Search the evidence. Work with your own data. Run the analysis. Inspect the result. Then leave with something your team can review, reuse, and share.
An illustration of the steps a session leaves behind. Not a customer result, and the counts are made up.
These are representative workflows Cortexa can run today. They are examples, not customer claims.
Read source documents, apply the governing rule, reconcile conflicts, and update the workbook without disturbing untouched cells.
Review these extractables reports against the component list. Report every tested timepoint, separate organic and elemental results, flag gaps, and return a versioned workbook.
Inspect the actual report pages, tables, footnotes, and workbook tabs.
Match materials and timepoints; keep conflicting values visible.
Check every changed cell and reject unintended workbook drift.
Changed cells, source trail, caveats, and the untouched original.
Cortexa keeps evidence, computation, artifacts, and the conversation that produced them in the same place.
Find papers, trials, guidelines, patents, regulatory records, structures, and public datasets through one research loop.
Search within full text when it is available, inspect the exact supporting passage, and disclose when access stops at an abstract.
Bring PDFs, spreadsheets, images, scientific formats, and reusable Library folders into the same session as public evidence.
Clean data, calculate, simulate, fit models, test assumptions, and generate figures in isolated Python environments.
Start computation that needs more than one answer window, follow its progress, and let it run for up to 4 hours.
Inspect molecular structures in 3D, render formulas and figures, and keep the data behind every chart close at hand.
Record corrections when stronger evidence changes a value, review consequential answers, and keep limitations visible.
Create a cited research abstract, presentation, dashboard, spreadsheet, or shareable reader view without leaving the session.
Query approved databases, files, APIs, Slack, and GitHub alongside public research while preserving workspace access rules.
Ask for the computation in a sentence. See what it will cost before it starts, approve a ceiling, and get the result back in the conversation. No account to open, no queue to join, nothing running up a bill while you are not using it.
Every tier is metered by the second and charged at cost plus a stated markup. The maximum you approve is enforced while the work runs, not reconciled afterwards.
Statistics, cleaning, model fits and figures run in an isolated Python environment while Cortexa is answering, with the scientific stack already installed.
5 minutes
Parameter sweeps, long fits and simulations move to the background on up to 8 processors. Close the tab; Cortexa reopens the conversation with the results. You approve the maximum before anything expensive starts.
4 hours
Run a curated, version-pinned scientific model against your own text or Library files. Approve a hard maximum, watch the spend as it goes, stop it at any time, and keep the result as a reusable file with the exact model revision recorded.
Up to an hour a run
Training a model on your own labelled data is the direction this is headed, and is not built. We say so here rather than let a roadmap read as a feature.
The question decides the domain. Cortexa changes its sources, vocabulary, tools, and validation approach to fit the work.
No environment setup. No connector maze. Start in the browser with the evidence, files, and deliverable your project needs.
Try Cortexa