Knowledge infrastructure
for the AI era.
Gamgee builds hypergraph-based context infrastructure that models complex relationships across your data, so AI can reason over structure instead of just retrieving documents.
The context problem.
AI is only as good as the context it receives. Most context is flat.
Flat data, flat answers
Tables, documents, and embeddings capture facts but lose the relationships between them. Your AI can retrieve, but it can't reason.
Hallucination isn't random
When AI hallucinates, it's filling gaps where structural context is missing. Without a map of how things connect, it guesses.
RAG has a ceiling
Retrieval-augmented generation gets you most of the way. The last mile, where multi-way relationships matter, needs a different architecture.
How Gamgee works.
From raw data to structured knowledge, delivered to your AI.
Model your ontology
Define the entities and relationships that matter to your domain. Gamgee uses hypergraphs to model complex, multi-way relationships that traditional graphs can't represent.
Map your data
Connect your data sources. Gamgee maps raw data into your ontology automatically, building a knowledge graph with full provenance tracking.
Deliver context to AI
Your AI applications query the knowledge graph for structured context. Better context means fewer hallucinations, more accurate reasoning, and auditable answers.
Why Gamgee.
Built by people who know what production looks like.
Enterprise DNA
Our team built data systems at JP Morgan, Slice, and Auquan. We know what enterprise compliance, scale, and reliability require.
Security-first
SOC 2 TYPE II compliant and ISO 27001 certified. Your data governance requirements are met from day one.
Production-tested
We've shipped analytics systems that run every day, not just in demos. Error recovery, sandboxed execution, and observability are all built in.
Open source foundation
Hypabase, our hypergraph engine, is open source. Our core technology is auditable and our roadmap is public.
Hypabase
Open-source hypergraph modeling in Python.
The engine behind Gamgee's knowledge infrastructure. Model relationships between any number of entities in a single edge, with provenance tracking built in. Built for knowledge graphs, ontologies, and AI systems that need to understand how things connect.
from hypabase import Hypabasehb = Hypabase("procurement.db")# One edge connecting four entitieshb.edge( ["acme_corp", "globex", "widget_x", "contract_2024"], type="procurement", source="erp_system", confidence=0.95, properties={"value": 1_500_000, "terms": "NET30"})# Query and traversehb.edges(containing=["acme_corp"], type="procurement")hb.paths("acme_corp", "widget_x")Built by engineers who've shipped.
Enterprise-hardened. Production-tested. Years building data and AI systems at JP Morgan, Slice, and Auquan.
