The Benchmarking Blindspot 

Why Product-Level Insight Beats Sector-Level Assumption

Executive Summary

Benchmarking is a widely used tactic in investment analysis, yet it is fundamentally flawed when

it relies on sector-based assumptions rather than product-level data. This white paper

challenges the validity of traditional benchmarking and explains why emerging AI tools,

especially LLMs, struggle to correct the problem. It presents Elute’s Insights IDS as a solution

built to deliver high-fidelity, product-level comparisons based on global patent data, offering

clarity where conventional methods deliver noise.

1. Introduction

Benchmarking is often accepted as a practical proxy for understanding competitive positioning.

But while the method appears objective and analytical, its core weakness is systemic, as it

usually relies on pre-defined peer groups that share a sector, not a product or technological

foundation. The result is that the companies chosen for comparison may be familiar names, but

not relevant threats or true analogues.

2. The Benchmarking Blindspot

Most benchmarking exercises begin with who is *known*, not who is *similar*. Analysts reach for

accessible, well-known competitors that match by geography, revenue range, or vague sector

labels. These peer sets are rarely validated for actual technological similarity or innovation

overlap.

This leads to a persistent blindspot in investment decisions:

• Underestimating emerging threats or disruptors with unrecognised IP

• Overvaluing incumbents based on assumed comparability

• Missing high-potential investments due to inaccurate peer framing

3. Why AI and LLMs Struggle with Benchmarking Too

Many assume AI will solve this problem. But large language models (LLMs) are not optimised for

structural analysis of documents. They use probabilistic language generation, not deterministic

comparison. As a result, LLMs:

• May hallucinate company relationships

• Cannot reliably distinguish technical similarity from surface-level narrative

• Often reinforce existing bias by echoing familiar names from training data

In summary, LLMs inherit the same flawed benchmarking assumptions, but with more

confidence and less explainability. See our white paper, “Consistency over Guesswork”.

4. The Elute Alternative: Insights IDS and Product-Level Comparison

Insights IDS bypasses these flaws. Rather than accepting predefined peer groups, it builds

similarity from the ground up:

• Compares the actual content of 140 million patents using linguistic and distributional

algorithms

• Surfaces competitors that share technical invention DNA

• Delivers replicable, auditable results that reflect real innovation overlap, not surface

resemblance

The result is benchmarking built on evidence, not assumption. It highlights:

• Genuine competitors (even if previously unknown)

• Uniqueness in the investee’s IP

• Technology unknowns and untapped investment themes

5. Strategic Value to Investors

Product-level benchmarking shifts the entire investment decision stack:

• Improves due diligence by validating IP claims and identifying risks

• Enhances pipeline creation with previously overlooked but genuinely similar targets

• Strengthens portfolio strategy with evidence to support decision making

• Helps investors spot early signals before their widely recognized

6. Conclusion

Traditional benchmarking is no longer sufficient, if it ever was. The tools of yesterday and the AI

tools of today both fall short when it comes to real technological insight. Elute’s Insights IDS

provides a product-level lens that cuts through noise and reveals the true competitive

landscape.

For investors seeking clarity, consistency, and a genuine edge, this is benchmarking redefined.