Large Language Models (LLMs) have revolutionized how we interact with technology, powering everything from chatbots to search engines. But what about tackling complex, high-stakes research problems where precision and logical consistency are paramount? Current LLM-based approaches, even those augmented with retrieval methods (RAG), fall short in domains like medical research and financial analysis. Why? Because piecing together insights from multiple sources and reasoning through complex causal chains requires more than just finding keywords. At Elemental Cognition, we've developed Cora, a neuro-symbolic AI research assistant built on a platform that integrates the strengths of LLMs with the rigor of symbolic reasoning. Imagine exploring a potential link between rheumatoid arthritis and a specific kinase inhibitor. Instead of providing a shallow list of potentially relevant papers like existing RAG solutions, Cora uses a research template to systematically map out the connections, providing detailed evidence and citations for each step in the causal chain. This approach allows researchers to quickly grasp the core biological linkages without wading through mountains of literature. The same power applies to macroeconomic analysis. Consider predicting the impact of falling economic growth and high inflation on bond yields in an emerging market. LLMs often stumble over the logical intricacies of such scenarios, making flawed assumptions about independence or conflating mutually exclusive conditions. Cora, on the other hand, dynamically builds a causal map from relevant literature, allowing precise causal inference that accounts for the interplay of factors. It even lets users interactively refine the model, changing edge weights or adding new factors to explore what-if scenarios. Our initial evaluations in the medical domain show Cora's superior performance. Compared to GPT-4, Perplexity, and Elicit, Cora provides more comprehensive answers with significantly higher rates of justification and relevance. Critically, all of Cora's claims are backed by verifiable citations, addressing the problem of hallucinated references that plagues many LLM-based systems. Cora's strength lies in its ability to extract granular, evidence-based insights from unstructured data, creating a robust knowledge graph that guides its reasoning and explanation generation. This neuro-symbolic approach allows Cora to navigate complex multi-hop relationships, providing researchers with the depth and precision needed for confident decision-making in high-stakes domains. The future of AI isn't just about smarter chatbots; it's about building intelligent research assistants that can help us unlock deeper insights and solve the world's most challenging problems. And with platforms like Cora, that future is closer than ever.