Voice AI testing for telecom validates a voice agent on the carrier path callers actually use: SIP signaling, narrowband PSTN audio, DTMF keypad tones, and concurrent call load, rather than the clean WebRTC audio of a browser demo. Cekura runs those conditions directly, because an agent that passes on wideband audio can still misread a digit on narrowband.
TL;DR
- Cekura tests telecom voice agents on the narrowband G.711 codec real calls use, because Cekura's telephony testing guide documents an agent that confirmed "1-5-1-3" as "1-9-1-3" once the call dropped to narrowband.
- Cekura measures one-way delay against ITU-T G.114, which Cekura's telephony testing guide reports puts the preferred ceiling for one-way, mouth-to-ear delay at 150 ms, with quality degrading between 150 and 400 ms and past 400 ms "a conversation falls apart".
- Cekura Bench publishes no telecom-specific pass rate, because it does not segment by industry. It does publish infrastructure reliability across 7 configurations, ranging from 72.36% to 100%. Two caveats travel with that figure: providers selected these configurations, the OpenAI row excepted, where Cekura tested gpt-realtime-2.1 directly with no configuration submitted by OpenAI; and response time is Cekura's main-agent measure, not provider-native component timing.
- Cekura tests DTMF separately from speech, because Cekura's telephony testing guide documents an agent that asked for a 9-digit member ID, "captured the speech fine and ignored the keypad tones".
- Cekura load tests at expected peak rather than one call at a time, because its telephony testing guide records an agent that handled 5 test calls cleanly and "degraded badly at 25 concurrent calls".
What is voice AI testing for telecom?
Voice AI testing for telecom is the practice of validating a voice agent against the conditions of a real carrier call rather than a browser session. A telecom deployment routes calls through SIP trunks, narrowband codecs, and carrier infrastructure the agent's developers rarely control, so the suite has to exercise call setup, audio degradation, keypad input, and concurrent load the way production traffic will. Cekura's telephony testing guide sets out the checkpoints a call passes before the agent reaches a happy customer, naming the first as "Call setup, SIP registration, inbound, and outbound routing", failing when "Calls fail to connect or drop on pickup". The agent under test is itself telecom-specific: a 2025 telecom voice agent pipeline paper assembles its stack from telecom-adapted ASR, LLM and TTS models and evaluates it on telecommunications questions sourced from RFC documents rather than general speech data. Cekura tests the agent that answers on that path, not the carrier network underneath it, because the layers fail independently.
What are the key benefits of using end-to-end voice AI testing for telecom networks?
End-to-end voice AI testing for telecom networks exercises the whole call path in one run, from SIP setup through narrowband audio to the agent's reply, not each layer alone. Cekura tests this way because telecom failures do not share a cause: a clean result on one layer predicts nothing about the next. Cekura's telephony testing guide documents three cases that make the point. One agent confirmed "1-5-1-3" as "1-9-1-3" once the call dropped to narrowband. One asked for a 9-digit member ID and "captured the speech fine and ignored the keypad tones". One "handled 5 test calls cleanly degraded badly at 25 concurrent calls" after a provider rate limit began queuing responses. Each passes the component test for its own layer. Only the end-to-end run catches the failure. The benefit an operator gets is a decision, not a recording to review, since Cekura scores every run against a stated pass or fail criterion.
What should a telecom voice AI test suite cover on the carrier path?
Cekura's telephony testing guide names seven checkpoints a call passes through before the agent reaches a satisfied caller, each breaking in its own way and leaving its own fingerprint. Cekura tests each checkpoint as its own scenario set rather than folding them into a single conversation test, because a suite that scores only the conversation reports a pass while the call path underneath it is failing. The table below sets what a browser-based demo test actually exercises against what Cekura runs on the carrier path. The demo column is the honest one: for four of the eight rows, a browser session has no mechanism to test the failure at all.
