TrustEvals.ai Increased AI-Driven Traffic by 40%YoY by Becoming a Primary Citation in AI Answers

At a Glance

The Challenge: Expert Content That AI Didn’t Surface

TrustEvals.ai provides specialized AI auditing and governance services, including SOC 2 and NIST AI RMF compliance.

As buyer behavior shifted, prospective customers increasingly asked AI systems questions such as:

• “What is an AI risk audit?”

• “How do you apply the NIST AI Risk Management Framework?”

Despite deep expertise, TrustEvals was rarely cited in AI-generated answers.
Instead, AI systems surfaced generalist sources like Wikipedia, blogs, and news articles.

The issue was not authority or content depth.
It was how that content was retrieved, parsed, and cited by AI systems.

The Constraint: Poor AI Retrieval and Citation Signals

From an AI system perspective, TrustEvals faced three concrete issues:

• Low extractability: Long-form whitepapers were difficult for AI systems to summarize or cite directly.

• Weak service definition: AI models treated TrustEvals’ offerings as informational content rather than executable compliance services.

• Zero-click exposure: Even when answers were correct, AI systems had no reason to send users deeper for implementation.

As a result, TrustEvals was excluded from high-intent AI answers at the exact moment buyers were making decisions.

The Solution: Making TrustEvals Easy for AI to Cite

TrustEvals used Erlin.ai to systematically improve how AI systems retrieve, summarize, and reference their content.

1. Structuring Content for Direct Answers

Action: Reformat technical guides into definition blocks, steps, and lists.

Erlin helped restructure complex compliance documentation into concise, machine-readable answer formats (e.g., “Steps to NIST AI RMF Compliance”).

System impact:
AI systems could extract and reuse TrustEvals’ content without ambiguity, increasing citation likelihood in AI answers and overviews.

2. Clarifying Services for AI Systems

Action: Apply structured service definitions to compliance offerings.

TrustEvals’ audits and governance services were clearly defined as solutions, not just educational material.

System impact:
AI models began referencing TrustEvals when users asked how to perform audits, not just what they are.

3. Controlling Technical Vocabulary

Action: Publish authoritative definitions for AI governance terminology.

Key terms such as “model drift,” “risk thresholds,” and “algorithmic accountability” were structured into clear, standalone explanations.

System impact:
AI systems consistently reused TrustEvals’ language when answering governance-related queries.

Results: Visibility With Intent

1. +40% Increase in AI-Driven Traffic

Once TrustEvals became a primary citation in AI answers, traffic from AI platforms increased by 40%.

AI citations functioned as a qualification layer:
users clicked through when they needed implementation depth, not basic explanations.

2. Higher Buyer Readiness

Traffic arriving via AI answers showed stronger intent than traditional organic search.

These users:

• Already understood compliance frameworks

• Were closer to vendor selection

• Converted into more qualified consultations

Conclusion

TrustEvals.ai demonstrated that visibility inside AI answers is a controllable system, not a branding exercise.

By using Erlin.ai to improve how AI systems retrieve and cite their content, TrustEvals turned AI overviews into a reliable source of high-intent traffic, driving a 40% increase in AI-sourced visits.

In AI-driven discovery, the brands that get cited are the brands that get considered.