The trusted layer for grounded AI.
Job to be done
Ground every agent, copilot, and LLM workflow in verified knowledge · with real citations and a complete audit trail.
What PYRAMYD does
Outcome stats
average annual loss per enterprise from AI hallucinations and ungrounded model outputs · the loss PYRAMYD's grounding layer is designed to prevent (EY Responsible AI Survey, 2025).
of organizations reported AI-related financial losses in 2025 · grounding every answer in the graph keeps PYRAMYD users out of that population (EY Responsible AI Survey, 2025).
of enterprise AI users have made at least one major business decision based on hallucinated AI content · entity-level citations let reviewers catch the error before it ships (Deloitte).
Why this matters · third-party evidence
MeSH
Medical Subject Headings · the controlled vocabulary the US National Library of Medicine has used since 1960 to index PubMed/MEDLINE. Proves typed taxonomies scale to billions of biomedical citations.
U.S. National Library of Medicine · nlm.nih.gov/mesh
EuroVoc
The European Union's multilingual thesaurus covering 24 official EU languages across 21 fields of EU policy. Proves graph-grounded taxonomies survive multilingual, multi-jurisdictional production use.
EU Publications Office · op.europa.eu/en/web/eu-vocabularies
SNOMED CT
Systematized Nomenclature of Medicine · Clinical Terms. 350K+ active concepts used in clinical decision support across 80+ countries. The reference example for production-grade biomedical knowledge graphs.
SNOMED International · snomed.org
schema.org
The cross-search-engine vocabulary co-developed by Google, Microsoft, Yahoo, and Yandex. Powers structured data on the open web · the same JSON-LD pattern PYRAMYD uses for Article 50 provenance blocks.
Schema.org Community Group · schema.org
How APEX grounds a single answer
Every APEX response goes through the same pipeline. Each stage is logged to the per-tenant audit trail and exportable as JSONL.
Supervisor agent reads the user's question, classifies the intent, and picks the right specialist agent (or composition of agents).
Tenant-scoped FK traversal returns the typed entity set relevant to the question · respects row-level security, never crosses tenant boundaries.
Specialist agents run in parallel, each calling the graph-grounded tools they need (getVendor, listReleases, getReviewSentiment, etc.).
Every claim in the response is linked back to its source node + source URL + retrieval timestamp.
Machine-readable AI-content marker injected into the response payload · supports EU AI Act Article 50(2) transparency obligations without changing the visible UX.
Natural-language response composed and streamed token-by-token to the client.
Caller, intent, tools called, sources cited, and acceptance state written to the immutable per-tenant audit log, exportable as JSONL.
Competitive benchmark
| Neo4j / Stardog / Ontotext | Knowledge graph infrastructure. Powerful primitives; you bring your own data, your own ontology, your own workflows. |
| Palantir | Enterprise data integration and operations. Bespoke ontologies per customer; multi-quarter deployments; no out-of-the-box software-vendor graph. |
| Microsoft Graph / Salesforce Data Cloud | Horizontal context layers tied to one platform vendor. Strong inside their ecosystem; not built around enterprise software intelligence. |
| PYRAMYD | Pre-populated enterprise-software knowledge base + MCP server + entity-level citations + SOC 2 / ISO / EU AI Act readiness. The grounding layer that keeps agentic AI projects alive. |
Gartner predicts over 40% of agentic AI projects will be cancelled by end of 2027 due to inadequate grounding and governance. PYRAMYD is the trusted context layer that keeps them accurate, traceable, and enterprise-ready · built on the same typed-graph pattern that already runs PubMed, EuroVoc, and SNOMED CT in production.
We'll show APEX answering a real competitive question grounded in the live Product Graph · with citations you can click.
