Use prompts-gpt.com to monitor, explain, and improve AI visibility.
These public docs cover the core application workflow: set a baseline, organize projects, monitor buyer prompts, inspect source evidence, and convert answer gaps into content and citation actions.
2026 research-backed operating assumptions
Measure repeated answer evidence, not one-off score confidence.
Current AI search research supports the product workflow: AI Overviews change click behavior, answer engines cite sources differently from classic rankings, and coding-agent orchestration is becoming a terminal-native operating layer.
AI Overview click pressure
38% fewer organic clicks
Randomized 2026 field experiment on triggered Google AI Overview queries.
AIO activation variance
13.7%-64.7%
AIO activation varied materially overall versus question-form queries in a 55,393-query study.
Citation absorption dataset
21,143 citations
GEO research found citation breadth and answer influence diverge across ChatGPT, Google AI Overview/Gemini, and Perplexity.
AI referral conversion range
18% conversion benchmark
2026 public conversion studies increasingly report that AI-referred sessions convert materially better than generic organic traffic, but the safe product claim is still to validate with first-party attribution.
Terminal agent category
CCBench + TerminalBench
By May 2026, coding agents are benchmarked as their own category with terminal-agent leaderboards and real repo tasks, reinforcing that orchestration is now a distinct workflow layer rather than a novelty.
Source-quality evaluation is becoming formal
8 source metrics
SourceBench evaluates cited sources with content relevance, factual accuracy, objectivity, freshness, authority/accountability, clarity, and related page-level signals.
AI referral ROI needs first-party tracking
2.2x+ reported lift
A Q1 2026 Shopify analysis reported AI-referred buyers converting about 2.2x better than Google organic, reinforcing the need to measure AI referrals separately instead of using site-average conversion assumptions.
Agent orchestrators are separating from agents
9 OSS orchestrators
2026 coding-agent comparisons now separate terminal agents from orchestration layers that coordinate parallel agents, task graphs, and verification pipelines.
Dedicated orchestration is real
Parallel + pipeline workflows
Open-source and commercial orchestration tools now market explicit parallel and pipeline execution models, so the differentiation is no longer orchestration by itself but tying that orchestration to measurable product workflows and evidence.
Agentic GEO is emerging
Self-evolving GEO
AgenticGEO research frames GEO as an adaptive system that evolves strategies, supporting a product loop from monitor evidence to evaluated implementation.
AI search needs engine comparison
11,500 queries
A 2026 empirical benchmark compares Google Search, AI Overviews, and Gemini, reinforcing side-by-side answer evidence instead of single-engine conclusions.
AI-crawler readiness can correlate with demand
850K websites
Duda's 2026 study summary links AI-crawler optimization with materially higher human traffic and form submissions, supporting crawler/source-readiness workflows.
AI-first discovery behavior
37% of consumers
2026 reporting continues to show a meaningful share of consumers starting product research with AI assistants instead of classic search alone, which raises the stakes on answer visibility and source trust.
B2B AI referral conversion benchmark
14.2% vs 2.8% organic
A 2026 cross-platform benchmark reported materially higher conversion from AI-referred B2B sessions than organic search baselines; treat as directional and validate in-product.
GEO content cluster lift
3x citation frequency
Practitioner GEO guides cite higher AI citation frequency for topical cluster coverage versus isolated single-topic pages.
AI Overview prevalence in search
High share of queries
2026 AEO guides report Google AI Overviews appearing in a large share of Google searches, reinforcing engine-specific monitoring instead of one blended score.
Topical cluster citation lift
3x frequency
GEO content strategy guides cite materially higher AI citation frequency for comprehensive topic clusters versus isolated articles.
AI search market size 2026
$28.8B (+78% YoY)
The total AI search market is projected at $28.8B in 2026, up from $16.2B in 2025. AI search advertising ($8.7B, +107%) and API access ($3.9B, +86%) are the fastest-growing segments.
B2B CMO AI adoption for vendor discovery
84% of B2B SaaS CMOs
84% of B2B SaaS CMOs use AI/LLMs (ChatGPT, Claude, Perplexity) for vendor discovery in 2026, up from 24% in 2025. 68% start category discovery in AI tools before traditional search.
Zero-click AI sessions
93% end without website visit
93% of AI search sessions now end without a single visit to a website, making answer presence and citation the primary visibility metric over click-through.
Global AI search monthly active users
910M monthly, 390M weekly
As of 2026, approximately 910 million people use AI search tools monthly with 390 million weekly — nearly 3x the 320M monthly figure from 2024.
