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Build Trusted Graph‑AI for Enterprise Discovery
Verify, link and query scientific claims in seconds. SciClaim distills R&D documents into a claim‑centric Knowledge Graph with full provenance—so your team moves faster and safer.
Why SciClaim
Audit‑Ready Evidence
Every claim is linked to its source with checksum hashing—meet EU AI‑Act traceability out‑of‑the‑box.
Custom Domain Graphs
Deploy private knowledge graphs on your proprietary R&D data in weeks, not years.
Cross‑Domain Insights
Connect chemistry, biology and clinical outcomes to reveal hidden pathways and hazards.
API‑First Architecture
GraphQL & REST endpoints let you embed trusted answers in ELNs, LIMS or chat copilots.
How It Works
Ingest & Parse
Securely upload PDFs, patents or lab notebooks via on‑prem or S3 connector.
Verify & Score
Each claim undergoes verification through our proprietary cross-referencing system that ensures accuracy, reliability, and explicit citation trails.
Link & Enrich
Ontologies align entities; graph is enriched with public databases (MeSH, UniProt…).
Query & Integrate
Analysts ask questions in plain language or via API—answers arrive with citation paths.
Where SciClaim Makes the Difference
Pharma & Biotech
Slash literature review time by 90% & uncover off‑target pathways early. e.g: identify conflicting toxicity claims across 5k papers in minutes.
Legal & Regulatory
Map precedents and scientific evidence for filings. Produce audit‑ready dossiers with source trails.
Materials Science
Connect claims about composition, process parameters, and mechanical properties to accelerate new alloy or battery discovery.
Climate & ESG Analysis
Synthesize claims from environmental studies to validate sustainability strategies and ESG disclosures.
Join Early-Access Waitlist
No spam. We'll reach out with pilot openings and product updates.
Frequently Asked Questions
- What scientific disciplines does SciClaim cover?
We're starting with biomedical science as our first domain, where we're building specialized models and knowledge graph structures. Our roadmap includes expanding to adjacent fields with the highest potential impact through domain-trained systems that create field-specific knowledge graphs, ultimately working toward comprehensive cross-disciplinary coverage.
- How does SciClaim differ from current AI solutions?
Unlike general AI models that can hallucinate and lack source traceability, SciClaim builds a structured knowledge graph with explicit citation trails. This enables more reliable verification and cross-disciplinary connections that generic AI cannot provide, making it ideal for high-stakes scientific applications.
- How does SciClaim verify scientific claims?
Our verification approach combines domain-tuned LLMs with a multi-stage validation process. We're developing systems that extract claims from peer-reviewed literature and preprints, then cross-reference them across multiple publications to establish confidence levels. During our R&D phase, we're refining these verification methods to ensure the highest accuracy before scaling to production.
- Can I integrate SciClaim with my existing research tools?
While we're in the development phase, we're designing SciClaim with integration capabilities in mind. Our architecture will include a comprehensive API to allow future integration with reference managers, literature review tools, and other research software. We're currently defining these integration points and welcome early discussions with potential partners interested in future collaborations.
- How frequently is SciClaim updated?
SciClaim is designed to incorporate new scientific claims continuously as research emerges. During our initial development phase, we're focused on building the core infrastructure that will eventually monitor major journals, preprint servers, and other scientific sources to ensure the knowledge graph remains current. We're starting with batch updates while developing the real-time ingestion systems.
- What makes SciClaim different from other knowledge graphs?
SciClaim is uniquely focused on scientific claims rather than general knowledge. We emphasize verification, cross-disciplinary connections, and the specific needs of scientific research. Our knowledge graph is built specifically for accelerating scientific discovery.