"Ringg handled our pilots well. The moment we pushed volume, latency crept up and model accuracy fell on regional accents. Gnani was the only platform that held up without changes to our infra."
"Our compliance team flagged the vendor-assembled model stack during audit. With Gnani, data stays in India, the stack is fully owned, and we cleared the review without a single exception."
"We outgrew Ringg's architecture faster than we expected. Gnani's infra was already running at this load for other enterprises. There was no ramp-up, no instability. It just worked."
Ringg stitches together third-party ASR, TTS, and LLMs from external providers. You inherit every upstream vendor's accuracy ceiling, latency floor, and pricing changes. When any one model breaks, your calls break.
Ringg's current production scale is approximately 30x smaller than Gnani's. The infrastructure was optimised for low-to-mid volume deployments. At enterprise concurrency, latency climbs and reliability drops.
Without a model trained on 14M+ hours of Indic telephony data, Ringg cannot match accuracy on real-world Indian accents, code-switching, or noisy 8kHz call recordings. Global models adapted to India are not the same as models built for India.
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Criteria
At enterprise scale, in production
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Recommended
Gnani.ai
Full-stack sovereign voice AI, India-built, in production at scale
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Ringg AI
Voice AI orchestrator that wraps third-party models
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| Model OwnershipASR, TTS, LLM stack | ✓Full proprietary stackASR, TTS, VAD, diarization, orchestration: all owned | ✗Orchestration layer onlyWraps third-party ASR, TTS, and LLMs; no proprietary models |
| STT Word Error RateKathbath Noisy 8kHz, Indic languages | ✓17.5%Best in 8 of 9 languages; trained on 14M+ hours of Indic telephony | ✗No proprietary benchmarkDependent on upstream STT provider accuracy |
| Daily Call CapacityProven production volume | ✓10M+ calls/day30K concurrent; stress-tested for continuous high-concurrency loads | ✗Limited by upstream API rate limitsApprox. 30x smaller production scale; optimised for low-to-mid volume |
| End-to-End Latency P95At peak production load | ✓<500ms P95Full pipeline; no vendor chaining overhead | ✗800ms to 2sAPI chaining overhead from multiple third-party providers |
| Native Code-SwitchingHinglish, Tanglish, Tamlish mid-sentence | ✓40+ languagesNative training on code-mixed Indic audio; no routing required | ◑PartialDependent on upstream model capabilities |
| On-Prem / Air-GappedFull data residency | ✓YesCloud / On-Prem / Hybrid / K8S; data stays in India | ✗NoCloud only; data passes through third-party vendor infra |
| IndiaAI MissionGovernment-backed sovereign AI programme | ✓SelectedAmong the first companies selected by IndiaAI Mission | ✗NoNot selected; orchestration model not aligned with sovereign AI criteria |
| Compliance CertificationsFor regulated industries | ✓Full stackISO 27001, SOC2, HIPAA, PCI DSS, GDPR | ◑PartialLimited disclosures; dependent on upstream vendor certifications |
| Telephony IntegrationAvaya, Cisco, Genesys, Twilio | ✓100+ nativeDeep workflow orchestration; CRM, dialers, CCaaS platforms out of the box | ◑Limited connectorsAPI-first; lighter integrations; custom engineering required for enterprise stacks |
| Observability and AnalyticsCall intelligence and continuous improvement | ✓Full suiteInya Insights, Assist, Shield: real-time intent detection, churn signals, model retraining loop | ✗LimitedNo proprietary analytics layer; dependent on upstream provider reporting |
| Cost at ScaleMarginal cost per minute | ✓Lower at volumeModel ownership eliminates third-party API markups; predictable enterprise economics | ✗Per-call margin leakageEvery call carries upstream vendor costs; pricing exposure increases with scale |
Benchmarked on Kathbath Noisy 8kHz telephony audio. Data sourced from public benchmarks and product documentation, Q1 2026.
Send us a sample of your real recordings. We will run a live benchmark on your audio, in your languages, and show you exactly where the gap is.
