Quantitative intelligence
Go from natural language to reproducible workflows—so your desk spends time on ideas and validation, not one-off adapters and lost notebook context.
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> Inductus, perform a covariance denoising on my portfolio and export to Jupyter.
Product preview
Chat, data, and export paths in a single surface—built for desks that need clarity, not another generic chat window.
Knowledge that does not compound. Data work that never becomes research. Inductus is aimed at both—without turning your desk into an integration project.
Repositories and notebooks pile up; reproducibility does not. The goal is a workspace where prior work feeds the next experiment—not a graveyard of PDFs and forks.
Most of the calendar disappears into cleaning and joins before anyone tests a serious hypothesis. The goal is to collapse that tax without giving up ownership of your files or your methodology.
Technical architecture, integrations, and memory model— read the full Inductus product page.
Orchestration, knowledge, and memory—so research stays reproducible, not anecdotal.
Legible pipelines from prompt to steps—export when you are ready to harden methodology.
Bring repositories and notebooks into a workspace built for reuse—not one-off summaries.
Context that compounds across experiments so the dashboard reflects how your work actually evolves.
Local ownership where it matters; curated fundamentals where they help you validate—without losing a reproducible trail.
Ingest CSV and Parquet with discovery and cataloging on your side of the boundary—so the agent meets your data, not the other way around.
Access Sharadar SEP / SF1 where available—supporting exploration and validation without replacing your own datasets.
Disclaimer: Inductus AI is not a stock picker or trading advisor—it is a tool for exploring data and testing hypotheses.
