Analysis Blocks: Rethinking Financial Research as Composable Insights

Traditional research reports are monolithic documents—static, single-use, and difficult to build upon. What if we thought about financial research not as reports, but as composable, reusable insights that compound in value over time? That's the core idea behind Analysis Blocks.

The Problem with Traditional Research Reports

When you complete a financial analysis today, what happens to it? Typically, it becomes a PDF or document that lives in a folder somewhere. Maybe you or your team references it occasionally. But largely, the insights—and more importantly, the thinking process behind them—are locked away in static form.

Six months later, when you need to analyze the same company or a similar situation, you often start from scratch. Sure, you might skim your old report for reference, but you can't easily:

• ×Update it with new data without rewriting everything
• ×Extract specific analyses (like your margin analysis) to apply to other companies
• ×Compare your conclusions across multiple similar analyses
• ×Build new analyses on top of previous work systematically

Each research report becomes a dead end. The insights might be valuable, but they don't compound. You can't build a growing library of reusable analytical components that get more valuable the more you use them.

Introducing Analysis Blocks: Modular, Composable Research

Analysis Blocks represent a fundamentally different approach to financial research. Instead of monolithic reports, think of your analysis as composed of discrete, reusable blocks—each one a self-contained insight that can stand alone or combine with others.

How Analysis Blocks Work in Practice

Let's walk through a concrete example. Suppose you're analyzing a SaaS company's unit economics. With traditional tools, you might create a spreadsheet model and write up your findings.

With Analysis Blocks, you create:

Now here's where it gets powerful: Each of these blocks exists independently. When you analyze the next SaaS company, you can reuse Blocks 1-3 directly, just pointing them at different data sources. Your CAC Payback Analysis block becomes a standardized framework you apply consistently across companies, making comparisons meaningful and saving you from rebuilding the analysis each time.

Even better: Over time, as you analyze more SaaS companies, your blocks get smarter. They can automatically reference your previous analyses for comparative context. "This company's CAC payback of 18 months compares favorably to the 24-month median you've seen across 15 similar companies."

The Compounding Effect of Reusable Analysis

The real magic of Analysis Blocks is how they compound in value. Every analysis you conduct doesn't just answer today's question—it creates reusable components for future work.

After analyzing 10 SaaS companies using blocks:

• →Your blocks have been refined and improved based on real usage
• →You have a built-in comparison set for evaluating new companies
• →Your analysis of company #11 takes a fraction of the time company #1 did
• →You can spot outliers and anomalies instantly because you have context
• →Your institutional knowledge is captured in reusable form, not locked in individual brains

This is the shift from linear research (each analysis is independent) to compounding research (each analysis builds on everything that came before).

How ZenAsset Implements Analysis Blocks

At ZenAsset, Analysis Blocks are the fundamental unit of work. When you ask a question or request an analysis, the platform:

Your library of blocks becomes your institutional knowledge base—searchable, reusable, and constantly growing more valuable.

From Static Reports to Living Analysis

The shift from reports to blocks represents a broader transition: from static research artifacts to living, evolving analysis.

When new earnings data comes out, you don't rewrite your analysis—you refresh the relevant blocks with new data. The framework persists; only the inputs change. This makes maintaining up-to-date views effortless.

When you discover a new analytical approach, you can create a new block type and apply it retroactively to all your previous work. Your institutional knowledge doesn't just accumulate—it gets better over time.

This is what modern financial research should look like: modular, composable, and continuously improving. Not starting from zero with each new company or situation, but building on a growing foundation of reusable insights.