Karta AI Raises $3 Mn To Automate Inbound Calls For BFSI Players

Agentic AI startup Karta AI has successfully raised $3 million in seed funding to automate inbound calls for financial services providers. The round, led by PeerCapital and Cedar Hill Capital, also saw participation from Wyser VC, CDM Capital, and Seedstars. Karta AI founder and CEO Vijaypal Singh and Abhishek Kushwaha, both former Razorpay executives, aim to expand their go-to-market strategy and product development over the next two years.

The company's omnichannel customer communication platform handles voice, chat, email, and WhatsApp interactions, primarily focusing on complex incoming queries that require coordination across multiple enterprise systems. Karta AI claims to manage around 2 million customer conversations monthly across over 25 languages, with 10-12 paying customers such as PhonePe, Mindlabs, Bajaj, and Aavas.

Their AI agents resolve approximately 70% of borrower queries end-to-end without human intervention, significantly reducing the cost per query by around 50%. These queries include loan disbursal updates, account statements, foreclosure statements, and property document releases. Karta AI targets level 2 and 3 queries, which involve interpreting multiple customer intents, retrieving information from different systems, and executing workflows.

The startup deploys its AI agents in customer servicing and retention workflows for financial institutions, focusing on inbound interactions where customers contact businesses to resolve issues like failed payments, blocked cards, loan-related queries, and insurance claims. Unlike outbound calls, inbound conversations can involve multiple queries that require AI agents to retrieve information and execute actions across several systems.

Karta AI deploys AI agents across customer servicing and retention workflows at financial institutions, focusing on inbound interactions. According to Singh, the platform has achieved 70-80% end-to-end automation in its inbound proof-of-concept deployments and has won all such PoCs. The focus on complex queries reflects their disproportionate cost, with L2 and L3 queries accounting for around 80% of customer service costs.