Fintech / BFSI

Use Case / Problem Statement

A leading Singapore-based Insurtech operating at 50M-user scale was managing customer interactions across multiple channels — phone, WhatsApp, and email — without a centralized AI layer. The existing CRM was static: no real-time intent detection, no sentiment analysis, no call transcription, and no predictive renewal workflows.

• Agents had no visibility into customer mood or intent before or during a call
• Lead prioritization was manual and inconsistent across 200+ agents
• Renewal workflows were reactive, not proactive
• No systematic post-call analysis or performance feedback loop
• WhatsApp conversations weren't feeding into the CRM or scoring models

Solution & Development

1. Automated Lead Management — dynamic lead scoring and routing based on behavioral signals.
2. WhatsApp Integration — bi-directional messaging with intent and sentiment scoring.
3. Speech-to-Text — multilingual transcription for post-call analytics.
4. Intent Analysis Engine — classifies caller intent and routes to correct workflows.
5. Sentiment Analysis — real-time emotional trajectory scoring.
6. Next-Step Prediction — recommended follow-ups per lead.
7. Renewal Workflows & Smart Reminders — multi-touch renewal automation.
8. Advanced Lead Prioritization — high-value, at-risk, and lapsed segmentation.
9. Product Analytics & Continuous Feedback — 98.5% uptime infra with agent loops.

Use Case / Problem Statement

A PeakXV-backed lending company with 1,600+ agents was struggling with a core issue: the right leads weren't reaching the right agents at the right time. Their call and lead management system was rule-based — round-robin routing, no behavioral context, no dynamic scoring.

• No dynamic lead routing based on agent performance or lead intent
• Zero post-call analysis — no sentiment, escalation detection, or score update
• Retention outreach was manual and mistimed
• Cross-sell was opportunistic, not data-driven
• No AI assistance for agents during live calls

Solution & Development

• Smart Call Planning & Routing based on connect rates, lead score, and journey stage.
• Live Call Assistance with real-time next-action guidance and contextual scripts.
• Auto-Dialing with override to prevent lead fallthrough.
• AI-Driven Reshuffling Engine for pickup probability optimization.
• Post-Call Intelligence with sentiment, escalation flags, and score revision.
• Retention & Cross-Sell Engine for perfectly timed reconnects.

Use Case / Problem Statement

AMBAK needed a centralized AI intelligence layer — a “Central Brain” — to orchestrate decisions, workflows, and agent actions across multiple functional systems.

• Fragmented systems with no unified data or decision layer
• Agents operating without real-time intelligence or guidance
• No AI layer connecting lead data, product recommendations, and automation

Solution & Development

• Orchestration Layer: centralized decision routing across sub-agents.
• Agent Intelligence: next best action, product recommendations, checklist status.
• Workflow Automation: lead-to-disbursement automation.
• Data Unification: consolidated data layer across CRM and product systems.
• Multi-Agent Coordination: specialist agents for lead, docs, compliance, and communication.

Use Case / Problem Statement

Static lead scoring models failed to capture real-time behavioral shifts. High-value leads sat in the same queue as cold contacts, and routing remained FCFS or round-robin.

• Lead scoring was static — built on demographics, not behavior
• No re-score after each interaction
• High-value leads buried with cold contacts
• Routing ignored intent and agent capability
• Conversion opportunities lost due to stale prioritization

Solution & Development

• Dynamic Lead Scoring updated after every touchpoint.
• Contextual Intent Mapping across purchase-ready, nurture, and churn-risk states.
• Smart Routing Engine matching lead score × agent performance × specialization.

Use Case / Problem Statement

QA teams could audit only 3–5% of calls manually, creating compliance blind spots and reactive coaching.

• Manual sampling below 5% of call volume
• Script adherence checked inconsistently
• Coaching reactive, not predictive
• No system to detect training needs at scale

Solution & Development

• Automated call recording + multilingual transcription.
• AI script adherence scoring with deviation analysis.
• Composite call grading (content, sentiment, compliance).
• Quiz-based remediation for underperforming agents.

Use Case / Problem Statement

High rejection rates due to incomplete documentation and non-compliant forms caused rework, conversion leakage, and customer frustration.

• 17% application rejection rate
• Rejections discovered post-submission
• Inconsistent rejection remarks from insurers
• No guidance for agents to resolve rejections
• No aggregated view of rejection trends

Solution & Development

• NLP rejection analysis engine with plain-language guidance.
• Proactive rejection prevention via internal audit system.
• AI pre-submission review (OCR + STT) with automated checklists.

Use Case / Problem Statement

Manual processing of thousands of documents per week created delays, errors, and compliance risk.

• 350,000+ documents to process
• Slow, error-prone manual review
• No structured data extraction
• Compliance demanded accurate lineage
• Turnaround delays slowed loan approvals

Solution & Development

• Intelligent document classification by type.
• Structured extraction with OCR + LLM layer.
• Validation and cross-reference engine.
• Document lineage and audit trail with HITL for edge cases.