AI for Pharma R&D & Biotech
Extract and search across patents, pipeline documents, and scientific literature you upload, with findings linked to source for IP and Medical Affairs review.
Built for
Why Pharma teams struggle today
The problems we see in every pharma workflow.
- Patent landscape analysis across thousands of patents is expensive and slow
- Competitive pipeline intelligence is scattered across press releases and trial registries
- Regulatory dossier preparation requires precise cross-referencing
- Extracting comparable data from hundreds of publications is slow and inconsistent
- Evidence for a dossier is scattered across formats and teams
How DataStruct AI solves it
Purpose-built capabilities for pharma workflows.
- Patent claim analysis and freedom-to-operate assessment
- Pipeline intelligence extraction (compound, indication, phase, sponsor)
- Regulatory milestone extraction from filings and releases you upload
- Literature data extraction with source-linked summaries
- One workspace for patents, literature and pipeline documents
Pharma use cases
The most impactful workflows DataStruct AI powers for pharma teams.
Patent Landscape Analysis
Problem: FTO analysis across a therapeutic area means reading thousands of patents.
Solution: Claim and priority-date extraction across the patent set, with source links for counsel.
Impact: Patent landscapes mapped in days, not months. Better strategic decisions.
Competitive Pipeline Intelligence
Problem: Tracking competitors' pipelines means monitoring trial registries, press releases, and conferences.
Solution: Extraction of pipeline data (compound, indication, phase, sponsor) from the documents you upload, each linked to source.
Impact: Structured pipeline data for strategy teams to review.
Regulatory Dossier Preparation
Problem: eCTD and CTA submissions require precise cross-referencing.
Solution: Rule packs for required sections and source-linked extraction across modules, for reviewer checking.
Impact: Faster submissions. Fewer agency questions.
Scientific Literature Evidence Extraction
Problem: Medical Affairs needs comparable data from a growing set of publications in a therapeutic area.
Solution: Upload the publications, define the fields, and extract them with source links; ask questions across the set with citations.
Impact: Source-linked literature evidence for Medical Affairs review.
Document types we handle
Questions you can ask
- ›“What is the patent landscape for KRAS inhibitors?”
- ›“Compare efficacy data across competing compounds”
- ›“What FDA feedback has been received on this mechanism?”
- ›“Identify freedom-to-operate risks for our pipeline”
- ›“Track regulatory milestones for this indication”
Why Pharma teams choose DataStruct AI
Evidence-cited AI
Answers cite the sources they rely on, and records the system could not confirm are shown separately for a reviewer to check.
Access control and audit
Workspace roles with granular permissions and an audit log on sensitive actions. Tenant isolation is enforced in the application layer.
11 industries
Pre-configured terminology, document types, and rule packs for your vertical.
See DataStruct AI in action
Tell us about your team, document volumes, and integrations. We'll put together a tailored package and walk you through the platform.