GreyLabs AI raises Rs 85 crore from Elevation Capital, Z47; launches voice agent platform for BFSI

GreyLabs AI raises Rs 85 crore to scale voice AI for contact centres in BFSI

GreyLabs AI has secured Rs 85 crore (about $10 million) in new funding led by Elevation Capital, with participation from existing investor Z47 (formerly Matrix Partners India) and a group of angels. The Mumbai-based company also launched a voice AI agent platform focused on automating sales, service, collections, renewals, and verification calls for financial institutions.

GreyLabs works with 50+ BFSI clients, including RBL Bank, AU Bank, IDFC First Bank, Axis Finance, Motilal Oswal, SBI Life Insurance, Piramal Finance, ICICI Prudential Life Insurance, and Groww. The company previously raised $1.5 million and currently employs 44 people.

What's new: voice agents built to cover the entire call journey

The platform aims to fully automate contact centre workflows while addressing common problems: long wait times, incorrect pitches, and harassment in collections. It's positioned as an AI-based contact centre stack that can be expanded across lines of business as coverage and accuracy improve.

Co-founder Aman Goel highlighted a key operational gap: most banks audit a tiny fraction of calls-often just a handful per agent per month-leaving the majority unchecked. That gap increases the risk of mis-selling, unresolved issues, and compliance breaches.

Why this matters for finance leaders

• Risk control: Move from sample-based QA to near-full coverage, reducing mis-selling and collections misconduct exposure. See supervisory expectations and circulars from the Reserve Bank of India for context.
• Unit economics: Improve call containment and first-contact resolution to lower cost per resolution and recoveries OPEX.
• Customer outcomes: Shorter wait times and consistent scripts can lift NPS/CSAT and reduce escalations.
• Auditability: Structured transcripts, consent capture, and automated redaction strengthen evidence trails for internal audit and regulators.

What to ask before you pilot

• Containment and handoff: What's the autonomous resolution rate by use case (sales, service, collections, renewals, verifications)? How seamless is escalation to a human agent with full context?
• Compliance and QA: Can the system flag mis-selling, abusive language, and missing disclosures in real time? What's the accuracy (precision/recall) on each policy rule?
• Security and data residency: PII redaction, encryption, audit logs, and in-country deployment options. Certifications and controls relevant to BFSI.
• Language and accents: Coverage across Indian languages and regional speech patterns. Ability to fine-tune on institution-specific terminology.
• Integration: CRM/core banking/LOS/LMS connectors; support for SIP, CPaaS, and existing call recording systems.
• Reporting: Supervisor dashboards with QA coverage, dispute analytics, collections outcomes, and root-cause insights.
• Operations: Target SLAs (latency, uptime), fallbacks, and playbooks for peak volumes.
• Cost model: Pricing per minute/seat/conversation; projected impact on cost per resolution and recovery rate.

How to frame the ROI

• Baseline today: cost per call, first-contact resolution, average handling time, abandonment, QA coverage %, complaint rate, recoveries per agent.
• Pilot targets: lift in containment and FCR, reduction in AHT and escalations, increase in QA coverage, and compliance incident reduction.
• Translate to rupees: savings from lower manual QA load, reduced rework/chargebacks, fewer penalties/complaints, and higher collections throughput.

Client traction and focus areas

GreyLabs reports active work with more than 50 BFSI institutions, spanning banks, NBFCs, insurers, and brokerages. Priority use cases include tele-sales accuracy, collections conduct, renewal workflows, and KYC/verifications-areas with measurable compliance and P&L impact.

Competitive context

Competitors cited include Observe.AI, Uniphore, and Mihup. Expect differentiation on language coverage, real-time compliance, automation depth, deployment flexibility, and integration speed.

What's next for GreyLabs

The company plans to expand into a broader AI-based contact centre stack, grow its customer base to 300+ organisations, and open regional offices in Bengaluru and Delhi to support on-ground teams. Hiring is set to increase across regional functions.

Practical next steps

• Identify one high-value call flow (e.g., renewals or collections) and run a 6-8 week sandbox with strict success metrics.
• Involve compliance early to codify scripts, disclosures, and do-not-call rules into the policy engine.
• Set up an A/B design: AI-led calls vs. human-led calls for a clean comparison on costs, outcomes, and complaint rates.
• Plan change management: agent training for escalations, new QA dashboards, and refreshed SOPs.

Further resources

• Explore curated AI tools for finance: Complete AI Training - AI tools for finance

Investor perspective: "Over the past year, we've seen GreyLabs AI go from a bold vision to a product already delivering measurable outcomes for some of the largest players in BFSI. What stood out was the team's clarity, pace of execution, and deep understanding of this space," said Vaas Bhaskar, partner at Elevation Capital.