From published method to reproducible evidence.
Reprolume is building an AI-assisted research verification workflow for technical literature. We turn published methods into structured assumptions, reviewable experiment specifications, deterministic numerical checks, and evidence-linked explanations.
What we are building
A workflow that keeps the path from source material to experiment and interpretation visible.
Source-grounded structure
Identify the problem, assumptions, recurrence, claim, and scope in public technical literature.
Reproducible checks
Translate methods into explicit configurations and deterministic numerical experiments that can be replayed and inspected.
Evidence-aware output
Keep literature claims, illustrations, sampled evidence, and mathematically justified tight cases clearly separated.
Open-source proof of work
Public experimental foundation
ChainBench is an open-source experimental toolkit for auditable numerical learning and reproducibility around public, published optimization methods.
AI interpretation, deterministic verification
Model output is not treated as numerical ground truth. Reprolume's product direction keeps AI-assisted interpretation separate from deterministic code, tests, saved configurations, and reproducible outputs.
Built for technical clarity
Reprolume is an early-stage, bootstrapped project. We do not claim customers, revenue, funding, or adoption that we do not have. The current public proof of work is ChainBench.