Research basis
Competitive and market claims here are tied to reviewed public sources.
The current sweep prefers official competitor documentation, then current academic or practitioner research for measurement variance, engine differences, and workflow changes. This keeps conversion copy closer to what the market can actually support.
OtterlyAI pricing help
Official plan structure, prompt allowances, tracked surfaces, daily tracking, and add-on engine coverage.
Semrush AI Visibility features
Official overview of Visibility Overview, Brand Performance, Competitor Research, prompt tracking, and toolkit pricing.
Peec AI docs
Official documentation for visibility, position, sentiment, prompt setup, source workflows, and MCP access.
AI visibility uncertainty research
Research support for repeated measurement, confidence framing, and avoiding one-shot precision claims.
Google Search vs AI Overviews vs Gemini study
Research support for engine-by-engine comparison rather than one blended answer score.
Reviewed pricing benchmarks
Buyer expectations now come bundled with packaging expectations.
Teams are comparing prompt limits, engine breadth, exports, and action depth together. Use these public pricing signals as market context, not as a substitute for current vendor quotes.
Scrunch Core
$250/mo (125 prompts, 4 LLMs, 5 seats)Engine coverage: ChatGPT, Perplexity, Google AI Overviews, Copilot
4 platforms on Core; full 9-platform coverage Enterprise-only; hallucination detection Enterprise-only; optimization-first but no CLI execution
Scrunch Enterprise
Custom ($1,000+/mo est.)Engine coverage: ChatGPT, Claude, Perplexity, Gemini, Meta AI, Google AI Mode, Google AIO, Copilot, Grok (9 total)
SOC 2 Type II, hallucination detection, AXP; requires sales engagement; no self-serve orchestration
Promptwatch Essential
$99/mo ($990/yr annual)Engine coverage: All major LLMs on public plan materials
50 prompts, 1 website, 5 generated articles/mo; platform-first workflow
Promptwatch Professional
$249/mo ($2,490/yr annual)Engine coverage: All major LLMs + analytics suite
150 prompts, 2 websites, API access; still no local CLI orchestration
Otterly Lite
$25/mo (annual billing; $29/mo monthly)Engine coverage: 4 core
15 prompts; Gemini/AI Mode are paid add-ons
Otterly Standard
$160/mo (annual billing; $189/mo monthly)Engine coverage: 4 core + add-ons
100 prompts; monitoring/audit/reporting focus
Otterly Premium
$422/mo (annual billing; $489/mo monthly)Engine coverage: 4 core + add-ons
400 prompts; monitoring/audit/reporting focus; prompt-based pricing scales fast
Peec AI Starter
Public pricing starts at 50 prompts with 3 models includedEngine coverage: ChatGPT, AI Mode, AI Overviews, Copilot, Perplexity, Gemini, Grok
Monitoring-first packaging; model allowances still shape cost and workflow fit
Why buyers churn
Common reasons teams abandon their first AI visibility tool.
Monitoring without implementation ownership
Users repeatedly describe AI visibility tools as interesting dashboards that still leave the team guessing what to ship next.
Prompts-GPT response: Keep every report tied to a brief, source fix, comparison-page action, or orchestration workflow instead of ending with a score.
Weak measurement trust
Practitioners question whether tools are measuring durable model understanding or just prompt behavior from a narrow sample.
Prompts-GPT response: Show confidence rules, repeated scan guidance, exact answer evidence, and engine-specific context before claiming a trend.
Opaque packaging as prompt volume grows
Pricing often looks simple at the entry tier but becomes harder to forecast once teams need broader prompt coverage, more engines, or more users.
Prompts-GPT response: Keep public packaging attached to monitor counts, prompt coverage, exports, and workflow outcomes rather than hiding real usage tradeoffs.
One-shot wins that do not hold up later
Teams see a promising single answer, then struggle to prove whether visibility actually improved across prompts, engines, or repeated windows.
Prompts-GPT response: Make saved queries, recurring monitors, and trend views the default path from a free result to a paid workflow.
Low-effort roadmap
Operational improvements worth shipping first.
Recurring-query save and compare flows from public/free surfaces
Effort SValue 5/5Urgency 5/5Competitors increasingly sell continuity, not one-off checks. Saved research states and prompt-to-monitor handoff reduce drop-off after the first useful result.
Execution history visible in the agent workspace
Effort SValue 4/5Urgency 4/5The CLI/orchestration wedge is more credible when users can see recent runs, scores, and provider mix without hunting through local artifacts.
