. - It’s okay to call you today and tomorrow at that number. On the repair shop location:
- The tow is headed to a Northgate partner repair shop at 9th and Morrison. - I don’t have the exact street address in my system, just that intersection and that it’s one of our partner facilities. - When Priya contacts you, she can give you the full address, name of the shop, and any directions you need. The tow truck driver will also know exactly where they’re taking the vehicle. On who will contact you next
- Your assigned adjuster, Priya Shah, will contact you directly within one business day. - Typically, Priya will
- Confirm the damage and repair plan with you and the shop. - Go over your rental coverage and either set up the rental for you or tell you which rental company to call. - Sometimes the rental company may call you after Priya sets it up, but the first point of contact about how to proceed should be Priya. So, in order
1) Expect a call from your adjuster, Priya Shah, at 907-416-9084 within one business day. 2) She’ll confirm the repair shop details and arrange or authorize the rental. Is there anything else you’re unsure about while you’re waiting for the tow—like what to take out of the car, or what happens if the tow is delayed?