Transform insurance claims, KYC records, and multimodal evidence into structured, AI-ready datasets for faster settlements, risk assessment, and fraud detection.
Diverse multimodal data from claims, KYC, fraud, and legal evidence.
Manual correlation of multimodal data is inefficient and unscalable.
Missing details in multimodal data extraction risk poor decision-making.
High data volumes make manual review inefficient and prone to delays.
Inconsistent interpretation creates compliance gaps, inefficiencies, and higher risk.
.avif)
Multimodal Data Ingestion & Preprocessing
Deep Multimodal Information Extraction
Cross-Modal Data Correlation & Knowledge Graph
Data Validation, Enrichment, Structuring
Human-in-the-Loop Continuous Learning
BFSI-specific pipelines, knowledge graph-driven, human-in-the-loop learning
Strong for structured layouts but fails with inconsistent formatting
Effective transcription, limited on context linking
Good robustness to noise but no visual correlation
Fast and generalizable but underperforms on fine-grained financial data