Clear pricing benchmark context on comparison pages
Effort SValue 4/5Urgency 4/5Buyers compare entry friction first. Public comparison surfaces should help them understand prompt caps, engine add-ons, and workflow tradeoffs quickly.
Research-backed churn reasons in docs and positioning
Effort SValue 4/5Urgency 4/5Showing what disappoints buyers in competing tools sharpens the product story and keeps marketing copy honest.
High-intent editorial pages that connect free checks to paid monitoring
Effort MValue 5/5Urgency 4/5The conversion path is stronger when articles explain what to do after a free diagnostic and why recurring proof beats one-shot screenshots.
The category is expanding from more than one direction.
Visibility buyers now see AI-search specialists, PR suites, reputation products, and managed-distribution tools in the same shortlist. Clear workflow language matters more than ever.
GetMentions AI
mediumApril 2026
AI visibility + managed execution across 192K+ partner websites
Why it changes evaluation
Paid placement execution, not organic optimization — different value proposition
Meltwater GenAI Lens
low2025 (mid-year 2026 update)
Enterprise AI brand monitoring with AI-powered recommendations and LLM reporting
Why it changes evaluation
PR/comms-centric; 48-hour refresh; enterprise-only pricing
AdLift Tesseract
lowMarch 2026
AI sentiment analysis across Google AI Overviews, ChatGPT, Gemini, DeepSeek, Perplexity
Why it changes evaluation
Reputation management focus; less actionable than full-loop workflow
Trakkr
mediumMarch 2026 update
Competitor radar charts, citation filtering, multi-region reports, Google Agent detection
Why it changes evaluation
Google Agent detection is novel; API expansion underway
Yext Scout
medium2026
AI search and competitive intelligence with local benchmarking across Google, ChatGPT, Gemini, Perplexity, Claude
Why it changes evaluation
Enterprise distribution through existing Yext customer base; local visibility focus
BrandBeacon
low2026
Real-time brand presence monitoring across ChatGPT, Perplexity, Claude, Gemini
Why it changes evaluation
Lightweight real-time monitoring; limited action workflows
The final sweep focuses on the gaps buyers complain about after trying monitoring tools.
These are the low-effort, high-value improvements applied across the free tools, docs, content, and orchestration surfaces. Each item is tied to a public evidence source rather than an invented competitor claim.
Prompt-depth builder
Generate a full buyer-intent prompt set with monitor-ready groups instead of stopping at a short ad hoc query list.
Source-quality action plan
Expose source freshness, authority, extraction clarity, and action owner guidance directly in the free checker output.
Free-tool benchmark framing
Explain how one-off checks become paid monitor workflows with repeated prompts, engine coverage, source confidence, and exportable proof instead of competing only on a single visibility score.
Eval-mode implementation guardrails
Make cost, trace, criteria, pass/fail scoring, and diff review visible before users run multi-agent orchestration so eval mode behaves like a product workflow, not a vague agent race.
Executive-ready source proof
Package exact answers, citations, confidence, next decisions, and orchestration handoff as the report story.
Crawler-readiness proof
Surface robots.txt, llms.txt, canonical URL, source freshness, and AI-readable content checks as part of the visibility workflow rather than treating crawler access as a separate SEO chore.
Measurement confidence
Know when a result is a preview, a monitor signal, or an executive claim.
A free checker result is useful, but 2026 AI visibility research and practitioner reviews keep pointing to the same risk: one answer can look precise while the market signal is still under-sampled. Use the thresholds below before exporting a claim.
Prompt sample
25+ buyer promptsCover category, problem, comparison, alternative, pricing, implementation, and local-intent questions before reporting a trend.
Engine breadth
5+ answer surfacesCompare ChatGPT, Gemini, Perplexity, Claude/Grok, and Google AI surfaces because answer behavior and citations diverge by engine.
Repeat evidence
2+ scan windowsRepeat the same prompt set before acting on score movement; single-run visibility can look falsely precise.
Source proof
Owned + third-partyKeep answer excerpts, citations, source type, freshness, and action owner visible before exporting stakeholder claims.
Surfaces to separate
Google AI Mode
Track Google AI prompts separately from ChatGPT and Gemini when the buyer journey depends on search-like answers.
Grok and DeepSeek
Use them as priority surfaces when the audience is technical, US consumer, or developer-heavy.
ChatGPT Shopping and AI commerce
Separate product recommendation visibility from brand-monitoring prompts when the buyer can convert directly from an AI answer.
Sources to classify
Reddit and forums
Track whether community citations support your brand, a competitor, or a stale narrative before writing another owned page.
