Enterprise Voice AI
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Right Voice AI.
200+ enterprises run on Gnani.ai — with proprietary models, full-stack deployment, and outcomes you can measure on day one.
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Buyer's Guide
How to Choose the Right Voice AI Platform
Most evaluations fail because teams optimize for demos, not deployment. Here's what actually separates platforms at enterprise scale.
01 — Start here
Does the vendor own their model stack — or just resell it?
Most voice AI vendors are wrappers around Google, Azure, or AWS speech APIs. That means you inherit their accuracy ceiling, their latency, their pricing changes, and their data terms. A proprietary stack — built on real telephonic audio in your target languages — is the only way to get accuracy that improves with your data, latency you can actually negotiate, and deployment options that fit your compliance posture.
Ask for a benchmark on 8kHz telephony audio02
Multilingual isn't a checkbox. It's an architecture decision.
Supporting 10 languages via separate models is very different from natively handling mid-sentence code-switching like Hinglish or Tanglish. Ask whether the platform was trained on code-mixed utterances — or whether it just routes between language models.
Test with real agent call recordings03
Latency at scale is not the same as latency in a demo.
A 300ms response time with 10 concurrent calls means nothing at 10,000. Ask for P95 latency numbers at peak load — not average latency on a controlled test.
P95 under 500ms end-to-end is the bar04
On-prem and air-gapped deployment should be a standard option, not a premium add-on.
For BFSI, government, and regulated industries, data residency is non-negotiable. If a vendor can't deploy inside your VPC or on your infrastructure within a week, that's an architectural constraint — not a timeline issue.
Verify ISO 27001, SOC2, and DPDPA alignment05
Integration depth determines how fast you go live.
Native connectors to your existing telephony stack (Avaya, Cisco, Genesys) and CRM (Salesforce, Zoho, ServiceNow) cut deployment time from months to days. Ask for a live integration walkthrough, not a slide listing logos.
Target under 1 week to first live call06
Outcomes on paper versus outcomes in production.
Ask for a reference customer in your industry, at your call volume, with your language mix. Any vendor can show a pilot result. What you need is proof at 1M+ calls/month with measurable OpEx reduction you can verify independently.
Request a live client reference call07
Voice authentication should be built in, not bolted on.
If fraud prevention and identity verification are handled by a third-party add-on, you're adding latency, cost, and a compliance surface. Native voice biometrics with anti-spoofing — trained on your caller population — is a meaningful differentiator at scale.
Ask about deepfake and replay attack detection08
Commercial model should align with how you actually scale.
Per-minute pricing sounds simple until you're running 10M calls a month. Understand whether pricing is per minute, per concurrent session, or outcome-based — and model it against your actual usage curve before signing anything.
Model at 3x your current call volumePlatform Comparison
Not all Voice AI is built the same way.
The category you buy from determines what you can build, how fast you go live, and what happens when you need to scale. Here's how the four types compare on what actually matters.
| Evaluation Criteria |
gnani.ai
Full-Stack Sovereign AI
|
Foundational Voice AI
Indic-focused model labs
|
Voice AI Orchestrators
Third-party model wrappers
|
Call Analytics Platforms
Post-call intelligence only
|
|---|---|---|---|---|
| Model Ownership | ||||
|
Proprietary STT + TTS
Owns the model, not just the API
|
Yes Full STT + TTS + LLM stack |
Partial STT only, limited TTS |
No Resells Google / Azure |
No Analytics layer only |
|
Trained on Telephony Audio
8kHz real-world call data, not studio recordings
|
14M+ hours of telephonic audio | Partial | No | Partial |
|
Word Error Rate — Indic languages
Independent benchmark, 8kHz telephony
|
20.3% WER, 15pt lead on closest rival | 35.5% WER (independent benchmark) |
Varies, vendor-dependent |
Not applicable |
| Deployment | ||||
|
On-Prem / Air-Gapped Deploy
Full data residency inside your infra
|
Yes Cloud / On-Prem / Hybrid / K8S |
No | No | Partial |
|
Time to First Live Call
From contract to production
|
< 1 week to production with 100+ integrations | 4 to 8 weeks |
2 to 6 weeks |
2 to 4 weeks |
| Scale & Reliability | ||||
|
Daily Call Capacity
Proven at enterprise scale
|
10M+ calls/day, 30K concurrent | Not enterprise-grade |
Depends on upstream API limits |
Recording-only pipelines |
|
End-to-End Latency (P95)
At peak production load
|
<500ms P95 end-to-end | 500ms to 1.2s |
800ms to 2s API chaining overhead |
Post-call only |
| Capabilities | ||||
|
Native Voice Biometrics
Built-in, not a third-party add-on
|
Yes Anti-spoofing + deepfake detection |
No | No | Partial |
|
Multilingual Code-Switching
Hinglish, Tanglish, mid-sentence
|
Yes 40+ languages, native code-mix |
Partial Single-language models only |
Partial | No |
|
Agentic AI, No-Code Builder
Deploy voice agents without engineering
|
Yes Agent live in <5 minutes |
No | Partial | No |
| Compliance & Sovereignty | ||||
|
Regulatory Compliance
Certified for regulated industries
|
Yes ISO 27001, SOC2, HIPAA, PCI DSS, GDPR |
Limited |
Depends on upstream vendor |
Varies |
|
Sovereign AI Infrastructure
Selected under national AI programme
|
Yes IndiaAI Mission, 1 of 4 selected |
Partial | No | No |
* Competitor data based on publicly available benchmarks and product documentation as of Q1 2026.
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