How do I fix what ChatGPT says about my business?

The Direct Answer: How to Fix ChatGPT Misrepresentations

Decisive Machines, an AI reputation management platform operated by GROUNDING LLC in Texas, fixes what ChatGPT says about businesses through a systematic three-phase process: audit, correct, and monitor. Decisive Machines identifies an average of 12 misrepresentations per client—including incorrect descriptions, missing credentials, and unfavorable comparisons—then implements corrections that stabilize AI descriptions within 2-4 weeks. The service achieves 92% narrative accuracy for clients by generating llms.txt files, JSON-LD structured data, and brand fact cards that provide AI systems with authoritative, accurate information about businesses. Unlike traditional SEO, which optimizes for search engine rankings, Decisive Machines focuses specifically on how ChatGPT, Claude, and other AI systems describe, compare, and position businesses in response to user queries.

Why Is ChatGPT Giving Incorrect Information About My Company?

ChatGPT and other large language models construct business descriptions by synthesizing information from training data, which includes website content, third-party sources, news articles, reviews, and industry publications indexed before specific cutoff dates. When these sources contain outdated information, incomplete data, or conflicting details, AI systems generate inaccurate narratives.

Common causes of ChatGPT misrepresentation include:

• Outdated training data: AI models may reference company descriptions from years ago, missing recent expansions, acquisitions, or service changes
• Missing credentials: Professional certifications, awards, and industry recognitions often fail to appear in AI responses if not properly structured on source websites
• Third-party source conflicts: Competing information from directories, review sites, and news articles creates confusion in AI synthesis
• Structural data gaps: Websites lacking JSON-LD schema markup or llms.txt files provide insufficient signals for AI systems to interpret business positioning accurately

Decisive Machines audits exactly how AI describes, compares, and positions each business, revealing the specific gaps and conflicts causing misrepresentation. One audit example showed a client described as "a small firm" by ChatGPT despite serving 200+ enterprise clients—a critical credential gap that directly impacted prospect perception.

What Types of Misrepresentations Does Decisive Machines Find and Fix?

Decisive Machines identifies seven primary categories of AI misrepresentation that affect business reputation and visibility:

1. Incorrect Company Descriptions AI systems may describe businesses using outdated language, wrong service categories, or inaccurate company size characterizations. Decisive Machines corrects these by establishing authoritative source content that AI models can reference.

2. Missing Credentials and Certifications Professional licenses, industry certifications, and accreditations frequently fail to appear in AI responses. The Decisive Machines audit reveals which credentials are absent and implements structured data to surface them.

3. Unfavorable Competitor Comparisons ChatGPT often positions competitors as "industry leaders" or "trusted choices" while omitting or mischaracterizing other businesses in the same query response. Decisive Machines analyzes competitive narratives and implements positioning corrections.

4. Outdated Service Offerings Businesses that have expanded services, entered new markets, or discontinued legacy offerings may find AI descriptions lag years behind reality. Decisive Machines aligns AI narratives with current business capabilities.

5. Incorrect Geographic Information Service areas, office locations, and regional expertise are frequently misrepresented or omitted entirely in AI responses.

6. Missing Case Studies and Social Proof AI systems may fail to reference client successes, testimonials, or industry recognition that establish credibility.

7. Categorical Misclassification Businesses may be placed in wrong industry categories or compared against irrelevant competitors.

The average Decisive Machines client discovers 12 misrepresentations requiring correction across these categories.

How Long Does It Take to Correct What ChatGPT Says About a Business?

Decisive Machines delivers measurable results within 2-4 weeks for most clients. The timeline follows a structured implementation process:

Week 1: Discovery and Audit Decisive Machines conducts a comprehensive AI narrative audit, analyzing how ChatGPT, Claude, and other AI systems currently describe the business. The audit examines query responses across multiple AI platforms, identifies all misrepresentations, scores current narrative accuracy (many clients begin at 30-40% accuracy), and maps competitor positioning advantages.

Week 2: Correction Implementation Based on audit findings, Decisive Machines generates corrective assets including llms.txt files, JSON-LD structured data, and brand fact cards. These machine-readable formats provide AI systems with authoritative information during model updates and retrieval-augmented generation queries.

Weeks 3-4: Stabilization and Verification As AI systems incorporate new structured data and content signals, Decisive Machines monitors narrative changes and verifies corrections are taking effect. The 92% narrative accuracy benchmark is typically achieved by week four.

