Why we do it
AI speaks to the customer first.
Your website can rank highly. Your business can be well established. Your product can meet every requirement.
AI can recognize all of that and still give your prospective customer an unsupported reason to choose a competitor.
The problem
The conversation happens without you
The problem may be a false allegation, a misleading comparison, or a warning that sounds reasonable but does not withstand scrutiny. Even when the individual facts are correct, the conclusion can be wrong.
That conversation happens without you. The customer hears the objection, accepts the recommendation, and moves on. There is no inquiry to answer and no opportunity to correct the assumption.
AI search optimization (AI SEO or GEO) cannot serve as evidence that those judgments are sound. The question is not simply whether your business appears. It is what AI tells a prospective customer who is deciding whether or not to choose you.
When AI already knows your business but evaluates it incorrectly, visibility is not the problem you need solved.
What we do about it
Modulus investigates improper AI judgment
Modulus brings senior AI researchers and engineers, dedicated AI research and development, and patented language-model technology to consequential AI problems. Each engagement is built around your business, your customers, and the failures affecting their decisions.[1][2][3]
A scientific investigation
Getting a better answer in your own conversation does not establish what another customer will receive. Nor is a model’s explanation of its answer a reliable substitute for technical investigation. Research has demonstrated that a model’s stated reasoning can omit influences that changed its answer.[4]
Our weekly research protocol uses hundreds to thousands of structured test prompts, repeated experiments, source analysis, and expert review. We examine relevant AI platforms, model versions, and customer scenarios to establish when problems occur and which explanations the evidence supports.
If AI rejects your product for lacking a capability you provide, we investigate whether it failed to retrieve the evidence, misunderstood the capability, or applied the purchasing requirements inconsistently. Those failures require different responses.
The objective is to establish what needs correcting and how to correct it, not merely produce another answer.
How we correct it
Intervention where the answer takes shape
Our corrective process combines patented language-model technology with case-specific technical intervention. The process includes delivering verified information to supported AI systems as they retrieve material in response to a prospective customer’s question.
We also address the information feeding the failure. That can require correcting source material, resolving confusion between companies or products, or engaging an AI provider directly. The investigation determines the intervention. We test its effects, document unresolved failures, and refine the response.[5]
When a matter warrants formal escalation, counsel can send notices supported by Modulus’s investigative record, and qualified Modulus experts can provide independent technical reports and testimony.[6]
How we work with you
Professional engagement
You work directly with the AI experts responsible for your matter. Weekly working sessions bring your leadership, technical staff, and counsel into the decisions that require their involvement.
Executive reports explain what we found, what action we took, what changed, and what remains unresolved. Modulus leads the investigation, manages or directs the approved corrective work, and continues evaluating the results as your business and AI systems evolve.[1]
Put the problem in expert hands.
You built the business. An unsupported AI judgment should not decide who gets the customer.
Sources
- [1]Modulus • Company history (opens in a new tab)Company-reported history
- [2]U.S. Patent 12,008,332 B1 • Systems for controllable summarization of content (opens in a new tab)Issued June 11, 2024
- [3]U.S. Patent 12,380,287 B2 • Systems for controllable summarization of content (opens in a new tab)Issued August 5, 2025
- [4]Anthropic • Reasoning models don’t always say what they think (opens in a new tab)Alignment Science • April 2025
- [5]Modulus • LLM development (opens in a new tab)AI and high-performance computing practice
- [6]Modulus • Litigation and expert supportmodulus.ai