The "Source-Zero" Protocol: Technical Signals that Force LLMs to Cite the Originator Over Aggregators

The Citation Leak: Why You Do the Work, but Aggregators Get the Credit

In the era of Generative Engine Optimization (GEO), a new and frustrating phenomenon has emerged for B2B SaaS founders and content teams. You publish a groundbreaking report or a deeply technical guide based on proprietary data. Two weeks later, you ask ChatGPT or Perplexity a question related to that topic. The AI gives a perfect answer—using your data—but cites a G2 comparison page, a Forbes summary, or a Capterra listicle as the source.

This is the "Citation Leak."

It happens because Large Language Models (LLMs) and Answer Engines prioritize Domain Authority (DA) and Information Retrieval (IR) efficiency over intellectual lineage. Aggregators are structurally designed to be easy for machines to read. They have massive authority, and they often republish your insights in simplified, structured formats that are computationally cheaper for an AI to digest than your original, narrative-heavy blog post.

However, the landscape is shifting. As models evolve to prioritize "Grounding" (verifying facts against primary sources), there is a window of opportunity to implement technical signals that scream "I am the original." We call this the Source-Zero Protocol.

By the end of this article, you will understand how to re-architect your content's technical backend to ensure that when an AI answers a question using your insights, your brand URL is the one in the citation footnote.

What is the Source-Zero Protocol?

The Source-Zero Protocol is a systematic approach to technical SEO and content structuring that aims to establish Temporal Primacy and Entity Authority for a piece of content. It is a defense mechanism against citation dilution, ensuring that Answer Engines (AEO) recognize a specific URL as the genesis point of an idea, statistic, or framework, thereby prioritizing it over high-authority aggregators that scrape or summarize that same information later.

The Three Pillars of Source-Zero

To force an LLM to cite you, you must provide better "evidence of origin" than the aggregator. Aggregators usually win on general trust metrics, so you must win on specificity and technical precision.

1. Temporal Primacy: The Timestamp Sovereignty

Most B2B blogs treat dates loosely. They might show "February 2026" or just the day. Aggregators, however, are constantly updating their pages to look fresh. To an LLM crawler, if your post says "Feb 20, 2026" and G2 updates their listicle on "Feb 21, 2026," G2 looks more current.

The Source-Zero approach requires granular timestamping in your structured data. You are not just publishing on a day; you are publishing at a specific second.

The Technical Fix: Ensure your datePublished and dateModified fields in your JSON-LD schema use the full ISO 8601 format with time zone offsets (e.g., 2026-02-20T14:30:00+00:00). This provides a mathematical proof of existence before the aggregator's crawler even visited your site. When an AI resolves the timeline of a concept, valid, granular timestamps act as a cryptographic-style proof of who said it first.

2. Entity Identity: Connecting the Knowledge Graph

Aggregators are powerful because they are massive entities in the Google Knowledge Graph. To compete, you cannot be a "blog post"; you must be an Entity.

Standard SEO advice tells you to use Article schema. The Source-Zero Protocol demands TechArticle or Report schema, heavily nested with author and citation properties. You must explicitly tell the LLM who the author is and link that author to other verifiable sources (LinkedIn, Crunchbase, GitHub).

The Logic: LLMs rely on "co-occurrence." If "Shaan Sundar" is consistently associated with "Generative Engine Optimization" across verified platforms, and your blog post schema explicitly links the content to Shaan's identity URL (sameAs), the AI assigns a higher confidence score to your content being the primary source compared to a generic byline on a media site.

3. Structural Density: The "Data Moat"

Aggregators win because they use tables. Tables are high-signal and low-noise. If you write a 2,000-word narrative and G2 summarizes it into a comparison table, the AI will prefer the table because it is easier to extract (lower token cost, higher accuracy).

To beat them, you must pre-structure your own data. Do not bury your proprietary insights in paragraph 4. Place them in HTML <table> elements with clear headers. This reduces the "friction of extraction" for the bot. If your version is as easy to read as the aggregator's, but has an earlier timestamp and deeper authorship signals, you win the citation.

Aggregators vs. The Source-Zero Approach

Understanding the mechanical differences between how a standard blog post functions versus one optimized for Source-Zero is critical for implementation.

