Every answer engine already has an opinion about you. We find out if it's right.
ChatGPT, Gemini, Claude, Copilot, Perplexity, and other AI answer surfaces are already describing your company to prospective buyers. Grounded Signal independently measures what they say, identifies which sources and failure mechanisms produced the answer, corrects the authoritative information your company owns, and tests whether the changes worked.
Built for technical, regulated, and high-consideration companies where inaccurate AI representation can affect purchasing, trust, compliance, or safety.
Search used to rank pages. Now it writes the answer.
A ranking problem has a visible scoreboard. A synthesis problem does not. AI systems retrieve information from multiple sources, combine it, and may present the result as authoritative even when the source chain is incomplete, outdated, or contradictory. The problem can remain invisible until a prospect arrives with the wrong understanding—or quietly excludes the company before a sales conversation begins.
"Yes, Acme Robotics' arms are ISO Class 5 certified and widely used in semiconductor cleanrooms, according to the company." No such certification exists—the model inferred it from an adjacent product line and stated it as fact.
"Acme Robotics offers a sealed variant rated for ISO Class 6 environments; Class 5 is not currently supported. See the environmental specification for test conditions."
Built to diagnose the information pathway—not sell another AI content layer.
Most GEO tools can tell you that a result occurred. Grounded Signal determines why it occurred, whether it is accurate, which source shaped it, and what change is most likely to improve it.
Built from model evaluation, not SEO templates
Our approach comes from direct experience with frontier-model training and evaluation, information retrieval, and model behavior—not a repackaging of conventional SEO.
Diagnosis before intervention
A missing mention is a symptom. We determine whether the failure is access, retrieval, source selection, entity resolution, synthesis, citation, or weak underlying evidence.
Accuracy before visibility
We test whether your company is represented correctly, whether important qualifications survive synthesis, and whether citations actually support the generated claims.
Canonical improvement, not a shadow website
We improve the authoritative website, documentation, evidence, feeds, and public information your company owns. The value remains with you after the engagement ends.
Independent and cross-platform
We test the AI surfaces that matter to your buyers without tying the analysis to one model provider, search engine, publishing platform, or proprietary visibility score.
Measured before and after
Recommendations are tied to explicit ground truth, repeated trials, matched conditions, and controlled retesting—not one favorable screenshot.
We treat GEO as an experimental systems problem—not merely a content problem.
Before changing anything, we establish what is true, what each system can retrieve, which sources it selects, and where the generated answer diverges from the evidence. Every recommendation is tied to a diagnosed failure and a measurable test—not a guess about what an AI system might prefer.
A versioned record of what is true
Canonical names, claims, conditions, dates, and sources—built into a structured reference before we evaluate a single AI response against it.
Can the systems actually reach and use you?
Indexing, crawl access, page selection, and comprehension are tested directly—separating “could not find it” from “found it and got it wrong.”
What is actually being said, repeatedly
Your real discovery funnel—comparison, due diligence, technical validation, and skeptical questions—tested repeatedly across the AI surfaces most relevant to your buyers.
Scoped, measured, and retested
Changes are documented and evaluated against matched comparison pages and repeated baseline measurements, helping distinguish intervention effects from ordinary model and platform variation.
Ten deliverables, in sequence.
Each step depends on the one before it. We do not prescribe changes before we know what is broken, and we do not call the work finished after one favorable retest.
Ground-truth & entity map
Your canonical facts, claims, conditions, and sources, organized into a single reference against which the rest of the engagement is measured.
Technical retrieval audit
Whether priority systems can reach, read, index, and use your pages—before we ask what they are saying about you.
Cross-platform baseline benchmark
How you are currently described across the AI surfaces relevant to your buyers, using the questions they actually ask.
Citation & source graph
Which first-party, third-party, outdated, and competing sources are shaping the answer—and which authoritative source should support each important claim.
Failure-mode analysis
Where each answer breaks down and why: not found, wrongly matched, misread, outdated, poorly evidenced, misattributed, or displaced by another source.
Prioritized intervention plan
Recommendations ranked by business risk, likely effect, and implementation cost—not by what is easiest for an agency to bill.
Canonical information remediation
We work with your technical, product, content, and communications teams to correct the client-owned sources producing the failure—from crawl access and entity clarity to documentation, evidence, structured data, and version control.
Controlled retest
The original benchmark is repeated under matched conditions after ingestion is verified, with comparison pages and repeated trials used to separate material improvement from ordinary platform variation.
Ongoing monitoring panel
Because model behavior and source selection are not static. A standing panel flags factual regressions, citation changes, and competitive displacement before they become persistent patterns.
Executive report
The findings, risks, evidence, and next actions in plain language for the people who make product, technical, communications, and budget decisions.
Durable GEO without a second website for machines.
Grounded Signal follows a semantic-parity standard: information may be delivered in different accessible formats, but people and AI systems should receive the same material facts, qualifications, and limitations.
Client-owned
We improve the website, documentation, feeds, structured data, and public evidence the client owns. The value does not disappear when a subscription ends.
Semantically consistent
We do not redirect AI crawlers to materially different claims, publish machine-only facts, or maintain a separate synthetic identity for the company.
Evidence-based
We do not use keyword padding, fabricated authority, hidden model instructions, unsupported structured data, or mass-produced prompt pages.
Measurable
We use explicit ground truth, repeated trials, matched conditions, and longitudinal testing—not screenshot-only proof or a single opaque visibility score.
Built on frontier-model and web-systems expertise.
Grounded Signal was founded by Katy Johnson, an applied AI scientist with direct experience in the training and evaluation pipelines of multiple frontier models.
Her background spans machine learning, web development and infrastructure, technical product development, government AI work, engineering in deployed environments, and research across computer science and the physical sciences. That combination provides an unusual view of both sides of the GEO problem: how organizations publish information and how modern AI systems retrieve, transform, evaluate, and sometimes distort it.
This provides a deeper understanding of how information can be:
- Retrieved or overlooked
- Compressed during synthesis
- Detached from important qualifications
- Merged with conflicting sources
- Generalized beyond the evidence
- Misattributed
- Represented inconsistently across models
Grounded Signal uses no confidential model data, proprietary platform information, or insider influence. Our advantage is systems-level understanding, rigorous experimental design, and the ability to diagnose why failures occur.
Built for companies where a wrong AI answer costs more than a lost click—technical products, regulated decisions, and high-consideration purchases researched before a buyer ever talks to a person.
Find out what generative AI is already saying about you.
Every engagement begins with a focused scoping conversation. We identify the AI surfaces, buyer questions, information risks, and business outcomes that matter—and tell you candidly when a full audit is not justified.