How to Build a Queryable KYC Knowledge Graph From Unstructured Documents

A Memgraph Community Call demonstrated how Quark Labs transforms messy KYC and investor-services documents, including scanned PDFs, handwritten forms, and inconsistent layouts, into a queryable knowledge graph. The workflow connects to multiple document stores, extracts both Markdown for AI reasoning and JSON for structured processing, and maintains versioning, deduplication, privacy controls, and auditability, including air-gapped deployment options. JSON-derived entities and relationships are loaded into Memgraph to connect identities, documents, addresses, ownership details, transactions, and other facts across filings, while preserving provenance such as source document, segment, handwritten status, and ingestion timing. Natural-language queries can then retrieve graph-backed evidence spanning several documents rather than simply locating similar text, although answer-generation layers may still experience technical failures independently of the evidence graph. The presentation emphasized that reliable entity resolution is central to the approach, using multiple signals such as normalized names, passport numbers, IDs, and other identifiers to avoid incorrect merges, and noted that graphs can begin with common or generic domain entities and evolve into a fuller ontology as use cases develop.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.