
Pushes for the next step on every call: qualification, callback, or close. Doesn't resolve and end.
Built for short transactional flows: appointment confirmations, KYC verification, delivery updates.
Holds 30+ turns of objection handling without losing the thread or repeating itself.
Voice rhythm tuned for one-touch interactions, not multi-turn negotiation.
Adapts mid-call when leads switch between Hindi, English, or regional dialects.
Limited adaptation when leads push back, stall, or change the topic.
Reads the lead's CRM history in the first second, opens calibrated to who they are.
Generic voice library, no lead-specific personalization.
Calm under pushback. Negotiates instead of escalating.
Call ends when the workflow ends.



























Capabilities
SquadStack.ai
Ringg.ai
Focus
SquadStack.ai:
Sales conversion at scale for India's consumer brands across BFSI, EdTech, E-Commerce, Travel, Healthcare, Automotive, and Logistics
Ringg.ai:
SMEs and mid-market businesses
Scale
SquadStack.ai:
40 lakh+ AI sales calls daily
Ringg.ai:
~50K calls daily across all use cases
Speech Models
SquadStack.ai:
Proprietary in-house STT and TTS, trained on 5M+ hours of real Indian sales conversations across 6+ languages
Ringg.ai:
Orchestration platform combining third-party STT, TTS, and LLM APIs
POC Success Rate
SquadStack.ai:
92%
Ringg.ai:
No published POC success rate on sales conversion campaigns
What you get
SquadStack.ai:
A full sales engine: lead manager, voice agent, channels, A/B testing, supervisor, ROI optimizer, all owned by a forward-deployed team
Ringg.ai:
A no-code orchestration platform with third-party speech models wired together
Performance
SquadStack.ai:
90% connectivity, 40% more conversions, 3x lower CAC vs human agents
Ringg.ai:
Customer's team owns the performance outcome on top of Ringg's APIs
A/B testing
SquadStack.ai:
Built-in controlled experiments on voice, prompt, channel sequence, and timing through the Optimize engine
Ringg.ai:
No A/B testing engine
Long Conversations
SquadStack.ai:
No drift on even 30-turn complex sales calls
Ringg.ai:
Built for short transactional workflows like confirmations, verifications, and scheduling
Compliance
SquadStack.ai:
ISO 27001, ISO 27701, SOC 2 Type II, DPDP, TRAI, with RBI disclosure scripts and IRDAI alignment for BFSI deployments
Ringg.ai:
SOC 2, ISO 27001
Best Fit For
SquadStack.ai:
Enterprises optimizing sales conversion at scale, where conversion lift and CAC reduction are contracted KPIs
Ringg.ai:
Mid-market businesses deploying voice agents quickly across light transactional use cases
You're driving sales conversion at scale for India's consumer brands.
You need voice AI trained on real Indian sales conversations across Hinglish, Tanglish, and regional code-mixing.
Your KPIs are conversion lift, CAC reduction, and connectivity.
You need to handle complex sales conversations, cross-channel.
You want a team of forward-deployed engineers owning your business metrics.
You're a mid-market business deploying voice agents with limited engineering bandwidth.
Your use cases are light and transactional, like appointment scheduling, KYC.
You need broad multilingual support across Arabic, Spanish, French, German.
Your KPIs are operational efficiency and call automation rate.

For sales conversion at enterprise scale, yes. For broad multilingual voice automation across light transactional use cases like appointment scheduling, delivery confirmations, and KYC, Ringg is built for that broader job. The honest question is which job the platform is being hired for. Ringg is a no-code orchestration layer for SMEs and mid-market companies to ship voice agents fast. SquadStack runs 40 lakh AI sales calls daily for India's biggest consumer brands, with conversion as the contracted KPI.
For light, transactional use cases like appointment scheduling, delivery confirmations, and KYC verification, the answer might be yes. SMEs and mid-market companies that need a voice agent shipped this quarter without engineering effort are Ringg's natural buyer. SquadStack is built for a different buyer: enterprises where sales conversion is a contracted business outcome. The forward-deployed team owns the agent build, the campaign optimization, and the conversion metric on behalf of the customer. The buyer is a CRO or VP of Sales, not an ops manager scoping a no-code tool.
The POC runs against your actual sales KPIs: connectivity, qualified leads, conversion rate, and CAC. SquadStack runs on the customer's real campaign data and current baseline, with conversion as the contracted KPI. POC success rate has been 92% (23 of 25 structured evaluations) against an industry average of 50-60%. The deciding factor is usually architectural: an orchestrator with no proprietary speech models cannot match the WER, latency, and naturalness of a stack where every layer is built for Indian sales conversations. On the SquadStack stack, every layer compounds: better STT means better lead intent extraction, better LLM context handling, better TTS persuasion, better outcome data flywheel. On an orchestration platform, the model-quality ceiling is set by whichever third-party API the platform routes to.
Three architectural differences. First, model ownership: SquadStack builds its own STT trained on 5M+ hours of real Indian sales calls, its own TTS with 1000+ cloned Indian agent voices, and runs proprietary fine-tuning on the LLM layer. Ringg orchestrates third-party speech models from global providers. Second, data moat: SquadStack's India Interaction Graph carries 100M consumer profiles and 400M+ prior interactions, so 30% of new leads land warm. Ringg has no equivalent data layer. Third, target outcome: SquadStack contracts on sales conversion lift, with documented 1.5-2x conversion improvement and 2-3x CAC reduction. Ringg's published metric is "77% of interactions automated without human intervention," which measures call automation rate, not revenue impact.
Both platforms have logo overlap with marquee Indian brands. The difference shows up in deal size, expansion pattern, and outcome ownership. SquadStack lighthouse accounts include IndiaMART (₹74L+ MRR, 6 use cases live, won head-to-head against Sarvam, Origa, Hampam, Gnani), BankBazaar ($1M ARR in 3 months across 3 bank partners, won against Gupshup and Gnani), JustDial (1.5x conversion lift, won against Sarvam, GreyLabs, Convin, Convozen), and Shiprocket. SquadStack expanded with Shiprocket after winning a head-to-head structured evaluation, today running 5x seller identification vs the prior baseline. The deal pattern is land at $100K+ ARR and expand to $500K-$1M+ within 6-12 months, contracted on outcome metrics like conversion rate and CAC, not call automation rate.