AI statistical programming automation
AI statistical programming automation
CodeMagic
AI-Assisted Code Generation for Statistical Programming
CodeMagic is Geninvo’s AI statistical programming automation platform. It converts your ADaM specifications and TLF mock shells into ready-to-execute R code automatically. As a result, your statistical programming team delivers results up to 50% faster, with 85% code accuracy. Furthermore, every output is GxP-compliant and CDISC-aligned out of the box. In addition, CodeMagic integrates directly into your existing Statistical Computing Environment (SCE). Therefore, you can start automating on day one without disrupting your workflow.
CodeMagic's Three Core Automation Modules
Automated ADaM Code Generation CodeMagic reads your variable-level ADaM specification metadata and derivation logic. As a result, it produces complete, CDISC-compliant ADaM dataset code instantly. No more manually translating specifications into R scripts. Furthermore, your programmers can review and run the output immediately saving days of manual work per dataset.
TLF Automation for Clinical Trials Upload your Tables, Listings, and Figures mock shells alongside your ADaM specs. CodeMagic generates structured, execution-ready R code for every output. In addition, the formatting follows your organisation’s conventions — consistently applied across all 200–500 TLFs in your study. Therefore, your team delivers a complete TLF package in a fraction of the usual time.
Raw Data to SDTM — Automated CodeMagic converts raw clinical data to SDTM-compliant datasets using your detailed mapping specifications. As a result, a process that traditionally took 4–6 weeks is completed in days. Furthermore, the outputs are audit-ready and submission-standard from the start not after a lengthy QC cycle.
The AI Engineering Behind CodeMagic
CodeMagic is not a general-purpose AI code generator. It is purpose-built for clinical data statistical programming. As a result, it understands the exact complexity of CDISC standards, regulatory requirements, and pharmaceutical programming conventions. Four core technologies power the platform:
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Context Engineering
study metadata is structured into precise AI-ready inputs
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Prompt Engineering
domain-expert prompts guide every code generation task
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Retrieval-Augmented Generation (RAG)
semantic search retrieves relevant CDISC patterns at runtime
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Human-in-the-Loop Review
expert programmers validate every AI output before execution