Review platforms
Keep profiles current, strengthen review recency, and map competitor review citations into outreach or customer-proof tasks.
Publisher and creator coverage
Prioritize earned-source gaps when competitors are cited from third-party proof and your owned pages are ignored.
Start with a visibility baseline
Run a public AI visibility check for your domain, category, and competitors before creating a workspace. Use the baseline to choose the prompts worth monitoring.
Create projects and prompt monitors
Group work by brand, domain, market, competitor set, and buyer intent. Add recommendation, alternatives, category, local, and problem-aware prompts.
Review answer evidence
Compare mention rate, answer position, sentiment, competitor pressure, citations, model coverage, source quality, and crawler signals from each scan.
Turn gaps into actions
Prioritize content briefs, source fixes, llms.txt updates, comparison pages, FAQ changes, and media outreach from the evidence behind each answer.
Implemented gap scorecard
Top five research-backed gaps prioritized by value, urgency, and implementation effort.
Prompt-depth builder
ChatGPT Query Generator
Source-quality action plan
GEO Content Score Checker
Free-tool benchmark framing
Free tools
Eval-mode implementation guardrails
Codex Script Generator and Agent Workspace
Executive-ready source proof
Docs, reports, and articles
The docs map to the workflows teams run every week.
Use these guides to keep AI visibility work repeatable: one domain context, one monitored prompt system, one evidence trail, and one action backlog.
Product sweep playbook
Use the evidence-first operating loop to decide when a public prompt becomes a monitor, alert, report, export, or agent orchestration job.
Workspace setup
Configure a brand domain, competitors, market context, and reporting preferences so scans stay consistent across teams.
Prompt monitor design
Build reusable prompt sets around the buying questions people ask ChatGPT, Gemini, Perplexity, Claude, Grok, and other AI assistants.
Source and citation review
Classify owned pages, competitor pages, reviews, publishers, videos, communities, and directories that appear in AI answers.
Reports and exports
Use recurring reports, CSV citation exports, PDF brand reports, and GEO audit exports to share answer evidence, source gaps, and next actions with brand, SEO, and agency stakeholders.
Agent orchestration
Design multi-phase agent pipelines with sequential, parallel, or DAG-based execution. Each phase gets its own tool, model, prompt, timeout, and retry policy. Export pipeline definitions as JSON, YAML, Bash, PowerShell, Docker, or GitHub Actions workflows.
Client package (prompts-gpt)
Use the published prompts-gpt npm package for CLI sweeps, multi-provider orchestration, parallel coordination, SDK integration, and agent file syncing. Full docs at /docs/prompts-gpt-package.
API and integrations
Use the REST API and project tokens for programmatic access to visibility data, citations, prompt results, and export endpoints.
Transparent self-audit
Review the public prompts-gpt.com self-audit before repeating the same workflow for your own domain or client account.
Free tools
Use the visibility checker, GEO content score checker, query generator, market search, and llms.txt generator as public entry points before moving into a saved workspace.
Trust and account controls
Review privacy, terms, security, and cookies pages before using customer domains, private prompts, or reporting workflows.
Getting started guide
Fast path for baseline checks, project setup, confidence guardrails, and first actions.
Open guideDocs hub
Public capability map covering workflow, research, trust, proof exports, CLI, and FAQs.
Open guideProduct sweep playbook
Evidence-first operating loop for free tools, monitors, alerts, reports, and orchestration.
Open guideCLI and package docs
Package install, orchestration modes, SDK usage, and local workflow guidance.
Open guideReports and source actions
How to turn answer evidence into stakeholder-safe reports, source fixes, and action owners.
Open guidePublic tools and safe inputs
Guardrails for no-signup tools, safe data handling, and conversion entry points.
Open guideRead trust and legal docs before sending sensitive context.
Do not submit confidential customer records, private strategy, unreleased product information, or private URLs into public preview tools.
Use stable proof artifacts for sharing, and the checker for live diagnostics.
Public AI visibility checks are useful, but they are not the same as stable proof. These routes separate repeatable documentation and exports from the mutable rerun URL.
Product sweep playbook
Evidence-first workflow for prompt discovery, monitors, sources, alerts, reports, exports, and CLI orchestration verification.
Self-audit article
Stable methodology page that explains what prompts-gpt.com can prove publicly today and where the limits are.
Self-audit markdown export
Stable public proof artifact for sharing with buyers, evaluators, and AI systems that prefer markdown.
