What problem does AITWIRE solve?

When AI systems answer questions about a business, those answers may be incomplete, outdated, or inaccurate — and most businesses have no systematic way to measure, calibrate, or prove what changed. AITWIRE solves this by running structured probe cycles across major AI systems, scoring answers against a canon of verified facts the customer controls, publishing those facts to authority surfaces it manages, and re-measuring whether answers improved with statistical rigor. The result is an evidence-based record of AI answer performance rather than guesswork.

What industries does AITWIRE serve?

AITWIRE serves customers across a broad range of industries, including SaaS and e-commerce, digital marketing and consultancy, professional services such as legal, consulting, healthcare, and finance, and enterprise brands. Its vendor-neutral measurement approach makes it applicable to any organization that wants to understand and improve how AI systems answer questions about them, from solopreneurs to large enterprises.

How does AITWIRE address wrong AI answers about my business?

AITWIRE recommends corrections and publishes your confirmed facts to the machine-readable authority surfaces it manages. Measured gaps become prioritized recommendations backed by data briefs, and your confirmed facts are published as machine-readable authority surfaces it manages: entity.json, JSON-LD, llms.txt, FAQPage schema, and structured feeds. AITWIRE does not write your prose — every AI-proposed correction is suggest-and-confirm, so nothing goes live without your approval.

How does AITWIRE prove whether an AI answer improved?

After a change, AITWIRE re-measures the same metrics on the same questions and reports the movement per AI system — before and after. Movement is reported as real only when it clears statistical checks: dimension scores carry sample sizes and 95% confidence intervals, so improvement claims are evidence you can review and retain, not vibes.

How AITWIRE measures AI answers about you | AITWIRE Help

What AITWIRE measures

AITWIRE asks AI systems questions about your business and scores their answers. It checks these leading public AI systems and answer surfaces: ChatGPT, ChatGPT Search, Claude, Gemini, Perplexity, Grok, Mistral and Llama (Meta). How many of them your account covers depends on your plan.

Google AI Overviews is not measured: AITWIRE captured the overview off the live results page until that capture was retired on 25 September 2026.

What the answers are scored on

AITWIRE scores answers on five dimensions:

Accuracy: whether the answer matches the facts you have confirmed in Your Facts.
Citation: whether the answer names your business or your website.
Sentiment: the tone the answer takes toward you. See Tone in AI answers.
Quality: how complete and useful the answer about you is.
Recommendation: whether the answer recommends you, mentions you, leaves you out or steers people away.

Accounts that track competitors also see competitive position: how often answers name you compared with those competitors.

How the questions are asked

You approve the questions. AITWIRE drafts questions from the facts you monitor, and you approve them, one at a time or all at once, under Questions sent to AI services in Prompt Library. You can add your own there too.
Every question is asked cold. No personalization, account history or AITWIRE context is added, and answers are generated at low randomness. Questions are repeated across AI systems and over time.
The AI system, not an app. AITWIRE asks each system directly rather than through its consumer app. An app can add personalization, memory or extra search on top, so what one person sees in an app can differ from what AITWIRE records.

How often

Questions are asked on the schedule you choose in Prompt Library: your plan's own schedule or a slower one. You can also pause it. Each run is a cycle, and Cycle Results in AI Monitor (Answer Accuracy in the sidebar) lets you open the questions and answers from recent cycles.

How to read the scores

Look at the sample size. Scores come with the number of answers behind them (n) and, where one can be computed, a 95% confidence interval. A small n means a wide interval: treat it as an early read.
A narrow interval is not proof. It means the sample is precise, not that the reading is right. Grading is automated and can make mistakes.
Check whether a change is real. The Is this change real? card in AI Monitor compares the last 30 days with the 30 before. A movement smaller than the combined 95% confidence intervals is shown as within noise. Because several dimensions are tested at once, a larger movement is marked confirmed only if it also survives a standard correction for multiple comparisons, and exploratory if it does not.
Read trends, not single answers. AI systems change as their models and search results change, and one answer is one sample.

Learn more

Methodology: how AITWIRE measures, in more depth.
Tone in AI answers: what the sentiment score means and how to check it.
Downloading Your Probe and Answer Data (CSV): export the answers behind your scores.