SquadStack.ai, an Indian voice AI platform, has shifted to outcome-based pricing for its sales agents, tying customer costs directly to revenue generated rather than minutes used. The model is live for banking and financial services clients and will expand to other sectors in the coming months, challenging the per-minute rates that dominate India's voice AI market.
Most voice AI vendors in India charge between ₹2 and ₹6 per minute. SquadStack argues this metric ignores what enterprises actually buy: loan disbursals, insurance renewals, and university enrolments. "Two rupees a minute is a trap. Voice AI being cheaper than humans is true, and it is the least interesting thing about it," said Apurv Agrawal, co-founder and CEO of SquadStack.ai. "Per-minute pricing turns voice AI into a BPO with better margins and pays vendors not to care whether the call is converted."
How the pricing model works
Under the new structure, customers pay a fixed base fee to run the AI agents plus a success fee tied to business outcomes. SquadStack maps the customer's sales funnel before deployment, establishes baseline metrics, and estimates the lift its agents can deliver. The split between fixed and success-based components varies by customer and use case.
For personal lending, SquadStack earns a fee on loans disbursed through AI-handled conversations, benchmarked against the disbursed amount. For credit cards, the fee links to cards issued. The company said it takes on this performance risk based on six years of running sales funnels for consumer brands in India.
Performance data from current deployments
SquadStack's AI agents run sales campaigns for DMI Finance, BankBazaar, IndiaMART, Kreditbee, AngelOne, and more than 60 other brands. DMI Finance has disbursed over ₹700 crore in loans through the platform, with disbursals 30% higher than those from human agents. BankBazaar engaged more than 8 crore leads while cutting its customer acquisition cost in half. IndiaMART saw 20% higher conversions at 15% lower cost across more than 2 crore buyer leads in the first year.
The company reports that 93% of its proofs of concept convert into full production deployments, compared with an industry average of around 25%. This track record underpins its willingness to link pricing to outcomes.
Outcome-based pricing has gained traction in the US for customer support, with companies such as Sierra, Intercom's Fin, Zendesk, and HubSpot charging based on resolved interactions. SquadStack said the economics differ for India's predominantly outbound sales market, where the value of a voice AI interaction ties more directly to revenue. This shift in pricing models reflects a broader trend in AI for Sales, where businesses increasingly demand measurable returns.
The economics of conversion versus cost-cutting
Agrawal framed the argument in revenue terms. "Cutting calling costs by 60% saves a lender about 2% of revenue; lifting conversion by 10-15% adds 10-15% of revenue," he said. AI agents can run thousands of experiments simultaneously across timing, language, messaging, and follow-ups - a scale of testing human teams cannot match.
The company is betting that pricing against business outcomes rather than call volumes will align its incentives more closely with enterprises deploying the technology. For sales professionals looking to understand how AI agents can fit into their workflows, the AI Learning Path for Sales Representatives offers practical guidance on integrating these tools.
Why this matters for sales professionals
When a vendor's revenue depends on your revenue, the relationship changes. Outcome-based pricing means the AI provider only succeeds when your team closes more loans, issues more cards, or converts more leads. This shifts the conversation from "how many calls can the AI make?" to "how much more can we sell?" For sales leaders evaluating voice AI, the metric to watch is no longer cost per minute - it is revenue per conversation.
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