floatz | AI Scientific Due Diligence

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.

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.

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?

1. 1

   Connect data sources: Integrate relevant scientific, trial, and IP data.

2. 2

   AI analysis: Allow ML models to process data and infer risk signals.

3. 3

   Review insights: Examine AI-generated conclusions and human-verified findings.

4. 4

   Contextualize claims: Understand scientific assertions within the full evidence system.

5. 5

   Identify risks: Pinpoint hidden scientific vulnerabilities before investment.

6. 6

   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

FAQ from floatz

• What is floatz AI-driven scientific due diligence?

  floatz provides AI-powered scientific due diligence for life-science investors, focusing on identifying hidden scientific risks in biotech and life-science companies before capital is committed.

• How does floatz identify scientific risks?

  floatz uses a knowledge graph to represent interconnected evidence (science, trials, IP, people) and employs ML risk signals to infer weak, negative, and non-obvious risks from fragmented data.

• What is the role of AI in floatz?

  AI, specifically ML models and agentic AI, analyzes the knowledge graph to infer risk signals and translate them into conclusions, which are then human-reviewed for clarity and accountability.

• Why is contextual evaluation important for scientific claims?

  Contextual evaluation ensures that scientific claims are assessed within the full body of evidence, rather than in isolation, revealing a more accurate picture of their credibility and potential risks.

• Who benefits from floatz's services?

  Life-science investors, venture capitalists, and investment firms focused on biotech and pharmaceutical sectors who need to de-risk their investment decisions.

• How does floatz improve upon traditional due diligence?

  floatz moves beyond assessing isolated claims by creating an interconnected evidence system and using AI to uncover implicit uncertainties and overlooked negative signals that traditional methods might miss.

• What kind of data does floatz analyze?

  floatz analyzes scientific literature, clinical trial data, intellectual property information, and details about key personnel involved in life-science ventures.

• Can floatz help uncover 'hidden' scientific risks?

  Yes, floatz is designed to surface hidden scientific risks by analyzing fragmented and noisy evidence to infer subtle, negative, and non-obvious risk signals.

• What is the output of floatz's due diligence process?

  The output is decision-grade scientific diligence, providing clear and accountable conclusions that support defensible investment decisions.

• Is floatz affiliated with any research institutions?

  floatz is a partner of the ETH AI Center, indicating a connection to advanced AI research and development.