About Research GAP
Research GAP is built for the uncomfortable space between having a research idea and understanding what the literature can actually support.
Starting a research project often begins with a promising question, but finding a few related papers is only the first step. You still need to understand what those papers actually studied, which populations, methods, datasets, and outcomes they covered, how strong and accessible their evidence is, and whether other work already addresses the same idea from a different angle.
Research GAP helps turn that early uncertainty into a clearer, inspectable starting point. It searches relevant literature, organizes the evidence behind the results, compares the boundaries of existing work, and looks for competing papers that may challenge an apparent opening. The goal is not to declare a topic globally novel. It is to help you see what has been studied, what remains uncertain, and what is worth reading or investigating next.
Two depths
Quick Search uses deterministic query planning, OpenAlex retrieval, deduplication, and basic relevance ranking to return a fast list of potentially useful papers. It does not use paid AI models, perform structured evidence extraction, verify candidate gaps, or write a full research report. Use it when you want an inexpensive first pass and a quick sense of what is already indexed around an idea.
Full Gap Analysis uses the same retrieval foundation, then adds AI-assisted analysis where configured: query refinement, evidence extraction, comparison of the retrieved literature, and report writing. It also checks direct matches, builds a literature landscape, proposes evidence-based candidate gaps, and searches for counterexamples. AI helps interpret and organize the retrieved evidence; it does not search the entire world’s literature or prove that an idea is globally novel. Accessible full text is optional and never assumed.
Complete project flow
Separate the core facets of the question without inventing missing details.
Turn the original idea into a small set of complementary search formulations.
Discover candidate papers through several bounded scholarly search routes.
Merge duplicate works and rank the remaining papers against the original idea.
Collect structured claims from abstracts or accessible full text.
Compare the methods, settings, data, outcomes, and limitations represented in the retrieved work.
Suggest possible openings only from explicit patterns in the observed landscape.
Search directly for papers that could contradict or already cover each candidate gap.
Present a qualified conclusion with coverage, supporting papers, and visible limitations.
Acknowledgements
OpenAlex supports scholarly metadata and discovery. Semantic Scholar supports scholarly search and metadata where configured. OpenAI provides AI-assisted structured analysis where configured.
Parts of the evidence-retrieval and grounded scientific synthesis approach were inspired by PaperQA2 by FutureHouse. This is design inspiration; Research GAP does not claim that PaperQA2 supplies its data or that PaperQA2 code is used.
Research GAP is independently developed and is not affiliated with or endorsed by these organizations.
Creator
I’m Temuujin, a researcher and builder interested in making scholarly exploration more transparent, practical, and easier to question. I built Research GAP to help turn an early research idea into a clearer, evidence-aware direction.
Begin with a question
