Announcing Enjamb's backing by Y Combinator to build the agentic workspace for drug programs.Biostatistics and programming
Enjamb runs Python and R analyses, drafts statistical outputs, supports SDTM, ADaM, TFL, and QC workflows, and turns results into review-ready narratives.
Workspace prompt
Generate first-pass TFLs and QC notes from uploaded clinical datasets and the SAP draft.
Agent response
Enjamb profiles the data, writes analysis code, produces review-ready outputs, and links the numbers back to source files and assumptions.
Execution runbook
Enjamb turns an open-ended program request into a reviewable chain of evidence, analysis, drafting, and audit.
Agents align datasets, endpoints, cohorts, windows, and populations with the analysis assumptions.
Python and R agents create transformations, summaries, figures, listings, and tables with inspectable code.
The QC workflow checks row counts, denominators, missing data, ranges, labels, and narrative consistency.
Agents turn reviewed outputs into concise language for clinical, statistics, and regulatory stakeholders.
Use case workflow
Execution
Upload datasets and ask Enjamb to clean, transform, test, visualize, and explain the results using reproducible code your team can inspect.
Review
Generated code, source files, transformations, tables, figures, listings, and narratives remain connected so teams can see how each conclusion was produced.
Agent team
Translates analysis assumptions into executable programming tasks.
Generates tables, figures, listings, and supporting code.
Validates outputs and highlights inconsistencies.
Explains results in review-ready narrative form.
Artifacts produced
Review safeguards
Outcomes
The goal is not more documents. It is a faster, more traceable way to move program work into review.
Faster exploratory and production analysis drafts
More transparent QC cycles
Better alignment between stats and regulatory writing