DocVerify AI — Intelligent Document Compliance Validation on AWS

What It Does DocVerify AI is a serverless, agentic document quality assurance platform built on Amazon Bedrock AgentCore Runtime and the AWS Strands SDK. It integrates into existing document intake workflows via a simple API call, processes submissions asynchronously through a multi‑stage validation pipeline, and returns a structured pass/fail verdict—with per‑page reasoning and evidence—to the caller’s private endpoint. No polling loops, no manual review queues, and no human intervention for standard document types. Who It’s For Organizations that process high volumes of documents requiring strict structural and authorization validation before acceptance or downstream processing. Representative use cases include:

Banking & Financial Services: Loan origination packages, KYC/AML onboarding dossiers, trade finance documents, and mortgage application bundles—verifying completeness, page order integrity, and customer authorization marks prior to underwriting or compliance review Insurance: Policy issuance documents, claims submissions, and reinsurance treaty packages—ensuring required sections are present, correctly sequenced, and authorized by all necessary parties Telecom, Media & Technology: Vendor contracts, partner agreements, and regulatory license filings—automating structural QA before legal or compliance review Retail & Manufacturing: Supplier compliance certificates, quality audit reports, and trade documentation—detecting incomplete or mis‑assembled submissions at ingestion before they reach downstream systems

How It Works DocVerify AI executes validation using a Directed Acyclic Graph (DAG) model:

Cache Lookup: Each submission is fingerprinted using a content hash and active Ruleset Version ID. Matching prior results return instantly at zero inference cost. Rule updates automatically increment the version ID, ensuring documents are revalidated when required. Structural Pre‑Screening: An AWS Lambda function renders each page to high‑resolution images and performs pixel‑density analysis to detect blank pages. Documents containing empty pages fail early without invoking AI inference. Parallel AI Analysis: For pages that pass pre‑screening, Amazon Bedrock is invoked via the Converse API using S3 URI pointers (Zero‑Copy pattern). Page sequence verification, authorization mark verification, and image quality screening run concurrently. Rules‑Based Aggregation: A Strands Agent applies deterministic business logic to per‑page results, producing an overall verdict with structured failure reasons, page references, and spatial evidence. Audit Record & Delivery: Final validation reports are written to Amazon DynamoDB as immutable, timestamped records and delivered via HTTP POST to the caller’s private VPC webhook.

Key Differentiators

Vision‑native reasoning: Multimodal LLM analysis handles handwritten marks, non‑standard layouts, rotated pages, and variable scan quality without template maintenance Zero‑Copy architecture: Document images are accessed directly from Amazon S3 by Bedrock—content never transits application servers or leaves the customer’s AWS account boundary Ruleset‑aware caching: Automatic revalidation when rules change, without manual cache management Audit‑first design: Page‑level decisions stored immutably in DynamoDB with spatial evidence and coverage ratios, supporting SOX, GDPR, Basel III, and ISO/IEC 27001 readiness Cost‑efficient and elastic: Early pre‑screening avoids unnecessary inference costs; parallel execution enables full‑document validation in under 60 seconds

What’s Included

Production‑ready Strands Agent with four configurable validation modules: structural pre‑screening, page sequence verification, authorization mark verification, and image quality screening Terraform infrastructure‑as‑code for full AWS deployment Configurable validation ruleset templates with chain‑of‑thought hallucination mitigation patterns Structured JSON output schema with per‑page, per‑check results, spatial coordinates, and coverage ratios CloudWatch dashboards and AWS X‑Ray tracing for end‑to‑end observability