Academic research has never been short of papers — the real challenge is finding the right ones and synthesizing them reliably.
Researchers, graduate students, and educators often rely on platforms like Google Scholar, PubMed, or Semantic Scholar. While powerful, using them separately is time-consuming, fragmented, and still leaves a critical problem unsolved: how to write a literature review without introducing AI hallucinations.
This is exactly the gap we set out to solve.
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One-Stop Academic Search Across Major Databases
Instead of searching multiple platforms one by one, our system provides a unified academic search experience across:
• Google Scholar
• PubMed
• Semantic Scholar
With a single query, users can retrieve relevant papers from all three sources in parallel. This dramatically reduces repetitive searching and ensures broader coverage across disciplines, especially for interdisciplinary research.
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Enhanced Keyword Expansion Beyond Simple Search
Academic concepts are rarely expressed using a single term.
For example, when users search for “AI”, relevant papers may instead use terms such as:
• Artificial Intelligence
• Machine Learning
• Deep Learning
• Neural Networks
• Intelligent Systems
To address this, our backend enhances every query using regex-based and semantic keyword expansion. Instead of relying on a literal keyword match, the system automatically identifies domain-specific synonyms and related terms, then searches across all supported databases simultaneously.
This means users discover more relevant papers — not just those that happen to use the exact wording they typed.
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Literature Reviews Built Only on Real Papers
Most AI writing tools struggle with one critical issue: hallucinated citations.
Generated references may look convincing, but often:
• Do not exist
• Do not match the cited claims
• Cannot be verified in real databases
Our approach is fundamentally different.
How it works:
1. Users first search and select real academic papers
2. The literature review is generated only from those verified papers
3. Every claim in the review is grounded in actual abstracts and metadata
Because the AI never invents sources, the result is a hallucination-free literature review that can be checked, cited, and trusted.
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Why This Matters for Researchers and Students
This workflow offers several key advantages:
• ✅ No fabricated citations
• ✅ Traceable sources for every summary
• ✅ Faster literature review drafting
• ✅ Better coverage through multi-database search
Instead of replacing academic judgment, the system supports it — helping users move from search → synthesis → writing with confidence.
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Designed for Real Academic Workflows
This tool is built for:
• Graduate students writing theses and dissertations
• Researchers conducting systematic or narrative reviews
• Educators preparing course materials
• Anyone who needs accurate, verifiable academic summaries
By combining enhanced academic search with grounded AI writing, we aim to make literature review writing faster, safer, and more reliable.
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Final Thoughts
AI can be incredibly powerful in academic writing — but only when it is anchored to real data.
By integrating Google Scholar, PubMed, and Semantic Scholar into a single search workflow, expanding queries intelligently, and generating reviews strictly from real papers, this approach demonstrates how AI can assist research without compromising academic integrity.
If you believe literature reviews should be efficient and trustworthy, this is the direction academic AI needs to take.
