Generative engine optimization (GEO) tools answer one deceptively simple question: when a customer asks ChatGPT, Gemini, Perplexity or Copilot about your category, does your brand appear — and is what they say about you accurate?
The market now contains several distinct tool categories that are often lumped together as "GEO tools". They overlap, but they are not interchangeable. This page explains the categories, the criteria that actually matter, and how the better-known products differ in positioning.
Vendor features and pricing change frequently. Everything here is a qualitative description of positioning, not a price comparison. Verify current details with each vendor before subscribing.
Why the category split matters
A GEO program has four stages, and no single product covers all of them equally well:
- Readiness — can AI crawlers reach your content, and is it machine-readable?
- Diagnosis — what is structurally wrong with the site, and what should you fix first?
- Measurement — do you appear in AI answers, how often, and with what share of voice?
- Attribution — which AI platforms actually send traffic, sessions and revenue?
Buying stage 4 when stage 1 is broken produces a dashboard full of zeros. Buying stage 2 when stage 3 is already healthy produces fix lists nobody needed. Match the tool to the stage where your business is actually blocked.
Category 1: Diagnosis-first tools
These products start from a URL, crawl it, and return a prioritized list of issues: crawlability, metadata, heading structure, thin content, broken internal links, AI crawler access, and whether an llms.txt exists.
Who it suits: small teams and single-site businesses that need to know what to fix, not just what happened.
What to demand:
- Checks that map to real failure modes, not vanity scores.
- Explicit AI crawler checks:
GPTBot,OAI-SearchBot,ClaudeBot,PerplexityBot,Google-Extended,CCBot. - A readable, ordered action list — not 400 undifferentiated warnings.
- A rendered view of what an AI model actually sees on the page.
Bidvix sits here: free diagnosis, technical audit, AI visibility sampling, competitor share of voice, and claim-accuracy checks, with a credit-based public API for programmatic use.
Category 2: Monitoring and visibility platforms
These are the big-dashboard products. They run large prompt panels across multiple engines on a schedule, track brand mentions over time, and produce share-of-voice and sentiment reporting for marketing teams and agencies.
Who it suits: established brands with a dedicated SEO or content team, where the question is "how are we trending" rather than "what is broken".
What to demand:
- Prompt coverage that reflects your real buying questions, not generic keywords.
- Per-answer evidence — every claimed mention should be inspectable, not just a count.
- Engine coverage that includes both classic search-grounded answers and pure model answers.
- Clear methodology for mention rate, citation rate, and share of voice.
AthenaHQ and Profound are the best-known names in this tier. Both are positioned around brand monitoring, competitive intelligence, and action recommendations for marketing organizations. AthenaHQ emphasizes AI visibility intelligence and brand monitoring across engines; Profound emphasizes large-scale answer data, competitive benchmarking and enterprise workflows. Their strengths are breadth and reporting depth — not on-page technical repair.
Category 3: Attribution-first tools
These products instrument analytics. They tag or segment AI referral sources in GA4 and other platforms, then report sessions, conversions and revenue attributable to AI channels.
Who it suits: teams that already generate AI traffic and need to prove commercial impact.
What to demand:
- A maintained, documented list of AI referrer domains (they change).
- GA4 or warehouse integration you control, not a black-box pixel.
- Revenue and conversion, not just sessions.
Otterly is positioned around AI search visibility monitoring with a strong emphasis on answer-level tracking and a free entry point; Scrunch leans toward connecting AI visibility to traffic and revenue and preparing content for agent consumption. Both are reasonable choices when attribution is the headline need.
Category 4: Content and agent-readiness tooling
A newer cluster focuses on making your site legible to machines: generating llms.txt, structuring product data, and formatting content so agents can quote it correctly.
Who it suits: sites already visible in AI answers that want cleaner citations and fewer factual errors.
What to demand: output that matches the published llms.txt convention, no vendor lock-in on the generated file, and a diff you can review before publishing.
The evaluation criteria that matter
Ignore feature-count tables. Ask these instead.
1. Is the evidence inspectable?
If a tool says you were mentioned in 34% of answers, you must be able to open the answers. Numbers without raw responses cannot be defended in a board meeting and cannot be debugged.
2. Does it test the questions customers actually ask?
A prompt panel built from head terms measures keyword presence. A good GEO tool generates the long, conversational questions a buyer would type: "which tool is best for a small exporter shipping to the EU". Ask to see the prompt list before you buy.
3. Does it separate mention from citation?
Being named in an answer and being the source the answer links to are different outcomes with different mechanics. A tool that reports one blended "visibility score" hides the distinction that determines whether you get traffic.
4. Does it check claim accuracy?
AI models confidently state wrong facts — wrong pricing, wrong headquarters, wrong product scope. Monitoring that only counts mentions will never flag "ChatGPT says we charge $99". Accuracy verification is a distinct capability; confirm whether the vendor has it.
5. Can you verify the technical layer?
If the tool cannot tell you whether GPTBot is blocked by your CDN, or whether your llms.txt is reachable, its visibility metrics describe symptoms without the cause.
6. What is the unit of cost?
Per-seat pricing suits one team. Per-diagnosis or credit-based pricing suits agencies and programmatic pipelines. Understand what one extra site or one extra thousand prompts costs before signing an annual contract.
How much GEO tooling you actually need
Stage 0 — nothing indexed, no AI presence. You need diagnosis and fixes. A monitoring subscription at this point will show a flat line for months.
Stage 1 — appearing occasionally. You need measurement with inspectable evidence, plus the fix backlog to increase frequency.
Stage 2 — consistent presence. You need competitor share of voice and claim accuracy, because the risk shifts from invisibility to misstatement.
Stage 3 — attributed revenue. Add attribution tooling and keep a diagnostic check running to catch regressions after site changes.
Most SMB sites are at Stage 0 or Stage 1 and buy Stage 3 tooling. That is the most common wasted GEO budget.
A 30-minute evaluation script
Run this against any shortlisted tool:
- Enter a domain you know is technically weak. Does it surface the real blockers?
- Ask for the full prompt list. Are these questions a customer would ask?
- Open one raw AI answer. Is it the actual response text?
- Look for an
llms.txtcheck and an AI crawler check. Are both present? - Check whether competitor mentions are measured on the same prompts, at the same time.
- Ask what happens when an engine changes its UI or capping. Who absorbs that breakage?
A tool that fails 1–3 is a reporting layer, not a GEO tool.
Where Bidvix fits
Bidvix is deliberately diagnosis-first. It runs a technical audit, samples AI answers across configured engines, measures competitor share of voice, and verifies claims AI makes about your brand against your published facts. Every sampled answer is inspectable, and the public API is credit-based so agencies can run checks without per-seat contracts.
It is not the right tool if you need enterprise-grade daily monitoring across thousands of prompts, or if your only question is revenue attribution. In those cases, pair a monitoring or attribution platform with a diagnostic check so fixes have a source.