| Checkpoint | Failure mode on a real telecom call | What a browser demo test covers | What Cekura runs instead |
|---|---|---|---|
| Signaling and connectivity | "Calls fail to connect or drop on pickup" | Nothing: a WebRTC session never registers over SIP | Automated inbound and outbound calls over a live carrier, across every configured route including failover |
| Audio quality and codec | "Garbled speech, the agent mishears digits" | Wideband audio that keeps detail the carrier throws away | The same scenario on the 8 kHz narrowband codec callers use, with transcripts compared against wideband |
| Latency and turn timing | "The agent talks over the caller or leaves dead air" | Local round trip with no carrier transport in the path | One-way delay and agent processing measured against the shared ITU-T G.114 budget, at p90 rather than mean |
| Jitter and packet loss | Buffers exhaust and lost packets corrupt what the agent hears | A clean local connection | Test under real jitter and loss: Cekura's VoIP testing guide reports "Jitter above 100ms" exhausting jitter buffers and "Packet loss at even 1%" corrupting call quality |
| DTMF and IVR | "Wrong branch, PIN entry ignored" | Nothing: a browser has no keypad tones to send | Injected digit sequences for PINs, account numbers, and transfers across every menu branch |
| Concurrency and load | "Slowdowns or failures at peak volume" | One call at a time | A ramp from baseline toward peak, watching where latency and error rates break |
| Call control | "The caller dropped during a handoff" | Nothing: transfers, hold, and voicemail have no browser equivalent | Transfer, hold, and voicemail detection scripted as first-class flows rather than as side effects |
| Security | "The agent leaks info or breaks its rules" | Single-prompt text probes | Jailbreak and data-extraction probes across a full multi-turn call, since adversarial callers behave differently on audio than in chat |
Signaling and audio surface earlier than the rest in a real deployment, because a call that never connects or arrives unintelligible never reaches the point where DTMF handling, call control, or security would be exercised at all.
Is there benchmark data on voice AI testing for telecom?
No telecom-specific pass rate exists. Cekura Bench does not segment results by industry, and the word telecom appears nowhere on it. What it publishes is the outcome a telecom deployment cares about most: infrastructure reliability, "the share of calls that completed without a provider-side or connection issue", scored across "All 246 retained calls per configuration". Across the 7 configurations in this frozen matched study of 82 scenarios and 3 repeats, that figure runs from 72.36% to 100%. Two caveats travel with it: providers selected these configurations, except the OpenAI row, where Cekura tested gpt-realtime-2.1 directly with no configuration submitted by OpenAI; and response time here is Cekura's main-agent measure, not provider-native component timing. Method matters more than ranking, because Cekura keeps failed connections in the denominator: "Calls that did not connect or produced no transcript stay in the denominator." Busy signals and failed SIP registration are production outcomes, and filtering them before scoring reports a number the phone line will not honour.
What is the most effective approach for voice AI testing for telecom support agents?
The most effective approach for voice AI testing for telecom support agents is scenario-based testing scored against explicit pass or fail criteria, re-run as regression on every change rather than checked once before launch. Cekura defines those criteria on metrics including "instruction following, latency, CSAT, interruptions", so a scenario meets its criterion or fails it instead of being graded by hand from a recording (see Cekura's automated voice bot testing guide). Cekura runs those scenarios on the carrier path the support line uses, since an agent that resolves a billing question on wideband audio can still misread an account number on narrowband. Cekura scores that failure as Word Error Rate, and per Cekura's voice AI evaluation metrics guide, "Errors in names, nouns, and numbers count fully. Verb errors count as half, since they less often change intent." That weighting is what makes WER usable on a telecom support line, where the intent-bearing words are account numbers, PINs, and plan names rather than verbs.
How do I implement automated voice AI testing for telecom customer service applications?
Implementing automated voice AI testing for a telecom customer service application connects the agent to a harness that places real calls, then encodes each journey as a scenario with its own pass criterion. Cekura integrates natively with the stack a deployment runs, listing "LiveKit, Pipecat, Vapi, Retell, ElevenLabs, Telnyx", so tests reach the agent over the path production traffic uses. Cekura's implementation order is:
- Connect the agent through its existing telephony stack, so every test call runs on the carrier path.
- Turn each journey, a bill inquiry, a SIM swap, a failed authentication, into one scenario with one pass criterion.
- Score each scenario on instruction following, latency, CSAT, and interruptions.
- Run the suite on narrowband audio at expected peak concurrency, not one call at a time.
- Keep the suite running after launch, since routing, prompts, and menus change.
Step 5 is continuous production monitoring, which turns a launch test into a standing regression suite. The five steps are Cekura's recommended order, not a measured finding.
Which telecom customer journeys should a voice AI test suite cover?
