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When AI systems answer questions about a business, those answers may be incomplete, outdated, or inaccurate — and most businesses have no systematic way to measure, calibrate, or prove what changed.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.

Which AI Engines AITWIRE Monitors | AITWIRE Help

The engines behind AI answers

AITWIRE polls these leading public AI systems and answer surfaces — the assistants and answer engines your customers actually use: ChatGPT, ChatGPT Search, Claude, Gemini, Perplexity, Grok, Mistral and Llama (Meta). Google AI Overviews is not measured: AITWIRE captured the overview off the live results page until that capture was retired on 25 September 2026.

Training-prior answers versus live-search answers

Not all AI answers are produced the same way, and AITWIRE distinguishes them:

Training-prior engines answer from what the model learned during training. They change only when the model is retrained, so correcting them is a longer game. As of early 2026 the standard Grok chat mode is one of these: xAI retired its live-search auto-grounding, so bare Grok now answers from its training prior.
Live-search engines retrieve the web at answer time and can cite their sources, so they reflect your freshly published facts within days of a re-crawl. Perplexity and ChatGPT Search work this way — and AITWIRE now also polls a live-search variant of Grok (grok_search) that uses a live web-search tool and returns citations, tracked as a separate, opt-in line on top of the core set so it never muddies the training-prior Grok trend.

Knowing which kind of engine gave an answer tells you what to expect from a fix: live-search engines move within days of a re-crawl; training-prior engines move on the model retraining schedule.

How probes are allocated

AITWIRE sends only the questions you have approved, on the schedule you choose (up to the cadence your plan includes). Within them, by design, it concentrates your probe budget where it carries the most signal rather than spreading it thin:

A fixed sentinel core of your highest-priority questions — your core brand-identity and pricing facts — is measured every cycle, so your headline trend line never loses continuity.
Event-driven probes keep full density: anything steered by an active campaign, and any question inside a verification window after you publish a fix.
The remaining questions rotate on a deterministic schedule, so the whole set is covered over successive cycles without over-sampling any one area.

This is a measurement-design choice, not cost-cutting: steady-state accuracy drifts only slowly, so anchoring the questions that matter and rotating the rest measures what counts without adding noise.