insightSci (Insights for Science) is a scientific intelligence platform designed to transform scholarly data into actionable knowledge that supports research, discovery, and innovation.

The platform enables researchers, graduate students, faculty members, universities, research centers, and innovation organizations to explore scientific landscapes, identify influential authors and institutions, analyze research trends, evaluate scientific impact, discover emerging opportunities, and uncover knowledge gaps across any field of study.

By integrating bibliometrics, scientometrics, network analysis, data science, and artificial intelligence, insightSci transforms large volumes of scientific information into structured insights that support evidence-based decision-making throughout the research process.

More than a search tool, insightSci provides a comprehensive environment for scientific mapping, literature exploration, research evaluation, publication strategy, institutional benchmarking, and knowledge discovery. Its mission is to help researchers better understand how knowledge evolves, where opportunities for contribution exist, and how scientific evidence can be transformed into meaningful impact.

Scientific research does not begin with a blank page. It begins with a question - and behind every good question is a researcher who has mapped the field, understood what has already been established, identified where the gaps are, and positioned their contribution with precision. That process has always existed. What was missing was a structured, teachable, and technology-supported way to carry it out.

Research Mapping Intelligence (RMI) is that framework. Developed over four years of direct work with graduate researchers across master's and doctoral programs, RMI formalizes the three cognitive layers that underpin rigorous scientific knowledge production - and gives researchers the tools to navigate each one with confidence.

At insightSci, we believe that artificial intelligence should enhance scientific thinking, not replace it. Our approach - Augmented Scientific Intelligence - combines scientific evidence, structured analytical processes, and responsible AI to support rigorous knowledge creation. The researcher remains at the center. AI acts as an intelligent partner that helps organize information, identify patterns, reveal opportunities, and accelerate analytical tasks - while preserving the critical thinking, creativity, and scientific judgment that drive meaningful discoveries.

RMI is built on a foundational conviction: artificial intelligence does not produce scientific knowledge. Researchers do.

What AI can do - and what insightSci is designed to make it do - is amplify the researcher's capacity to navigate complexity, process scale, and structure synthesis. It compresses the time spent on orientation so the researcher can invest more in the work that only they can do: generating original questions, interpreting evidence through a disciplinary lens, making conceptual connections that no algorithm anticipates, and producing the kind of insight that moves a field forward.

The new in science comes from researchers. From their questions, their disciplinary depth, their willingness to pursue what the field has not yet addressed. insightSci exists to make that pursuit more structured, more efficient, and more grounded in evidence - so that the energy researchers invest in the process translates more directly into the contribution that only they can make.

RMI was designed not only as a research tool but as a formative framework - one that helps graduate students and early-career researchers develop the habits of mind that rigorous scientific practice requires.

When a doctoral student works through the three layers of RMI, they are not just conducting a literature review. They are learning to read a field before reading papers. They are learning to identify gaps systematically rather than intuitively. They are learning to position their contribution with precision rather than assumption. They are learning, in other words, what it means to produce knowledge rather than merely consume it.

That formative dimension is what distinguishes insightSci from a search tool or a citation manager. It is a structured environment for scientific thinking - one that supports the researcher at every stage of the process, from the first search to the final synthesis.

The 1,000-publication working set is a deliberate design decision, not a technical limitation. Bibliometric analysis requires a coherent, manageable corpus - one large enough to reveal meaningful patterns across the field, but focused enough to remain analytically tractable. insightSci selects those records by relevance and scientific importance, ranks them by a composite performance metric, and opens the workspace immediately. If your query matches more than 1,000 works in the global index, the broader match count remains visible as context - so you always understand the full scale of the field - while the analytics and export operate on the curated ranked set. If it matches fewer, every result is included.

If you run the same query - identical filters and parameters - within 30 days, insightSci reuses the previously assembled dataset rather than rebuilding it from scratch. This ensures analytical consistency: when you return to refine your analysis, you are working with the same ranked corpus as before. The session header shows when your data was last updated.

When you search a new topic for the first time, insightSci does not simply return a list of links. It builds a complete analytical workspace tailored to your query. The first results appear quickly, and the broader dataset continues to grow in the background - without blocking the workspace or requiring you to wait before exploring.

Here is what is happening behind the scenes:

If you search the same topic again within 30 days, your workspace loads without delay. The system recognizes the existing dataset and makes it available immediately - analytics appear in seconds rather than minutes.

This also benefits the broader research community: if another researcher has recently searched the same topic with the same parameters, the system can assemble your workspace from that existing dataset without collecting everything from scratch. You receive your own private copy of the data - the original researcher's workspace is never affected, and your analyses remain fully independent.

Global Landscape, Authors, Journals, Networks, and Data share the same filter bar in this order: Year, Citations, Type, Language, SJR, Quartile, and Weighting. When you set a filter on any of these surfaces, it applies across them — ensuring a consistent analytical frame throughout your session. Library and Scientific Review use controls specific to their workflows.

insightSci derives a small set of performance indicators from your working dataset to help you compare papers and authors within the current research slice. All scores are computed in relation to the dataset assembled by your current search, so values cannot be compared directly across different sessions or queries.

insightSci is powered by OpenAlex, one of the world's largest and most comprehensive open catalogs of scholarly metadata. Developed and maintained by OurResearch, a nonprofit organization dedicated to increasing access to research, OpenAlex represents a transformative movement toward transparency, accessibility, and reproducibility in science.

We are grateful to the OpenAlex team and the broader open-science community for their commitment to making high-quality scholarly data freely available to researchers worldwide.

If you use insightSci in your research, we recommend citing both the platform and the underlying data source.

insightSci was founded by Vilker Zucolotto Pessin, a doctoral researcher in Education at the Federal University of Espirito Santo (UFES) and CEO of insightSci Tecnologia e Inteligencia Cientifica Ltda. His academic path spans Civil Engineering, Business Administration, Education, Computer Science, and Business Intelligence, and his professional work focuses on structuring Public-Private Partnerships at the Espirito Santo Development Bank.

The platform was born from direct experience in the field. Working closely with master's and doctoral students and faculty across graduate programs, Vilker observed a persistent and costly problem: researchers consistently struggled to navigate the scientific literature with structure, direction, and efficiency. Over four years of iterative development, the methods and tools that became insightSci were prototyped, tested, and refined in direct collaboration with more than 1,800 researchers across multiple fields and institutions.

That process produced not only a platform, but a peer-reviewed framework. Vilker is the author of the Smart Bibliometrics (published in Scientometrics, 2022), the Scientific Mapping Process (published in MethodsX, 2023), and the GapFinder (published in Studies in English Language Teaching, 2025) - three studies that form the scientific foundation of insightSci and its Research Mapping Intelligence framework. The platform's software and associated intellectual property are registered at Brazil's National Institute of Industrial Property (INPI).

Beyond insightSci, Vilker works as a structurer of Public-Private Partnerships at the Espirito Santo Development Bank, with a focus on urban infrastructure, smart cities, and sanitation projects. He holds a Master's degree in Engineering and Sustainable Development, an MBA in Business Intelligence, international certifications in infrastructure project development (Infrastructure and Projects Authority, UK) and in Public-Private Partnerships (CP3P-F, APMG International).

His work sits at the intersection of technology, science, and society - with a consistent focus on building methods and tools that help researchers produce higher-quality knowledge in an increasingly complex information landscape.