Self-audit JSON export
Machine-readable proof payload with discovery links, credibility notes, and project handoff guidance.
Checker rerun URL
Live diagnostic surface for fresh answers. Useful for inspection, but not a stable proof artifact.
Source quality scorecard
Research-backed guide for judging citation freshness, authority, extraction clarity, and action ownership before sharing reports.
Executive reporting guide
Decision-first reporting workflow that connects answer evidence, competitor pressure, source confidence, and implementation orchestration.
May 2026 market intelligence
Research-backed sweep of Otterly, Peec, Semrush, GEO/AEO studies, conversion benchmarks, and CLI orchestration positioning.
GEO content calendar
Prompt-first calendar workflow from research through monitored citation proof.
AEO citation playbook
Citation mechanics, source repair, conversion context, and orchestration handoff for May 2026.
GEO optimization strategy 2026
Research-backed guide for prompt research, source scoring, repeated monitors, and implementation workflow design.
Answer engine optimization guide 2026
Question architecture, answer-first page structure, citation strategy, and recurring AEO measurement.
AI visibility market shift playbook 2026
Reviewed competitive shift summary for monitoring, optimization, implementation, and packaging expectations.
GEO content strategy and AEO distribution
Answer-first content, source coverage, and distribution guidance for citation-ready pages.
Public machine-readable routes should stay aligned with the product truth.
These files support SEO crawlers, AI systems, and internal QA. Review them whenever homepage positioning, docs, pricing, self-audit exports, or public route inventory changes.
Brand facts
Human-readable canonical product facts page for evaluators and AI systems.
brand-facts.json
Machine-readable product facts endpoint under /.well-known/.
llms.txt
Machine-readable source map for AI systems that need canonical product, docs, and proof URLs.
robots.txt
Crawler access rules for public product pages and private application routes.
sitemap.xml
Public canonical URL inventory for marketing, article, metric, and proof pages.
Connect AI visibility data to your existing stack.
Use the API for programmatic data access, project tokens, citation exports, and JSON payloads for BI dashboards.
REST API
Programmatic access to visibility data, citations, prompt results, and export endpoints for approved integrations.
Project tokens
Create project-scoped API tokens for scripted pulls, exports, and internal workflow tooling without sharing user sessions.
BI exports
Export visibility metrics, citation data, and trend history as CSV or JSON for BI dashboards and team reporting.
Build multi-phase agent pipelines with sequential, parallel, or DAG execution.
Agent orchestration lets you chain AI tool invocations into repeatable pipelines. Define phases, dependencies, checkpoints, and rollback strategies. Export the resulting pipeline definition as JSON, YAML, Bash, PowerShell, Docker, or GitHub Actions.
Pipeline Designer
Visual pipeline builder for creating and configuring multi-phase agent workflows with tool, model, and timeout selection per phase.
Multi-Format Export
Export pipelines as JSON, YAML, Bash, PowerShell, Dockerfiles, or GitHub Actions workflows for CI/CD integration.
DAG Execution
Support for dependency-based execution ordering. Phases run in parallel when possible, respecting the dependency graph.
Portable exports
Export pipeline definitions and scripts for review, handoff, CI, and controlled local execution.
Programmatic access to prompt packs and local orchestration.
The published prompts-gpt package provides typed methods for pulling prompt packs, generating prompt files, syncing agent-readable instructions, and running local orchestration with a project token.
Quick Start
import { PromptsGptClient, syncPrompts } from "prompts-gpt";
const client = new PromptsGptClient({
token: "pgpt_...",
apiUrl: "https://prompts-gpt.com",
fetch,
});
// Pull prompt packs for the linked project
const prompts = await client.pullPrompts({ category: "Claude Code" });
// Write AGENTS.md / Cursor / Copilot files
await syncPrompts(prompts, { agent: "all" });Local orchestration guardrails
npx prompts-gpt setup
npx prompts-gpt run --prompt-file .prompts-gpt/review.md --agent codex
# Run artifacts are written locally
# .scripts/runs/<run-id>/
# summary.md
# agent.log
# worktree-before.txt
# worktree-after.txtTreat prompt files, logs, and summaries as potentially sensitive. Review provider privacy settings and use only accounts and plans authorized for automation.
Client Methods
client.getProject()— Inspect the linked project contextclient.pullPrompts(query)— Pull prompt packs for the projectclient.generatePrompt(input)— Generate one prompt pack from a goal
Operational Notes
- Use only provider accounts, plans, and API routes authorized for automation.
- Review output before using it for legal, compliance, employment, or publishing decisions.
