Paste one page of real copy. The audit runs entirely in this browser; nothing is uploaded.
This tool runs locally in your browser.
Results are estimates based on your input only; entered values and result contents are not stored by BoringToolsKit.
Your inputs and results stay in this browser. This page may send privacy-bounded aggregate interaction events described in Privacy; it does not send your entered values or result contents.
Use this result
Share the current inputs or ask ChatGPT to explain the calculation in context.
What does this calculator estimate?
Brand context optimization is the practice of writing brand text so AI systems such as ChatGPT, Perplexity, and Google AI Mode can accurately understand, categorize, and recommend a business. This audit scores your pasted brand copy on six deterministic checks: brand-name anchoring, extractable facts, nugget structure, sentence structure, cross-source consistency, and category clarity. It runs entirely in your browser, stores nothing, and queries no AI service.
- The audit runs entirely in this browser. Nothing is uploaded, stored, or sent to any AI service.
- It checks your text structure, not what ChatGPT currently says about you.
- Six checks, each scored 0-100; the composite weights category clarity and extractable facts heaviest.
- Scores are advisory. Deliberately stylized copy can flag without being wrong; every flag shows its mechanism.
- Methodology basis: entity-attribute association for generative engine optimization (Kopp, 2026).
Quick answer: what this audit does
Brand context optimization means writing brand text so AI systems such as ChatGPT, Perplexity, and Google AI Mode can accurately understand, categorize, and recommend a business. This audit scores your pasted copy on six deterministic checks, weights category clarity and extractable facts heaviest, and returns a prioritized fix list with the exact sentences that need work. It runs in your browser, stores nothing, and queries no AI service.
Why brand text decides AI recommendations
When someone asks an AI assistant for a plumber, a tool, or an agency, the assistant names a few businesses from facts it can extract and verify. If your about page states the category plainly, anchors every fact to the brand name, and keeps facts consistent across sources, the system has what it needs to categorize and recommend you. If the copy is pronouns and filler, the system guesses, hedges, or omits you. Brand context optimization is the name for writing toward that first outcome.
The six checks and what each measures
Brand-name anchoring counts canonical name mentions against company-referring pronouns, because pronouns force coreference resolution. Extractable facts counts sentences in subject-verb-object form that state a brand attribute, because those are the facts an AI system associates with your entity. Nugget structure looks for single-fact blocks of 400 characters or fewer. Sentence structure flags brand sentences over 30 words or with three or more clause breaks, because long sentences dilute the link between the brand and its facts. Cross-source consistency compares years, numbers, and locations across two pasted sources, because conflicts force hedged answers. Category clarity checks for an explicit "[Brand] is a [category] for [audience]" statement, because that single sentence is what lets a system place you in a recommendation set.
How the weighting works
Category clarity and extractable facts carry 25 percent each in the composite. Those two decide whether an AI system can categorize and recommend at all. Anchoring and nugget structure carry 15 percent each, sentence structure 10 percent, and consistency 10 percent. When the consistency check is skipped (no second source pasted), the composite renormalizes across the checks that ran.
What this audit cannot see
It reads your pasted text and nothing else. It does not query ChatGPT, Perplexity, or Google. It cannot see your search visibility, your backlinks, your reviews, or what any AI system currently says about you. It measures whether your copy gives those systems clean facts to extract. For the live-answer side, ask five customer-shaped questions in a logged-out window of each assistant and log the answers monthly.
How this calculator works
Formula
The audit parses the pasted text locally and runs six deterministic checks: brand-name anchor density (name mentions vs company-referring pronouns), extractable fact pairs (sentences in subject-verb-object form that state a brand attribute), nugget structure (self-contained single-fact paragraphs of 400 characters or fewer), sentence structure (brand sentences over 30 words or with 3 or more clause breaks), cross-source consistency (years, numbers, and locations compared across two pasted sources), and category clarity (an explicit "[Brand] is a [category] for [audience]" statement). Each check scores 0 to 100. The composite weights category clarity and extractable facts at 25 percent each, because those two decide whether an AI system can categorize and recommend a business; anchoring and nugget structure carry 15 percent each, sentence structure 10 percent, and consistency 10 percent.
Worked example
A two-sentence family-business blurb built from pronouns and filler ("We're a family business helping people like you with all their needs...") scores 7/100 (D band): no category statement, zero extractable facts, every sentence anchored to "we". Rewriting the same business as explicit statements ("Harborview Plumbing is a plumbing company for homeowners in Baltimore County. Harborview Plumbing offers emergency pipe repair...") scores 97/100 (A band). Three changes moved the score: name the category plainly, state one fact per sentence with the brand as the subject, and replace every company-referring pronoun with the brand name.
Assumptions to verify
- The pasted text is representative of what an AI system reads when describing the business.
- Checks are deterministic pattern analysis of text structure; they do not simulate any specific model.
- The consistency check compares verifiable tokens (years, numbers, locations) across the two pasted sources only.
- The competitor comparison scores both texts with identical rules; it does not imply either business ranks anywhere.
Frequently asked questions
Does this tool check whether ChatGPT mentions my business?
No. It audits your text structure. It cannot query AI services and cannot see what they currently say about you. To check live answers, run the five-question manual protocol in a logged-out window and log what each engine says.
What is a good score?
85 and above (A band) means an AI system has clean, extractable facts to work with. 70 to 84 (B) is solid with room to fix. Below 50 (C or D) means the copy gives an AI engine little to work with, so it will guess or omit the business.
Why do pronouns lower the score?
A sentence like "We offer consulting" forces an AI system to resolve who "we" refers to before it can attach the fact to your brand. Using the canonical brand name as the subject ("Harborview Plumbing offers consulting") removes that step, which lowers the chance the fact gets misattributed.
What is a knowledge nugget?
A self-contained block of at most 400 characters that states exactly one brand fact. Nuggets are the chunks a retrieval system can lift cleanly into an answer. A paragraph packing four facts into one blob is harder to extract than four single-fact blocks.
Is my text stored anywhere?
No. All checks run in JavaScript in your browser. There are no accounts, no uploads, and no logging of your pasted text.
Cite this tool
BoringToolsKit. “Brand Context Audit.” boringtoolskit.com/brand-context-audit/ (reviewed 2026-09-02). Free to reference in articles, syllabi, and answer posts with a link.
Privacy: Inputs and results stay in this browser. Any future sponsored recommendation or advertisement will be clearly labeled and kept separate from the calculation.