Executive Summary

Researchers and advanced learners (≈10M potential users) routinely waste time and run into reproducibility gaps because current tools make it hard to trace a claim back to methods, data, and first principles—this pain is acute for interdisciplinary teams, reviewers, and industry R&D groups. The result is duplicated effort and low confidence in decisions built on literature. You could build an interactive dependency graph that ingests machine-readable scholarship (OpenAlex, Crossref), uses LLMs and extraction models to identify claims, methods, and citations, and maps chains of evidence down to foundational assumptions; users can drill down, annotate, export provenance, and curate edges with confidence scores. Seed graphs would be auto-generated and improved via human-in-the-loop workflows and integrations with reference managers and institutional APIs. The market looks attractive right now: a $3.6B addressable market (10M users × $360/year) and rising demand for explainability and provenance in both academia and industry, while public APIs and AI extraction lower data and labor barriers to entry. Faster ingestion and automated seeding shorten time-to-value compared with purely manual solutions. The competitive edge would be precise, claim-level provenance and a UX optimized for forensic tracing of arguments rather than simple citation networks; key challenges are NLP accuracy, ontology alignment, and adoption hurdles that will require rigorous validation, publisher partnerships, and clear trust signals.

Market Opportunity

Trace cutting-edge science to first principles with an interactive dependency graph targets a $3.6B = 10M researchers & advanced learners × $360 annual spend on research/knowledge tools total addressable market with medium saturation and a year-over-year growth rate of 10% YoY (estimate based on EdTech growth and increasing R&D tooling spend; source: industry estimates for research and edtech software growth).

Key trends driving demand: Open, machine-readable scholarship — public APIs like OpenAlex and Crossref make large-scale ingestion feasible which lowers the data barrier for new tools.; AI-enabled extraction and summarization — LLMs and fine-tuned models enable automated claim, method and citation extraction to seed graphs rapidly.; Demand for explainability and provenance — both academia and industry want reproducible chains of evidence, creating demand for lineage tools.; Interdisciplinary research growth — more cross-field work increases the value of tools that reveal prerequisites and where disciplines intersect..

Key competitors include Semantic Scholar, Connected Papers, OpenAlex (OurResearch).

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Trace cutting-edge science to first principles with an interactive dependency graph

Executive Summary

Market Validation

Market Opportunity

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