AI is in the roadmap. The bottleneck is still in the workflow.
Most pharma companies have spent five years building better data infrastructure. Study teams still lose weeks, not because of platform limitations, but because of the execution layer sitting beneath them.
Protocol translation takes weeks
Manual database specification builds run 12–14 weeks in complex studies, a cycle compressible to days with agentic automation and governed AI.
Data fragmented across platforms
Decentralized and hybrid trial models have splintered data across EHRs, wearables, labs, and imaging, creating review burdens that compound with every study.
AI stuck in pilots, not production
Governance gaps and validation debt keep AI programs from scaling. Organizations that govern from day one deploy to production twice as fast as those retrofitting later.
Regulatory pressure is accelerating
Context-specific validation, source data traceability, and AI explainability are more important than ever and are the baseline inspection requirements.
Three AI-native tools. One continuous workflow.
Built on NLP, ML, and agentic automation, each tool targets a specific stage of the trial lifecycle where manual effort is highest and the compliance stakes are clearest.
Protocol to study build, in a single workflow
CSAT uses NLP and ML to extract protocol content and auto-generate eCRFs, edit checks, and EDC-importable files, compressing a 12–14-week manual build cycle into days. One-click generation of the complete study build package with full traceability.
From raw EDC data to submission-ready SDTM datasets
ML-driven metadata repository learns transformation rules from historical mappings and applies them to new studies, automating variable derivations, terminology normalization, and domain generation. What previously took 12–14 weeks of manual programming is now done in days, with CDISC-compliant, audit-ready outputs.
Machine-readable protocols that power downstream automation
CDD transforms unstructured protocol documents into structured, machine-readable digital specifications aligned to USDM standards, enabling CSAT, SDTM automation, and agentic study management without manual re-entry. By analyzing historical protocols, CDD generates structured content, recommends precise section text, and surfaces analytics that inform smarter protocol decisions for future trials, reducing amendments before they happen.
Cycle-time gains at the workflow level
What representative Fresh Gravity programs deliver when AI is applied at execution, not at the reporting layer.
AI-enabled at every critical stage
Fresh Gravity's tools span the complete clinical development process, so impact compounds across your portfolio, not just one study.
Protocol Design
CDD digitization, USDM/DDF alignment, amendment reduction
CDD · CSATStudy Build
Automated eCRFs, edit checks, EDC file generation
CSATSDTM & ADaM
Automated mapping, derivation, CDISC-ready datasets
SDTM AutomationRegulatory & Submission
FDA/PMDA packages, inspection-ready governance
GovernedProductize, Don't Pilot.
Every engagement starts with one workflow, governs it from day one, and moves it to production, not into a pilot backlog.
One workflow. Highest manual effort. Clearest compliance stakes.
We identify where AI creates the fastest, most defensible improvement and govern that single workflow end-to-end, then expand to the next.
Context of use, validation, and audit trail from the start.
Regulatory expectations are explicit: AI explainability, source data transparency, and documented intended use are now inspection requirements. We build them in, not after the fact.
Databricks, Snowflake, your clinical data lakehouse.
Fresh Gravity's tools integrate with the platforms you've already invested in. We add the workflow layer, not another data platform.
30 minutes. One workflow. A candid conversation.
We identify one workflow where AI creates the fastest measurable impact, and where it wouldn't. No presentation. No agenda beyond your reality.
Regulatory compliance is built in
Not a checklist at the end, but the foundation that makes governed deployment possible from day one.
Running experiments. Accumulating validation debt. Waiting for governance to get cleaner before scaling.
One governed workflow at a time. Audit-traceable. Showing up in cycle-time data. Pulling ahead.
Clinical AI that's embedded in your workflows, governed for regulators, and built to scale; not another proof of concept that stalls at the pilot stage.
Engineered for Outcomes.
See Fresh Gravity Clinical AI in action
Tell us your priority, CDISC compliance, faster data readiness, or RWE, and we'll show you the fastest path to production.
www.freshgravity.com