- Keep sensitive code, secrets, and regulated data out of public previews and shared exports.
Three execution modes for every workflow complexity.
Choose parallel for independent tasks, pipeline for sequential dependencies, or eval for quality-gated workflows with automatic rollback.
Parallel mode
Run multiple agents simultaneously on independent tasks. Each agent gets its own git worktree. Ideal for lint + test + review workflows. Command: npx prompts-gpt orchestrate --mode parallel.
Pipeline mode
Chain phases sequentially where output of phase N feeds into phase N+1. Use for research → implement → test → document workflows. Command: npx prompts-gpt orchestrate --mode pipeline.
Eval mode
Pipeline mode with self-evaluation scoring. Use a quality threshold and explicit criteria before a result becomes implementation context. Command: npx prompts-gpt orchestrate --mode eval --threshold 0.85.
Key commands for local orchestration and pipeline management.
Run these commands with npx prompts-gpt or after global install. Cross-platform support for macOS, Linux, and Windows.
npx prompts-gpt setupInitialize project configuration with token authentication and agent detection.
npx prompts-gpt orchestrate --mode parallelRace multiple agents or prompt variants on independent work and compare scored outputs.
npx prompts-gpt orchestrate --mode pipelineChain phases such as research, implementation, review, and report with context passing.
npx prompts-gpt orchestrate --mode eval --threshold 0.85Run pipeline with self-evaluation scoring; outputs below the quality threshold are rolled back and retried.
npx prompts-gpt orchestrate --mode eval --dry-runValidate the eval workflow and quality criteria before executing changes.
npx prompts-gpt diff <run-id>Inspect before/after worktree deltas from a previous orchestration run.
npx prompts-gpt run --watchExecute a prompt file with file-watching for automatic re-runs on changes.
npx prompts-gpt run --prompt-file .prompts-gpt/review.md --agent codexExecute a specific prompt file with a named agent runtime.
npx prompts-gpt sweep --evalRun iterative sweeps with self-evaluation after each iteration.
npx prompts-gpt sweep --parallelRun parallel agent sweeps across multiple targets or prompt files simultaneously.
npx prompts-gpt doctor --fixDiagnose pipeline configuration issues and auto-repair common problems.
npx prompts-gpt doctorCheck workspace health without making changes — validates config, tokens, and agent availability.
Before running agent teams, make the evaluation contract explicit.
Multi-agent orchestration is useful only when the run is reviewable. Keep criteria, usage, traces, and diffs attached to every visibility-to-implementation workflow.
Criteria
Score every run against correctness, citation readiness, actionability, risk, and implementation completeness.
Cost and tokens
Review token usage, provider count, retries, and run duration before expanding from one eval to parallel agents.
Trace
Keep the prompt pack, command, run id, selected output, rejected alternatives, and evaluator notes together.
Diff review
Run `npx prompts-gpt diff <run-id>` after execution so shipped changes stay tied to the original visibility finding.
Go from zero to a monitored AI visibility program in four steps.
prompts-gpt.com is an AI search visibility platform that monitors brand mentions, citations, sentiment, and competitor recommendations across ChatGPT, Claude, Gemini, Perplexity, and Grok. Start with a free check, then scale into recurring monitoring.
Run a free baseline check
Enter your domain in the AI Brand Visibility Checker to see how AI engines currently describe your brand, which sources they cite, and where the gaps are. No signup required.
Create a project
Sign up and set up a brand project with your domain, competitors, industry category, and target market. The platform suggests initial prompts based on your category.
Build prompt monitors
Add 15–25 buyer-intent prompts across category, comparison, alternative, and evaluation question types. Schedule recurring scans across ChatGPT, Claude, Gemini, Perplexity, and Grok.
Review and act
Analyze answer evidence, identify citation gaps, create content briefs for missing mentions, export stakeholder-ready proof, and track visibility improvements over time with recurring reports.
Common AI visibility problems and how to fix them.
Most visibility issues trace back to content gaps, source quality, or crawler access. Here are the most common situations and their fixes.
My brand appears but with outdated information
Update your homepage, features page, and documentation with current product descriptions, pricing, and capabilities. Add lastModified dates to key pages. Republish llms.txt with current canonical URLs.
Competitors are cited but my brand is not
Check which sources AI engines cite for competitors — often review platforms, comparison pages, or documentation. Create or improve equivalent content on the same source types.
My visibility score dropped suddenly
Check if a competitor published new comparison content or if an AI model was recently updated. Review crawler logs for access issues. Verify that key pages are still accessible and structured data is valid.
