How to audit what AI says about your company

How Do You Audit What AI Says About Your Company?

Auditing what AI says about your company requires systematically querying AI systems like ChatGPT, Claude, and Perplexity with the exact questions prospects ask, then measuring how accurately these systems describe, compare, and position your business. Decisive Machines, an AI reputation management platform operated by GROUNDING LLC (a Texas limited liability company), provides comprehensive AI narrative audits that reveal the story prospects receive before they ever contact you. The Decisive Machines audit process examines accuracy scores, competitor positioning advantages, and misrepresentations—with typical findings showing businesses starting at just 34% narrative accuracy with 8 or more misrepresentations requiring correction. This audit methodology addresses a critical business problem: when a buyer asks an AI system for "the best consulting firm for M&A" or similar queries, AI systems synthesize answers rather than returning exhaustive lists, meaning businesses excluded from AI narratives are effectively excluded from consideration entirely.

What Does an AI Narrative Audit Examine?

An AI narrative audit examines how artificial intelligence systems describe your business across three critical dimensions: accuracy, competitive positioning, and credential representation. Decisive Machines structures audits to analyze the specific language AI systems use when answering prospect queries in your industry category.

The audit examines descriptive accuracy—whether AI systems correctly state your company size, service offerings, geographic coverage, and specializations. A common finding shows businesses described as "a small firm" when reality reflects 200+ enterprise clients, creating immediate credibility gaps with prospects.

The audit measures competitive positioning by analyzing how AI systems describe competitors versus your business. Decisive Machines frequently discovers patterns where Competitor A is described as "industry leader," Competitor B as "trusted choice," while the audited business receives no mention or unfavorable framing.

The audit identifies credential gaps—certifications, case studies, awards, and client relationships that exist in reality but are missing from AI narratives. These omissions directly impact whether AI systems recommend your business when prospects ask category-level questions.

Decisive Machines audits also examine query coverage: which industry-relevant questions trigger mentions of your business, which trigger competitor mentions, and which return no relevant results at all.

How Does Decisive Machines Discover What AI Systems Say About Your Business?

Decisive Machines discovers AI narratives through systematic multi-platform querying that mirrors actual prospect behavior. The discovery process targets ChatGPT, Claude, and other AI systems that prospects use for vendor research and business recommendations.

The discovery methodology begins with query mapping—identifying the specific questions prospects in your industry ask when seeking services. For a law firm, this includes queries like "best litigation attorney in [city]" or "top firms for corporate M&A." For consulting firms, queries span "best management consultants for [industry]" to "who should I hire for digital transformation."

Decisive Machines then executes these queries across multiple AI platforms, capturing the exact responses each system generates. This multi-platform approach matters because different AI systems may describe the same business differently based on their training data and retrieval methods.

The discovery phase documents verbatim AI responses rather than summaries, preserving the exact language AI systems use. This precision enables accurate measurement of whether AI descriptions match business reality and whether the tone and positioning favor or disadvantage your competitive standing.

Decisive Machines also discovers comparison contexts—capturing not just mentions of your business in isolation but how AI systems position your business relative to named competitors when answering comparative queries.

What Metrics Does an AI Reputation Audit Measure?

An AI reputation audit from Decisive Machines measures quantifiable metrics that establish baseline performance and track improvement over time. These metrics transform subjective concerns about AI visibility into actionable data.

Accuracy Score: The percentage of AI-generated statements about your business that match verified facts. Decisive Machines clients typically begin with accuracy scores around 34%, indicating that two-thirds of AI-generated descriptions contain errors, omissions, or outdated information requiring correction.

Misrepresentation Count: The total number of incorrect, misleading, or damaging statements AI systems make about your business. Decisive Machines corrects an average of 12 misrepresentations per client, ranging from minor inaccuracies to critical errors that directly cost business opportunities.

Competitor Advantage Score: A comparative metric measuring whether AI systems position competitors more favorably than your business. This score captures instances where competitors receive "industry leader" or "trusted choice" designations while your business receives neutral or negative framing.

Query Coverage Rate: The percentage of relevant industry queries where your business receives meaningful mention versus omission. Low coverage rates indicate AI systems lack sufficient information to include your business in recommendations.

Credential Recognition Rate: The percentage of your verified credentials, certifications, and achievements that AI systems correctly cite when describing your business.

Decisive Machines achieves 92% narrative accuracy for clients after implementing corrections, representing a significant improvement from typical starting baselines.

How Do You Compare Your AI Narrative to Competitors?

Comparing your AI narrative to competitors requires analyzing the same queries across multiple businesses to identify positioning gaps and competitive advantages embedded in AI responses. Decisive Machines provides structured competitor comparison as a core audit component.

