Addressing a Critical Barrier to AI Adoption

The lack of reliable context has emerged as a key challenge for enterprise-grade AI applications. Startup Lovelace is tackling this problem with Elemental, an AI-powered platform designed to build knowledge graphs that are faster, cheaper, and more accurate than existing solutions.

How Elemental Works

Elemental helps ground large language models (LLMs) in verifiable information while providing full audit trails for decision-making. The system creates knowledge graphs from customer data, automatically identifying entities, relationships, and relevant attributes like time and location.

For example, Moore explained a use case involving maritime security: “There are going to be 500 ships going through [the Strait of Hormuz] over the next week, and we know some carry weapons. How do we prioritize inspections?” Elemental could analyze ship history, captain records, cargo manifests, and even weather patterns to identify high-risk vessels.

Benefits for Enterprises

Experts highlight several advantages:

• Improved accuracy: Knowledge graphs provide structured context that reduces reliance on LLMs’ probabilistic nature.
• Enhanced auditability: Full data provenance enables traceability of AI-driven decisions.
• Reduced token usage: Complex queries can be reduced from millions to just 10,000 tokens.

Broader Context Engineering Trend

Lovelace is part of a growing movement toward context engineering—providing LLMs with better data inputs. The global knowledge graph market is projected to surge from $1.34 billion in 2025 to over $19 billion by 2033.

YottaGraph, Lovelace’s complementary service, currently holds close to a trillion facts and adds another billion weekly—available free for financial data queries.