Intrinsic Valuation
Estimate value from the fundamentals of the business rather than from short-term price movement.
Methodology
Valuation should be structured, explainable, and assumption-led.
astrys.ai is built around fundamental intrinsic valuation principles, with software workflows designed to make assumptions easier to see, test, and refine.
Philosophy
The model is not the insight. The assumptions are.
Valuation is a structured reasoning process: connect business assumptions to cash flows, risk, reinvestment, and value. astrys.ai is designed to support that process without turning valuation into a black box.
- Assumptions before outputs
- Business logic before formulas
- Transparency before automation
Methodology Map
From business narrative to value.
astrys.ai connects business narratives to valuation outputs through structured assumptions and auditable model drivers.
Business Assumptions
Forecast Cash Flows
Risk & Discounting
Intrinsic Value
Revenue GrowthMarginsReinvestment NeedsCost of CapitalScenario Narratives
Core Methodology Modules
Fundamental concepts, structured for software.
The platform is organized around valuation concepts that are widely used in fundamental investment research.
Discounted Cash Flow
Connect operating forecasts to future free cash flows and value them through an explicit forecast and terminal value framework.
Cost of Capital
Reflect business and financing risk through assumptions around equity risk, debt risk, and capital structure.
Country Risk
Account for geographic exposure and market risk when estimating the risk profile of a company.
Bottom-Up Beta
Estimate business risk from industry exposure rather than relying only on a single historical regression beta.
R&D Capitalization
Treat durable research and development spending as an investment when it better reflects the economics of the business.
Scenario Analysis
Test how valuation changes across different narratives, assumptions, and uncertainty ranges.
Transparency By Design
Understand the valuation, not just the output.
astrys.ai is designed around the idea that users should understand the valuation, not just receive a final number.
Visible assumptions
Key assumptions should be visible, editable, and connected to valuation outputs.
Traceable outputs
Valuation results should connect back to the drivers and assumptions that produced them.
Explicit scenarios
Different narratives should be modeled directly rather than hidden inside a single point estimate.
Assistive AI
AI-assisted workflows should support research and explanation without replacing valuation judgment.
Evolving Library
Methodology library coming later
This page currently provides a high-level overview of the valuation principles behind astrys.ai. More detailed methodology notes, examples, and model documentation will be added as the platform matures.
For now, the goal is to provide enough context to understand the valuation philosophy without turning this page into full technical documentation.
astrys.ai
Build valuations from assumptions, not black boxes.
Explore structured valuation workflows with astrys.ai.
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