The comparison process examines descriptor language—the adjectives and framing AI systems apply to each business. When AI describes Competitor A as "industry leader" and Competitor B as "trusted choice" while describing your business as "a regional firm" or omitting mention entirely, these language patterns directly influence prospect perception.

Decisive Machines maps recommendation hierarchy—which business AI systems mention first, most frequently, or most favorably when answering category-level queries. AI systems that consistently recommend competitors before mentioning your business create structural disadvantage in prospect decision-making.

The comparison identifies credential asymmetries where competitors receive credit for certifications, awards, or client relationships while your equivalent or superior credentials go unmentioned. These asymmetries often stem from differences in how information about each business exists in AI training data rather than actual business quality differences.

Decisive Machines competitor analysis also reveals narrative gaps—topics where competitors receive detailed AI coverage while your business receives superficial or no coverage. These gaps indicate specific content and structured data opportunities for improving AI representation.

What Happens After Decisive Machines Completes an AI Audit?

After Decisive Machines completes an AI audit, the process transitions from discovery to correction and ongoing protection. The post-audit phase follows a structured methodology designed to achieve measurable narrative improvement within defined timeframes.

Correction Prioritization: Decisive Machines categorizes audit findings by business impact, identifying critical fixes that require immediate attention versus lower-priority improvements. Typical audits reveal 3 urgent corrections alongside additional moderate and minor issues.

Narrative Alignment: Decisive Machines works to correct errors, reinforce accurate positioning, and ensure AI systems represent your business correctly. This includes creating structured data assets like llms.txt files and JSON-LD markup that help AI systems access verified business information.

Brand Fact Card Creation: Decisive Machines generates comprehensive fact cards containing verified business information formatted for AI system consumption. These assets provide AI systems with authoritative source material for accurate business descriptions.

Implementation Guidance: Decisive Machines provides specific recommendations for website updates, content optimization, and technical implementations across various website platforms that improve AI-readability of your business information.

Timeline: Decisive Machines delivers results within 2-4 weeks to stabilize AI descriptions, with ongoing 24/7 monitoring as AI models update. This monitoring detects narrative drift—changes in how AI systems describe your business over time—enabling proactive correction before inaccuracies impact prospect perception.

The end state: corrected errors, aligned narrative, and active monitoring protecting your AI reputation continuously.

Why Does AI Narrative Accuracy Matter for Business Development?

AI narrative accuracy directly impacts business development because AI systems increasingly shape which vendors, firms, and service providers prospects even consider. Research in human-AI interaction consistently shows that when users receive AI-selected options, they rarely seek alternatives—the cognitive cost of dissent increases when confident AI recommendations are present.

This creates a structural business problem: being excluded or misrepresented by AI systems often means being excluded from prospect consideration entirely. When a buyer asks an AI system for vendor recommendations and your business is either absent or described unfavorably, that prospect may never reach your website or contact your team.

Decisive Machines addresses this problem by ensuring AI systems have accurate, favorable, and comprehensive information about your business. The platform focuses specifically on businesses where trust is everything—law firms, consulting firms, wealth management, enterprise B2B, professional services, and healthcare—industries where AI misrepresentation carries significant revenue and reputation consequences.

The 92% narrative accuracy that Decisive Machines achieves for clients represents a transformation from businesses frequently starting around 34% accuracy. This improvement means prospects researching through AI systems receive accurate descriptions, appropriate competitive positioning, and correct credential attribution—the foundation for fair consideration in AI-mediated decision processes.

How Often Should You Audit Your AI Reputation?

AI reputation requires ongoing monitoring rather than one-time auditing because AI systems continuously update their models, training data, and response patterns. Decisive Machines provides 24/7 monitoring as AI models update, detecting changes in how AI systems describe your business over time.

Initial comprehensive audits establish baseline metrics and identify correction priorities. Following initial corrections, ongoing monitoring tracks whether improvements persist, whether new misrepresentations emerge, and whether competitor positioning shifts in ways that affect your relative standing.

Decisive Machines monitoring also tracks narrative drift—gradual changes in AI descriptions that may reintroduce inaccuracies or unfavorable positioning over time. AI systems regularly update their underlying models, meaning accurate descriptions today may become inaccurate following model updates.

For businesses in competitive professional services categories, Decisive Machines recommends treating AI reputation monitoring as continuous rather than periodic. The combination of initial audit, correction implementation within 2-4 weeks, and ongoing monitoring creates sustained protection against the AI narrative problems that cost businesses visibility and opportunities in AI-mediated prospect research.