AI answers describe my brand inaccurately
Rewrite the opening paragraph of your homepage and product pages with a clear, 40–60 word answer-ready block that directly states what your product does. Update FAQ schema with corrected information.
A free check looked good but the paid trend still feels weak
Treat one free check as directional only. Expand to 25+ prompts, compare at least 5 answer surfaces, repeat the same scan window, and inspect whether owned citation share moved with the score before reporting progress.
The team knows the issue but not what to do next
Package the finding into a concrete action owner: content brief, comparison-page update, docs refresh, schema fix, source outreach, or an eval-gated CLI orchestration run. Monitoring without an execution owner usually stalls.
Frequently asked questions about using prompts-gpt.com.
How do I get started with prompts-gpt.com?
Start by running a free AI visibility check at prompts-gpt.com/free-tools/ai-brand-visibility-checker with your domain. Review the baseline, then create a free account to set up a project with your brand, competitors, and target prompts.
What is a prompt monitor?
A prompt monitor is a saved buyer question that the platform tracks across AI answer engines on a recurring schedule. Each scan captures the answer text, brand mentions, citations, sentiment, competitor context, and source evidence.
How do I create my first project?
After signing up, click New Project from the dashboard. Enter your brand name, domain, industry category, primary competitors, and target language. The platform will suggest initial prompts based on your category.
Can I use prompts-gpt.com without an account?
Yes. 6 free tools are available without signup: the AI Brand Visibility Checker, Market Search, ChatGPT Query Generator, llms.txt Generator, GEO Content Score Checker, and Codex Script Generator.
How often should I run visibility scans?
Run daily scans for 10–15 high-priority buyer prompts and weekly scans for the expanded prompt set. Monthly trend analysis helps track visibility score, mention rate, and citation share over time.
What export formats are available?
The platform supports PDF brand reports, CSV citation exports, GEO audit PDFs, Markdown evidence exports, and JSON payloads. Reports can be scheduled for recurring delivery.
How does the llms.txt file help AI visibility?
An llms.txt file provides AI crawlers with a machine-readable map of your canonical pages, documentation, and proof surfaces. It helps AI engines discover and cite your most authoritative content.
What is the brand facts page for?
The Brand Facts page and /.well-known/brand-facts.json endpoint provide a concise canonical summary of the product category, audience, capabilities, differentiators, and trusted public routes. Use them when AI systems or evaluators need a short stable product truth.
What should I do if my brand is missing from AI answers?
Use the prompt gap analysis to identify where competitors appear but you don't. Create comparison pages, update FAQ schema, improve documentation with current facts, and strengthen third-party review presence on platforms AI engines cite frequently.
What is an agent pipeline?
An agent pipeline is a multi-phase workflow where each phase uses a specific AI tool and model. Phases can run sequentially, in parallel, or follow a dependency graph (DAG). Pipelines include checkpoints, rollback strategies, and retry policies for production reliability.
How do I export a pipeline?
Open the public builder at /orchestration, configure your phases, then export as JSON, YAML, Bash, PowerShell, Docker, or GitHub Actions. Each export includes portable workflow content for review, handoff, or execution.
Can I use the published package for local orchestration?
Yes. Install prompts-gpt, create a PromptsGptClient with your project token, then use it to pull prompt packs, generate prompt files, sync agent-readable instructions, and run local orchestration. Review provider terms, privacy settings, and local run artifacts before sending sensitive code or prompts.
What is pipeline health validation?
Pipeline health checks validate your configuration before execution by verifying that dependency graphs are acyclic, all phases have prompts and tools assigned, timeouts are reasonable, and checkpoint coverage is present before you export or run the workflow.
What is the difference between parallel and pipeline mode?
Parallel mode races multiple agents simultaneously on the same task and picks the best result. Pipeline mode chains agents sequentially where each phase's output feeds the next — ideal for research → implement → evaluate workflows.
How does eval mode prevent low-quality output?
Eval mode extends pipeline mode with self-evaluation scoring against configurable criteria (correctness, citation-readiness, actionability). Outputs scoring below the quality threshold are automatically rolled back and retried with adjusted parameters.
What is the quality threshold in eval mode?
The threshold (0-1) sets the minimum acceptable quality score. Use --threshold 0.85 for production content workflows. Use --dry-run to validate eval criteria without executing changes. Individual criteria scores are reported alongside the overall quality score.
Optimize your AI visibility program with these proven approaches.
