LLMs fail at comparing related forecasts across sector boundaries. That's where the biggest investment opportunities hide. We built the structure to find them.
Try EKG FreeThe Problem
Investment teams are organized by sector. When GPU demand expectations shift, the technology analysts see it. But the utilities analysts covering power generation don't connect the dots until much later.
Research shows LLMs are "specifically worse than humans at combination questions about 2 events' odds of co-occurrence." They can't systematically find forecast inconsistencies.
"Plain vector similarity search falls short when answering multi-hop questions that require connecting information across multiple documents." The architecture is wrong.
"As a particular failure mode, we find LLMs are specifically worse [than humans] at combination questions [about 2 events' odds of co-occurrence]."ForecastBench: A Dynamic Benchmark of AI Forecasting Capabilities
The Solution
Our patented EKG gives AI agents the ontology architecture to find inconsistencies between related expectations across sector boundaries - and helps you handicap their impact on investments in 4 steps.
Step 1
Identify expectations that have moved, then discover related expectations that haven't shifted yet.
Step 2
Generate percentile forecasts for how related expectations will shift over your investment horizon.
Step 3
Translate expectation shifts into percentile forecasts for investment valuation changes.
Step 4
Use the EKG's relationships to optimize position sizes across your entire portfolio.
Real Example: AI Data Center Buildout
When AI data center buildout expectations spiked, GPU chip forecasts moved immediately. But Texas electricity production forecasts - for the same data centers - lagged behind.
The EKG connects these related expectations across the Technology/Utilities sector boundary. It found the inconsistency early.
Why Us
Knowledge Graphs got hot in 2025 when enterprises saw they're necessary for LLMs to achieve multi-hop reasoning on new information outside their training data. But no one else has designed and stress-tested a Knowledge Graph for the purpose of finding inconsistent investor expectations across sectors and across Prediction Markets, which also got hot in 2025. We have.
Tabular Manipulation of Higher Arity & Cardinality in Graphs & Ontologies, and Using a Logical Graphical Model to Define a Probabilistic Graphical Model.
Schedule a free scoping call to see how the EKG can help your investment process.
Try EKG FreeFree 30-minute call. No commitment required.
