A new startup is aiming to solve one of artificial intelligence’s biggest problems: lack of reliable context. Its platform, Elemental, uses AI to build knowledge graphs—structured networks that map relationships between data points—to help large language models (LLMs) make more accurate and trustworthy decisions. The company argues that without this contextual layer, AI systems struggle to deliver reliable results, especially in high-stakes environments.
The issue is significant because current AI models still produce high error rates. Studies show hallucination rates across leading models can range from 22% to as high as 94%, making them unreliable for critical applications. Lovelace’s approach grounds AI responses in verified data by linking entities, relationships, time, and location into a structured framework. This allows organizations to trace exactly where an AI’s answer comes from, improving both accuracy and auditability.
Knowledge graphs play a crucial role by turning raw data into meaningful context. Instead of relying purely on probability-based text generation, AI systems can use these graphs to connect facts and reason more consistently. Experts note that this approach not only reduces hallucinations but also improves explainability—helping businesses understand why an AI produced a certain output. Additionally, it can dramatically cut computing costs by reducing the amount of data (tokens) AI models need to process.
Overall, the development reflects a broader shift toward “context engineering” in AI. Rather than just building bigger models, companies are focusing on feeding them better, structured data. As demand grows for reliable AI in sectors like finance and intelligence, knowledge graphs are emerging as a key foundation for enterprise-grade AI systems, making them more accurate, scalable, and trustworthy.
