Description
Life-science investments are inherently tied to the strength of their underlying scientific foundations. However, scientific claims can appear credible while harboring hidden weaknesses that may lead to failure. Traditional due diligence methods often assess scientific claims in isolation, leading to incomplete evidence evaluation and unstated uncertainties. This fragmented approach can result in significant investment risks.
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floatz addresses these challenges by transforming scientific due diligence into a reliable process. The platform constructs an interconnected evidence system, a knowledge graph, that represents science, clinical trials, intellectual property, and key personnel. This holistic view allows for claims to be evaluated within the full context of available data, rather than as isolated points.
Machine learning models are employed to analyze this knowledge graph, inferring subtle, negative, and non-obvious risk signals from fragmented and often noisy evidence. These ML risk signals are crucial for uncovering potential issues that might be missed by human review alone. The platform then utilizes agentic AI to translate these inferred signals into actionable conclusions.
To ensure clarity and accountability, these AI-generated conclusions undergo human review. This hybrid approach, combining AI's analytical power with human expertise, delivers decision-grade scientific diligence. floatz empowers life-science investors to move from complex scientific data to defensible investment decisions with greater confidence, by surfacing hidden scientific risks before capital is committed.
floatz is a partner for investors seeking to de-risk their portfolios and make more robust investment choices in the dynamic biotech and life-science sectors. By providing a comprehensive and AI-enhanced due diligence process, floatz aims to strengthen the foundation of life-science investments.
- CategoryAI Research Assistants
- Pricing ModelContact sales
- DeploymentCloud
- PlatformsWeb
floatz's Core Features
AI-driven scientific risk intelligence
Knowledge graph for interconnected evidence systems
Machine learning models for risk signal inference
Agentic AI for signal translation
Human review for clarity and accountability
Contextual evaluation of scientific claims
Identification of weak, negative, and non-obvious risk signals
Analysis of fragmented and noisy evidence
Surfacing hidden scientific risks
Decision-grade scientific due diligence
Representation of science, trials, IP, and people
Mitigation of implicit uncertainty in investment decisions
How to use floatz?
Connect data sources: Integrate relevant scientific, trial, and IP data.
AI analysis: Allow ML models to process data and infer risk signals.
Review insights: Examine AI-generated conclusions and human-verified findings.
Contextualize claims: Understand scientific assertions within the full evidence system.
Identify risks: Pinpoint hidden scientific vulnerabilities before investment.
Make decisions: Utilize diligence reports for informed capital allocation.
floatz's Use Cases
- Biotech Investment Risk Assessment
- Life-Science Portfolio De-risking
- Clinical Trial Data Evaluation
- Intellectual Property Due Diligence
- Scientific Claim Validation
- Early-Stage Venture Capital Analysis
