Executive Summary

Many cognitive, psychology and neuroscience researchers—roughly 1.2 million globally—regularly struggle to turn raw behavioral data into publication-ready figures because of fragmented preprocessing steps, inconsistent statistical defaults, and limited coding or design expertise; labs and graduate students often report spending 10–20% of analysis time on figure production and iteration. This bottleneck slows manuscript submission, complicates peer review, and undermines reproducibility when figure generation is ad hoc and undocumented. A practical solution is a SaaS platform that ingests common behavioral data formats (CSV/JSON/BIDS), runs validated preprocessing and QC, and outputs editable, journal-formatted figures alongside the underlying R/Python/plotly code and provenance metadata; features would include templated plots for reaction-time distributions, psychometric functions, mixed-effects results, exportable SVG/PDF outputs, and an API for pipeline integration. Enterprise and lab-tier options would provide shared style guides, versioned templates, and audit trails to support reproducible workflows. The market is attractive now: a conservative TAM is about $2.4B (1.2M researchers × $2,000/year), and demand is growing as open-data and preprint practices increase available training datasets while funder and journal reproducibility mandates raise the cost of non-auditable figures. Advances in code-generating LLMs and standardization efforts lower technical barriers and shorten time-to-market, reducing development risk. To stand out in a medium-competition space you should prioritize scientific correctness and traceable provenance over novelty of visuals—differentiators include peer-reviewed figure templates, default statistical practices vetted by domain experts, and transparent code outputs that reviewers can run. Real challenges will be accommodating diverse experimental formats, ensuring privacy-compliant handling of human-subject data, and earning trust via partnerships and replication case studies, but a careful, validation-first approach can build durable adoption.

Market Opportunity

Researchers struggle to make publication-ready psychology figures — AI converts raw behavioral data into journal-ready visuals targets a $2.4B = 1.2M cognitive, psychology & neuroscience researchers x $2,000/year (avg tool + visualization service spend) total addressable market with medium saturation and a year-over-year growth rate of 8-14% — research software + open-science tooling adoption growing; more labs adopting paid analytics/visualization tools.

Key trends driving demand: Open-data & preprints -- more shared behavioral and neuro datasets increase available training material and demand for standardized figures.; Reproducibility mandates -- journals/funders require clearer methods and accessible data, creating demand for automated, auditable figure pipelines.; AI-for-code and plotting -- LLMs and code generation reliably produce visualization code (R/Python), speeding product development and developer adoption..

Key competitors include GraphPad Prism, Plotly / Dash (Plotly Technologies), R + ggplot2 ecosystem (with Posit/RStudio), BioRender, Adobe Illustrator / Manual polishing workflow.

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Researchers struggle to make publication-ready psychology figures — AI converts raw behavioral data into journal-ready visuals

Executive Summary

Market Validation

Market Opportunity

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