Quote Intake & Table of Benefits (ToB) Extraction
Zentis AI transforms quote document processing into a structured, validation-first intelligence pipeline.
Unstructured quotation documents introduce extraction risk and processing delays.
Insurance quotations often arrive as complex PDFs, scanned, mixed-language, multi-page, and inconsistently structured. Manual extraction of Tables of Benefits (ToB) is time-consuming and error-prone. Missing limits, misread percentages, or overlooked conditions create downstream errors and client dissatisfaction. OCR inconsistencies and interpretation risks further increase operational exposure.
Zentis AI applies schema-driven
document intelligence with
zero-inference extraction.
A Document Classification agent identifies PDF type (text, scanned,
mixed). An Extraction agent applies OCR only when required and
extracts the complete Table of Benefits exactly as written, without
inference or enrichment. A Validation agent enforces structured
schema rules, checks row-column consistency, validates numeric
limits and percentages, and flags uncertainties instead of guessing.
A Versioning agent maintains historical quote revisions. A PDF Generation
agent creates a clean, table-only standardized document. Finally,
a Data Delivery agent sends structured JSON and PDF outputs
directly into Malbazaar systems.
The result is deterministic extraction, zero hallucination, and full traceability back to the originating RFQ.
Expected Impact
50% faster RFQ turnaround cycles
Reduced manual tracking and email dependency
Improved response rates through automated reminders
Complete auditability and traceability
Deliver structured, timely, and controlled broker negotiations
Witness Agentic Intelligence in Action
