Breakthroughs Accelerated

Nimble Clinical AI Automation Platform

Nimble is built from the ground up as an R & AI-native environment to automate clinical data and document lifecycle.

Below is the current and near-term feature set organized around the key workflows where we apply vibe coding, vibe editing, agents, and automation.

Vibe Coding

Chat-to-code for clinical workflows – Describe what you need in natural language (“build ADSL from this spec”, “draft a swimmer plot for PFS/OS”) and let our Nimble coding agent generate R programs aligned with your SDTM/ADaM specifications, protocol, SAP, CRF and TFL shells.

Versatile coding agent – A configurable coding agent that can propose code, cross-check against specs, and suggest tests/QC snippets, providing robust support for simple to complex programming workflows in a highly customizable environment.

Context-aware, executable, audit-trailed – The agent works within your trial context, can execute generated R code directly in the platform, and records prompts, responses, so conversation is fully traceable in line with GxP expectations.

Vibe Editing*

Super-fast Clinical document authoring – Draft and refine protocol sections, CSRs, statistical analysis content, data review plans, and standards documents using natural-language prompts grounded in your existing materials.

Specification-centric authoring – Create and update machine-readable specifications for SDTM, ADaM, TFL shells, and data review rules so statisticians, programmers, and writers work from a single source of truth.

Grounded in data – Tie protocol text, CSRs, and specifications back to real SDTM/ADaM datasets, listings, and analysis outputs, so documents reflect actual trial data rather than remaining purely theoretical.

Review-ready drafts – Generate structured, versioned drafts that support efficient review and governance, including change tracking and clear rationales for AI-assisted edits.

Data Visualization

Rich dashboards and graphical patient profiles – Generate and maintain interactive views for safety, operations, data quality, and patient journey analysis, all powered by R and tailored for cross-functional clinical teams.

Templates driven and Versatile – Start from proven visualization templates (e.g. patient profiles, risk dashboards, profile grids), then use powerful slicing/dicing features to adjust filters, facets, and layouts on the fly, eliminating the need to program subgroups, endpoints separately.

DIY visualizations with AI guardrails – Biostatisticians and data scientists can spin up their own plots and dashboards using point-n-click, low-code configuration and AI-assisted R code, while still benefiting from centralized styling, metadata, and access controls.

SDTM & ADaM

Machine Readable Graphical Spec-driven – Use an Excel-like spec interface plus AI assistance to define SDTM dataset specs; the platform then creates submission-ready datasets directly from those machine-readable specs.

AI-assisted spec authoring – Upload protocol, SAP, and CRF excerpts and let the system propose draft SDTM/ADaM specifications, which you can refine rather than create from a blank page.

Define.xml in minutes – Generate Define.xml from the same specifications used for dataset creation, minimizing duplication of effort and keeping documentation aligned with the actual implementation.

Data Quality & Review

AI-accelerated anomaly detection – Agents scan across domains to flag outliers, inconsistencies, protocol-deviation patterns, and unexpected trends, complementing rule-based checks with pattern-driven insights.

Context-rich issue triage – Findings are linked back to patients, visits, and key endpoints, helping review teams quickly understand why something looks suspicious and whether it warrants a query or a design change.

Continuous, explainable QC – Reusable review “recipes” combine AI-driven checks, standard listings, and visualizations, giving teams a repeatable way to monitor data quality throughout the trial.

Tables, Figures & Listings

Template-based TFL generation – Configure standard TFL templates once, then reuse them across studies with parameterization for endpoints, populations, and subgroups, with the engine generating R code to produce consistent outputs.

AI-assisted shell drafting * – Provide high-level reporting requirements or SAP excerpts and let AI propose TFL shells and layout options, which you can adjust before locking down templates.

From vibe Coded to validation ready output – Describe the desired table or figure in natural language (“KM curve for PFS by treatment and stratification factors with 95% CI and risk table”), have AI draft the high quality TFL code, and, then run through the platform’s review and QC flows to reach independent QC-ready quality.

* These features are currently under development and planned to be released in the coming months.

Leadership

Vineet Jain

CEO of Nimble, combines deep expertise in pharmaceutical research with a passion for AI. His 20+ years of experience in data science, biostatistics, and data management, along with his background in computer science, statistics and machine learning, provides the foundation for Nimble's innovative solutions.

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