Entity-resolved knowledge graphs —
clean data in, clean graph out
A knowledge graph is only as good as the entities inside it. Tilores resolves duplicate records before data enters your graph database — so every node represents a real, unique entity, and every relationship is trustworthy.
A standard knowledge graph maps relationships between data nodes. But if the same real-world entity — a company, a person, a supplier — exists as multiple duplicate nodes due to inconsistent source data, the graph produces distorted results. Relationships are split, counts are wrong, and downstream AI systems reason on a corrupted picture.
An entity-resolved knowledge graph solves this at the source. Before data is ingested, Tilores identifies which records refer to the same real entity, merges them into a single canonical node, and preserves all source attributes. The graph database receives clean, deduplicated input — and the insights it surfaces are reliable.
Tilores as the pre-ingestion layer
Ingest records from CRMs, databases, data warehouses, or any structured source via the Tilores API.
Tilores matches and deduplicates records using fuzzy matching — handling name variants, typos, and format differences.
Each resolved entity becomes a single, enriched Golden Record with all source attributes preserved.
Clean, deduplicated entities are ingested into Neo4j, Amazon Neptune, TigerGraph, or your graph database of choice.
Works with leading graph databases
The most widely deployed graph database. Tilores-resolved entities load cleanly via the Neo4j driver or Cypher import.
AWS-native graph database for property graphs and RDF. Pairs naturally with Tilores on the AWS stack.
High-performance graph analytics at scale. Tilores ensures the underlying entities are as accurate as the graph patterns you're querying.
Tilores outputs standard JSON entities — compatible with any graph database that accepts structured node data.
Why entity resolution matters for knowledge graphs
Eliminate duplicate nodes before they corrupt your graph — not after
Surface hidden relationship patterns that split nodes obscure
Improve the accuracy of graph-based AI, fraud detection, and risk models
Reduce manual data cleaning effort across large, multi-source datasets
Maintain a continuously updated entity layer as new source data arrives
Full audit trail on every entity resolution decision — traceable and explainable
Other use cases
Build knowledge graphs on
entities you can trust
Talk to us about entity resolution as a pre-ingestion step for your graph database. Available on AWS Marketplace.