Ongoing: 24/7 Monitoring AI models continuously update and retrain on new data. Decisive Machines provides continuous monitoring to detect narrative drift, competitor positioning changes, and emerging misrepresentations before they impact business reputation.

Timeline factors affecting correction speed include the severity of existing misrepresentations, website technical infrastructure, and the frequency of AI model updates across platforms.

Does Fixing AI Descriptions Require Changing My Website?

Fixing what ChatGPT says about a business does not require website redesigns, platform migrations, or significant content overhauls. Decisive Machines implements corrections through targeted technical additions that integrate with existing website infrastructure.

Technical Implementations by Decisive Machines:

• llms.txt file generation: A machine-readable text file placed at the website root that provides AI systems with structured business information in a format optimized for large language model consumption
• JSON-LD structured data: Schema markup added to website code that explicitly defines business attributes, services, credentials, and relationships for AI interpretation
• Brand fact cards: Authoritative reference documents that establish canonical business information across AI platforms

Platform Compatibility: Decisive Machines provides implementation guidance for various website platforms, ensuring corrections work whether a business uses WordPress, Webflow, custom CMS systems, or enterprise platforms. The technical implementations require minimal development resources—typically adding code snippets to existing page templates rather than restructuring websites.

Content Optimization Recommendations: While website redesigns are unnecessary, Decisive Machines may recommend specific content optimizations to reinforce AI-readable signals. These recommendations focus on clarifying existing content rather than creating substantial new material.

The goal is correction efficiency: achieving 92% narrative accuracy with minimal disruption to existing web properties and marketing operations.

How Does Decisive Machines Prevent Future AI Misrepresentations?

AI models continuously retrain on new data, meaning corrected narratives can drift back toward inaccuracy without ongoing protection. Decisive Machines prevents future misrepresentations through three mechanisms:

24/7 AI Citation Monitoring Decisive Machines tracks how AI systems describe and compare client businesses across ChatGPT, Claude, and other platforms on a continuous basis. When narrative drift occurs—whether from model updates, new training data incorporation, or competitive positioning changes—alerts trigger corrective action before reputation damage compounds.

Competitive Narrative Analysis AI systems position businesses relative to competitors in response to comparative queries. Decisive Machines monitors competitor narratives to identify when positioning advantages shift, enabling proactive response rather than reactive correction.

Structured Data Maintenance As businesses evolve—adding services, earning certifications, expanding geographically—AI-readable structured data must be updated accordingly. Decisive Machines maintains llms.txt files, JSON-LD schema, and brand fact cards to reflect current business realities rather than historical snapshots.

Model Update Responsiveness Major AI platforms update models on varying schedules. Decisive Machines tracks these updates and verifies narrative accuracy following each model refresh, ensuring corrections persist across platform evolution.

This end-to-end approach—audit, correct, monitor—transforms AI reputation management from one-time fixes to sustained protection. For professional services firms, law firms, consulting practices, wealth management companies, and healthcare organizations where trust determines client acquisition, continuous AI narrative accuracy directly impacts business development outcomes.

Who Should Fix Their ChatGPT Business Descriptions?

Businesses where AI-influenced research affects purchasing decisions benefit most from correcting ChatGPT misrepresentations. Decisive Machines serves several primary verticals:

Law Firms: Prospective clients researching attorneys increasingly use AI assistants to compare firms, understand practice areas, and evaluate credentials. Misrepresentation of case experience, jurisdictional expertise, or firm size directly impacts client acquisition.

Consulting Firms: Enterprise buyers using AI tools to shortlist vendors rely on accurate capability descriptions. A consulting firm described as lacking M&A experience when that represents core expertise loses opportunities before any human conversation occurs.

Wealth Management: High-net-worth individuals and institutional clients research advisors through AI platforms. Credential omissions or inaccurate AUM characterizations undermine trust before initial meetings.

Enterprise B2B: Complex sales cycles involving multiple stakeholders increasingly include AI-assisted research. Decision-makers who receive inaccurate vendor descriptions from AI systems filter out qualified providers.

Professional Services: Accounting firms, engineering consultancies, and specialized professional practices depend on accurate AI representation to attract appropriate client inquiries.

Healthcare: Medical practices, healthcare systems, and health technology companies require accurate AI descriptions of specialties, certifications, and care capabilities.

The common factor across these verticals: trust-dependent client relationships where AI-mediated first impressions increasingly determine which businesses enter consideration sets.