Robustify
An evidence-strength checker for journalists, policy analysts, and curious readers that takes any cited study and shows the 'multiverse' of conclusions it could have reached under reasonable alternative analyses.
Journalists, policy analysts, and think-tank researchers citing empirical studies in their work
- Paste a paper and get a 'confidence ribbon' showing how much the headline conclusion shifts across plausible analytical paths
- Plain-language summary of which choices mattered most (data filtering, model spec, covariate selection)
- One-click caveat generator that produces hedging language for your article or memo
- Side-by-side comparison with the original authors' ideological priors (when disclosed)
A high-profile arXiv paper shows 72% of the human ideological gap in a real immigration study can be reproduced by AI personas running different analyses — readers need to see the underlying variability.
Established fact-checking/verification market for journalists (Lenz, ClaimReview, The Factual, Sider lists 10+ tools), but they target claim/source verification, not multiverse re-analysis of empirical studies — niche within the niche.Best AI Fact-Checking Tools to Trust in 2025 - sider.ai ↗AI Research Assistant — Verify Claims with Cited Sources | Lenz ↗
No direct competitor found that takes a cited empirical study and shows alternative-analysis conclusions for non-academic readers; adjacent tooling (R/Stata multiverse packages, specification-curve code) exists but is academic, not journalist-facing.Robustness checks and robustness tests in applied economics ↗Robustness Analysis in Quantitative Management Research ↗
Journalists are notoriously price-sensitive (free tools dominate the space); think-tank/policy buyers have budgets but it's a small market, and existing B2B comparable pricing (FactSet, BI tools) suggests high ACV is achievable only with clear ROI proof.Fact-checking | Verification | OSINT | Journalist's Toolbox ↗FactSet Pricing ↗
Replication crisis and forking-paths problem (Gelman & Loken) is a multi-decade structural issue reinforced by the cited arXiv finding on AI personas — demand is durable, not a trend.The Garden of Forking Paths - Mark Rubin ↗Paradise of Forking Paths: Revisiting the Adaptive Data Analysis Problem ↗
Technically demanding: requires parsing study methodology, generating justifiable alternative specifications, accessing/replicating underlying data (often restricted), and re-running statistical analyses with AI agents — non-trivial even with LLMs.How Robust are Robustness Checks? - arXiv ↗Robustness Tests for Quantitative Research (Cambridge) ↗