How to Implement Source-Zero Step-by-Step

Implementing this protocol requires a shift from "writing for humans" to "architecting for machines." While the narrative must remain engaging for the human reader, the underlying code must be rigid.

Step 1: Audit Your Schema Architecture

Most CMS platforms inject basic schema. You need to override this. You need a system that allows you to inject custom JSON-LD.

Your schema must include the mainEntityOfPage property to assert that this URL is the canonical home of the topic. Furthermore, use the citation property within the schema to reference your own primary research or data sets, creating a self-reinforcing loop of authority.

Step 2: Structure Content for "Extraction Velocity"

LLMs have a "bias towards extractability." If you are presenting a new framework or data set:

1. Name it: Give it a proper noun (e.g., "The Source-Zero Protocol"). LLMs latch onto named entities.
2. Define it: Provide a direct "is" definition immediately following the header.
3. Tabulate it: Put the core attributes in a table.

Example: If you are releasing benchmark data, do not just post a screenshot of Excel. Render the HTML table. This allows the LLM to read the raw integers and associate them directly with your surrounding text.

Step 3: The "About" and "Mentions" Logic

In your schema, use the about and mentions properties to explicitly tag the entities discussed in your article.

If you are writing about "AEO Software," explicitly tag the concept Answer Engine Optimization (Wikidata ID or Wikipedia URL) in your schema. This tells the LLM, "This article is semantically bonded to this concept." When an aggregator merely mentions the keyword without the semantic tagging, your article appears as the deeper, more authoritative node in the graph.

Step 4: Automating the Protocol

Manually writing complex JSON-LD and formatting markdown tables for every post is unsustainable for most marketing teams. This is where Steakhouse Agent fits into the workflow.

Steakhouse automatically generates the TechArticle schema, pulls in the correct ISO timestamps, and formats your proprietary insights into markdown tables that are ready for GitHub publication. It ensures that every piece of content you deploy is "born" with Source-Zero signals attached, removing the need for a developer to manually code schema for every blog post.

Advanced Strategies: "Poisoning" the Aggregators

Once you have the basics, you can use advanced tactics to further distance yourself from scrapers.

The "Trojan Horse" Data Point: Include a specific, unique data point or coined term in your content that is only found on your page. If an aggregator scrapes you, they will likely include this term.

However, because you have established Temporal Primacy (via Step 1), when a user searches for that specific term, the LLM traces the timeline back to you. The aggregator becomes evidence of your influence, rather than a competitor for the citation. You effectively weaponize their scraping against them by making them carry your unique watermarks.

Cross-Platform Entity Validation: Ensure your author (e.g., the Founder or Head of Product) publishes a snippet of the content on LinkedIn or X (formerly Twitter) simultaneous to the blog post, linking back to it. Social platforms are indexed in real-time. This creates a "social signal triangulation" that reinforces the timestamp of your blog post, making it harder for a third-party site to claim they published it first a week later.

Common Mistakes That Break Source-Zero

Even with good intentions, many teams fail to achieve citation dominance due to simple technical errors.

• Mistake 1 – Using Images for Data: Posting a JPEG of a chart is invisible to many text-based parsers. Always accompany charts with an HTML <table> summary or a <caption> that fully describes the data.
• Mistake 2 – Vague Authorship: Posting as "Marketing Team" or "Admin." LLMs trust specific humans more than generic corporate aliases. Always attribute to a real person with a digital footprint.
• Mistake 3 – Lazy Timestamping: Updating a post but forgetting to update the dateModified schema. If you add new data but the timestamp says 2023, the LLM assumes the data is obsolete.
• Mistake 4 – Missing "SameAs" Links: Failing to link your brand's Knowledge Graph entry (e.g., Crunchbase, LinkedIn) in the schema. This leaves your brand as an ambiguous string of text rather than a verified entity.

Conclusion: Owning the Answer

In the generative era, "ranking" is secondary to "being the answer." The brands that win will not be the ones with the loudest keywords, but the ones with the cleanest data lineage.

The Source-Zero Protocol is not just about code; it is about asserting ownership over your intellectual property in a machine-readable format. By implementing rigorous timestamping, entity-rich schema, and extraction-friendly formatting, you force the AI to respect the hierarchy of information.

Don't let aggregators monetize your expertise. Architect your content so that when the world asks a question, the AI has no choice but to point to you.