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yggy

Knowledge graphs built and kept up to date automatically

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A platform that automatically extracts, models, and updates knowledge graphs from documents and system exports, with sourced facts for enterprise applications

Overview

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The yggy homepage

yggy is a knowledge graph platform that automatically builds and maintains knowledge graphs from your documents and system exports. It handles extraction, entity modeling, duplicate resolution, and continuous updates while preserving source attribution for every fact.

The platform reads from ERP exports, SharePoint, OneDrive and Google Drive, building graphs with sources on every fact. Results are kept queryable and can run EU-hosted or on-premises. AI agents and people access the graph through the product, generated APIs, or MCP.

Each answer in the knowledge graph links back to its source document with supporting text and confidence values, enabling verification before use. The ontology is RDF-based with OWL 2 RL profile, and projects can export facts as JSON-LD or N-Quads with ontologies as OWL, SKOS and SHACL shapes.

Capabilities

  • Automatic extraction and modeling from documents and system exports
  • Source attribution on every fact with supporting text and confidence values
  • EU-hosted or on-premises deployment
  • Multi-hop query support for complex relationships
  • RDF-based ontology with OWL 2 RL profile
  • API and MCP access for AI agents
  • Continuous graph updates and duplicate resolution

Pricing

Pricing model: Contact for pricing

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yggy fact sheet

Community votes0
Websiteyggy.tech

yggy metrics

Vendor metrics for yggy.tech: Ahrefs Domain Rating and Majestic link data, with the delta since the last review.

Domain Rating
2
no change since last check
Trust Flow
6
no change since last check
Citation Flow
15
no change since last check
Referring domains
7
no change since last check
External backlinks
19
no change since last check

Vendor Domain Rating (Ahrefs) checked 6 Nov 2026, spec changed 25 Oct 2026. Vendor topic: Arts/Online Writing (6). Vendor Majestic data checked 25 Oct 2026, spec changed 25 Oct 2026. Link metrics supplied by Majestic. Trust Flow, Citation Flow and Topical Trust Flow are trademarks of Majestic-12 Ltd.

yggy vendor domain

Open the vendor record

We read this from the registry's RDAP record on 5 Nov 2026, 05:42 UTC.

Domain age
4 months
Registered
Expires
Registrar
Cloudflare, Inc.
DNSSEC
Not signed

Vendor domain report: yggy.tech

yggy post-launch report

Measured after yggy went live on SaaS Directory Pro on 25 October 2026.
MeasureAt launch, then latest
Domain Rating (Ahrefs)2 to 2 no change since launch
25 Oct 2026, then 6 Nov 2026
Trust Flow (Majestic)6
first measured 25 Oct 2026
Referring domains (Majestic)7
first measured 25 Oct 2026

Day 11 since launch. Next receipt: day 30, 24 Nov 2026. Many things move these figures, so a change is not proof of cause. Only stored readings are shown.

Biggest gains on SaaS Directory Pro

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Compare and explore

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Buyer questions

What is a knowledge graph as a service?

A knowledge graph as a service is a hosted or managed way to get a knowledge graph without building the pipeline yourself. yggy builds knowledge graphs automatically from your sources and keeps them current, reading from ERP exports, SharePoint, OneDrive and Google Drive, with a source on every fact. Agents access it through the product, generated APIs or MCP.

Is a knowledge graph better than RAG?

They solve different problems. RAG is a retrieval pattern that pulls relevant text into a model's prompt. A knowledge graph is a data structure with explicit entities and relations. RAG handles broad text well, while a graph handles questions that need several hops, exact matches, or an audit trail. Many systems use both.

What makes knowledge graphs relevant with language models?

Language models can write fluent answers, but cannot tell you which record the answer rests on. A graph gives models structured, checkable facts to work from, and gives reviewers something to verify before anything is sent. Agents answer from the graph with every claim carrying its sources.

Is RDF still used?

Yes. RDF underlies Wikidata, open government and life-science data, and schema.org markup. Its query language SPARQL is the standard for querying it. yggy's ontology is RDF-based with OWL 2 RL profile, and projects export facts as JSON-LD or N-Quads with ontologies as OWL, SKOS and SHACL shapes.

What is the purpose of a knowledge graph?

A knowledge graph stores things (parts, suppliers, contracts, people) and the relations between them in a form both people and software can query. Its purpose is to connect facts that sit in different systems, so you can ask a question across all of them and trace each answer back to where it came from. yggy builds the graph from your documents and system exports and keeps the source document, the supporting text and a confidence value on each fact, so an answer can be checked before anyone signs it.

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