A telecom voice AI test suite covers the operator's own customer journeys, not only the call path they arrive on. Cekura scores each journey against explicit pass or fail criteria, the same mechanism Cekura applies to instruction following, latency, CSAT, and interruptions, because scoring conversation quality alone never confirms the agent reached the billing system or handed off cleanly when authentication failed. Cekura's IVR testing guide records what happens when the routing under a journey changes: "A telecom provider changes press 3 for support to press 4 after adding a new service. Regression testing reveals that the change silently broke after-hours overflow routing. Customers calling at night were getting stuck in the main menu." The change shipped; the regression run is what caught it. Cekura scores each journey on the carrier path it arrives on, so a billing scenario runs on narrowband audio and an authentication scenario uses keypad entry, not speech alone. The journeys below are test-design targets Cekura recommends, not measured results.
| Telecom journey | What breaks | Pass criterion |
|---|---|---|
| Bill or usage inquiry | Agent returns a stale balance after a backend timeout | Balance matches the billing system of record on every run |
| SIM activation or swap | Activation reports success while the provisioning call failed | Activation confirmed against the provisioning response, not the agent's own summary |
| Plan change or upgrade | Agent quotes a plan the caller is not eligible for | Only eligible plans are offered, and the change is written once |
| Service outage | Agent misses a live outage and troubleshoots the handset instead | Outage identified and an ETA given without a device walkthrough |
| International roaming | Agent states the wrong country's policy | Policy matches the destination the caller actually named |
| Authentication failure | Agent proceeds past a failed identity check | The account stays protected and the call routes to a human |
| Transfer to a human | Handoff drops the call or loops back to the main menu | Transfer completes within policy and context carries over |
How does Cekura test telecom voice AI deployments?
Cekura sits on top of an existing telecom stack rather than replacing it, covering pre-production testing, infrastructure checks, and post-launch observability on the call path production traffic uses.
"Our agents are graphs, not prompts. Cekura is how we test each state and then end-to-end."
Nitish Poddar, CTO, Kastle
Cekura reaches that path through the SIP trunking its partner platforms use. Vapi's SIP trunking documentation describes a trunk as connecting "your internal PBX or VoIP system to a SIP provider", and LiveKit's telephony documentation states that "LiveKit trunks bridge your third-party SIP provider and LiveKit". Cekura's own benchmark run shows why the digit path needs its own scenarios: on Cekura Bench, Retell's example issue is a call where "The transcript captured a phone number correctly, but a different number was sent to the tool", and GPT Realtime's is a noisy-audio run where "a long pause was followed by lost digits and a skipped tool action". Both are digit failures a transcript-only check scores clean.
FAQ
What audio codec should a telecom voice agent be tested on?
Cekura tests telecom voice agents on narrowband G.711, the codec real PSTN calls use, not only the wideband audio a browser demo runs on. Cekura's telephony testing guide states that "PSTN calls ride on a narrowband G.711 codec that samples at 8 kHz and passes only 300 to 3400 Hz", and separately records an agent that read a caller's digits back wrong once the same call dropped to narrowband.
What latency budget does a telecom voice AI agent have?
Cekura measures telecom voice agents against ITU-T G.114, the ITU recommendation on one-way transmission time. Cekura's telephony testing guide reports that it "puts the preferred ceiling for one-way, mouth-to-ear delay at 150 ms", that between 150 and 400 ms quality degrades, and that past 400 ms "a conversation falls apart". Cekura scores the agent's own processing against what carrier transport has already spent, not against 150 ms in isolation.
How many concurrent calls should a telecom voice AI deployment be load tested at?
Cekura load tests telecom voice agents at expected peak concurrency rather than one call at a time, ramping from a baseline in steps to find where latency and error rates break. Cekura's telephony testing guide records an agent that handled 5 test calls cleanly and "degraded badly at 25 concurrent calls, when a provider rate limit started queuing responses, and latency tripled", well below any figure the deployment would have called its capacity.
Does DTMF need to be tested separately from speech?
Yes. Cekura tests DTMF separately from speech because the two paths fail independently. Cekura's telephony testing guide records an agent that asked a caller to enter a 9-digit member ID and "captured the speech fine and ignored the keypad tones, so every caller who typed instead of speaking hit a dead end". Cekura injects digit sequences for PINs, account numbers, and transfers, then confirms the agent routed or recorded each one correctly.
What packet loss or jitter breaks a telecom voice AI call?
Cekura tests telecom voice agents under real network impairment rather than a clean lab line. Cekura's VoIP testing guide reports "Jitter above 100ms" exhausting the jitter buffers voice traffic depends on and "Packet loss at even 1%" corrupting call quality. The same guide puts a Mean Opinion Score of "4.0 or above" at good for VoIP, with 3.5 the minimum most teams set, below which comprehension is noticeably affected.
