AI Solutions · Knowledge Graph
Connected intelligence for intellectual property.
Link patents, technologies, products, competitors, standards and internal knowledge into one reasoning layer your teams and AI systems can query.
Direct answer
What is an IP knowledge graph?
An IP knowledge graph is a connected data layer that links patents, technologies, products, competitors, standards, research and organizational documents as relationships rather than isolated records. Because context is explicit, both analysts and AI systems can reason over it — answering questions about coverage, exposure, overlap and opportunity structurally instead of rebuilding the picture manually each time.
Entities in the graph
Build sequence
Ingest
Patent data, literature, standards, product and internal sources.
Resolve
Entity resolution, ownership normalisation, family linking.
Connect
Relationships between patents, products, technologies and competitors.
Enrich
Classification, technical tagging and expert curation.
Reason
AI and agents query the graph for grounded answers.
Decide
Coverage, risk, licensing and R&D decisions with traceable evidence.
What we build
- Patent knowledge graphs
- Technology knowledge graphs
- Competitive intelligence graphs
- Product-to-patent relationship mapping
- Standards-to-patent linkage
- R&D and innovation knowledge mapping
- Entity resolution and ownership normalisation
- Enterprise IP knowledge systems
- Graph-grounded AI question answering
What it unlocks
- Which patents cover which products
- Where coverage gaps exist
- Competitor technology convergence
- Licensing and monetization targets
- Portfolio pruning decisions
- Standards essentiality context
- Faster diligence and reporting
- Institutional memory that survives turnover
Built for organizations with real IP complexity.
Connected capabilities
Agents become substantially more capable once they can traverse an explicit relationship layer instead of searching text.
Agentic AI for IPThe graph is the substrate; intelligence programmes are what your leadership reads from it each quarter.
AI-Powered IP IntelligenceSearch and analytics supply the validated evidence that populates and continually refreshes the graph.
AI Patent Search & AnalyticsOur long-term vision is an AI-native IP knowledge infrastructure — read how we are building toward it.
About Hashi IP SolutionsTalk to us
Build the layer your AI will reason over.
We scope one domain first — a product line or technology area — prove the queries it answers, then extend across the organization.
FAQ
Questions we hear from IP and technology teams.
What is a knowledge graph?
A knowledge graph stores information as entities and the relationships between them, rather than as isolated rows or documents. It lets both people and AI systems traverse context — how one thing relates to another — instead of searching text in isolation.
What is an IP knowledge graph?
An IP knowledge graph connects patents, technologies, products, competitors, standards, research and internal organizational knowledge into a single relationship layer, so questions about ownership, exposure, coverage and opportunity can be answered structurally.
How does a knowledge graph help IP teams?
It removes the manual reconstruction of context. Instead of rebuilding the picture for every project, teams query relationships directly — which patents cover which products, which competitors overlap, where coverage gaps sit.
How is it different from a patent database?
A database returns matching records. A knowledge graph returns connections: the graph knows that a patent belongs to a family, maps to a product line, cites a standard and is owned by an entity that is a subsidiary of a competitor.
Can a knowledge graph include internal company data?
Yes. Invention disclosures, product specifications, R&D documentation, agreements and prior project outputs can be linked alongside external patent data, under client-controlled access.
Does the knowledge graph improve AI accuracy?
Yes. Grounding AI in an explicit relationship layer reduces hallucination and improves reasoning quality, because answers are traced to connected, verifiable entities.
What questions can it answer?
Which patents protect this product; which competitors are converging on this technology; where do we have coverage gaps; which assets are candidates for licensing; what is our exposure in a target market.
How long does it take to build?
A focused graph covering one technology area or product line can be stood up in weeks. Enterprise-wide graphs are built incrementally, domain by domain.
Who owns the data in the graph?
The client owns their data. Hashi builds and maintains the graph structure and enrichment under agreed confidentiality and access controls.
Is this the future of IP management?
We believe so. As AI systems take on more IP work, the differentiator becomes the quality of the connected knowledge they reason over — organizations that build that layer now compound the advantage.