Elevation Capital Leads INR 85 Cr Series A In GreyLabs AI For BFSI Contact Centre Automation

GreyLabs AI Raises INR 85 Cr To Scale Voice Agents And Speech Analytics For Contact Centers

06 Oct '25 - GreyLabs AI has raised INR 85 Cr (about $10 Mn) in a Series A round led by Elevation Capital, with participation from Z47 (formerly Matrix Partners India) and angel investors. The funding will be used to expand the tech stack, grow its customer base, open regional offices, and strengthen on-ground client support.

Founded in 2023 by Aman Goel, Harshita Srivastava, Shivam Gupta, Raj Sanghavi, Debabrata Basak, and Shreyas Patel, GreyLabs AI focuses on automating contact center operations for financial institutions. Its product suite includes agentic voice agents and speech analytics to cut handle time, increase containment, and streamline quality assurance.

GreyLabs AI says it serves 50+ BFSI clients, including RBL Bank, AU Bank, IDFC FIRST Bank, Groww, and Axis Finance. The company plans to scale to 300 customers and expand its regional presence.

Why this matters for customer support leaders

• L1 volume offload: Agentic voice agents can handle high-frequency queries (balance, status checks, KYC, repayment reminders) and route only complex cases to humans.
• QA at scale: Speech analytics enables 100% call coverage, automated scoring, and pinpointed coaching moments, replacing random sampling.
• Compliance and risk: BFSI-grade deployments suggest stronger controls across consent capture, PII redaction, audit trails, and policy adherence.
• Operational stability: On-ground client support can cut rollout friction across telephony, CRM, and policy integrations.
• Vendor maturity: A larger customer base and fresh capital typically translate into faster roadmaps and better SLAs.

Quick actions to consider

• Prioritize use cases: Start with 3-5 intents that form 30-40% of call volume and have clear policies (e.g., status, due dates, FAQs).
• Check integration readiness: Confirm connectors for your telephony stack, CRM/ticketing, knowledge base, and identity systems.
• Define redlines: Lock data governance rules (PII handling, retention, encryption) and approval flows for prompts and policies.
• Pilot with guardrails: Pick one queue, run an A/B pilot for 4-6 weeks, and cap traffic until KPIs stabilize.
• Upgrade QA workflows: Move from random audits to ML-driven scoring, with coachable snippets and agent-specific playbooks.
• Train the floor: Teach agents how the bot routes, how to "takeover" gracefully, and how to flag edge cases for retraining.
• Set a retraining cadence: Weekly reviews of failure modes, policy updates, and knowledge base gaps.
• Vendor checklist: Ask for latency benchmarks, language coverage, consent flows, redaction methods, and disaster recovery plans.

KPIs that matter

• Containment rate: % of calls fully resolved by the voice agent.
• AHT and queue time: Measure both bot and human changes.
• FCR and CSAT: Watch for uplift without policy violations.
• QA coverage and resolution: 100% scoring, time to close coaching loops, and trend of repeat errors.
• Compliance flags: Consent adherence, sensitive word detection, and redaction accuracy.

Market context

Investor interest in AI for support ops is strong. Recent raises include OnFinance ($4.2 Mn, pre-Series A), Scalekit ($5.5 Mn, seed), and Pascal AI Labs ($3.1 Mn, seed), alongside a new INR 200 Cr fund to back AI-native startups. India's GenAI ecosystem is projected to reach a $17 Bn opportunity by 2030, pushing more teams to automate repetitive service workflows and scale QA.

Where to skill up your team

• AI courses by job: Customer Support - practical programs to upskill agents, QA, and support ops.
• Certification in AI Automation - frameworks for safe rollouts, governance, and KPI tracking.

Bottom line: prepare your data, pick high-impact intents, and run a controlled pilot. The teams that standardize QA and policy governance now will scale automation with fewer surprises later.