Start with 15–25 high-intent prompts
Focus on category, comparison, alternatives, and evaluation prompts that match real buyer language. Avoid generic informational queries that don't reflect purchase intent.
Run weekly scans for core prompts
Monitor your top 15 buyer prompts weekly and the expanded set monthly. AI answers change as models are updated and new sources are indexed.
Score content before publishing
Use the GEO Content Score Checker to evaluate pages before they go live. Ensure answer-ready blocks, FAQ schema, statistics, and entity clarity are in place.
Track source ecosystem breadth
Monitor which source types AI engines cite for your category. Build presence across owned, review, media, community, and partner sources — not just your website.
Explore related resources
Learn more about AI visibility workflows, optimization techniques, and platform features.
Show the same product truth to crawlers, AI systems, and evaluators.
Competitive AI visibility products expose public discovery files, evidence-led docs, and a transparent self-check path. prompts-gpt.com should do the same so the market sees the current AI visibility platform, not older prompt-library snapshots or a rerun URL without context.
Use the live checker for diagnostics. Use the published markdown or JSON self-audit exports when you need a stable public proof artifact. The JSON export also includes the Prompts-GPT.com project preset and monitor blueprint used for the ongoing internal follow-up path.
Brand facts
Canonical short-form product facts page for AI systems and evaluators.
brand-facts.json
Machine-readable product facts endpoint under /.well-known/.
llms.txt
Machine-readable source map for the current product truth.
robots.txt
Crawler access policy for public and private routes.
sitemap.xml
Canonical public URL inventory for discovery and recrawl.
Self-audit export
Stable markdown proof artifact for the prompts-gpt.com self-evaluation.
Self-audit JSON
Machine-readable self-audit payload with credibility notes and project guidance.
Self-audit report
Public methodology, export guidance, and current discovery gaps.
Live checker rerun
Fresh diagnostic surface for answer snapshots, citations, and recommendations.
Make each share surface explicit about stability, format, and audience.
The self-test showed that public discovery surfaces are only credible when buyers can tell which link is stable proof, which one is a live rerun, which files are AI-readable discovery aids, and which path requires sign-in for recurring monitoring.
Self-audit markdown export
markdownStable proofStable public proof artifact for external sharing, procurement review, and citations.
Audience: buyer
Open Self-audit markdown export: /reports/prompts-gpt-com-self-audit.mdSelf-audit JSON export
jsonStable proofMachine-readable self-audit payload with credibility notes, export context, and project guidance.
Audience: ai-system
Open Self-audit JSON export: /reports/prompts-gpt-com-self-audit.jsonSelf-audit article
htmlStable proofMethodology, current limits, and product-claim boundaries for public evaluation.
Audience: buyer
Open Self-audit article: /articles/prompts-gpt-com-ai-visibility-self-auditFree checker rerun URL
htmlLive diagnosticFresh answer snapshot, source list, and recommendations for live inspection.
Audience: operator
Open Free checker rerun URL: /free-tools/ai-brand-visibility-checker?site=prompts-gpt.com&submitted=1Official llms.txt
txtAI-readableCanonical AI-readable source map for the current public product story.
Audience: ai-system
Open Official llms.txt: /llms.txtrobots.txt
txtAI-readableCrawler access policy for public versus private routes.
Audience: crawler
Open robots.txt: /robots.txtsitemap.xml
xmlAI-readableCanonical public URL inventory for discovery and recrawl.
Audience: crawler
Open sitemap.xml: /sitemap.xmlPrompts-GPT.com project preset
htmlProject handoffAuthenticated handoff path from one public self-check into recurring monitoring.
Audience: operator
Open Prompts-GPT.com project preset: /free-tools/ai-brand-visibility-checker?auth=sign-up&next=%2Fdashboard%2Fprojects%2Fnew%3Fbrand%3DPrompts-GPT.com%26site%3Dhttps%253A%252F%252Fprompts-gpt.com%26category%3DAI%2Bsearch%2Bvisibility%2Bplatform%26source%3Dfree-checkerMake the public proof boundary explicit for every credibility check.
The self-audit should not force evaluators to infer which parts are inspectable now, which are only directional, which require a saved monitor, and which require sign-in. The matrix below makes that boundary machine-readable and visible on the page.
Answer snapshots
Inspectable nowA credible AI visibility product should show the actual answer snippet instead of summarizing results as a score only.
Inspect by
Run the checker on prompts-gpt.com, read the answer snippet and full-answer evidence, then preserve that run with the checker markdown or JSON download.
Share policy
Use checker-run markdown or JSON when sharing one diagnostic run. Use the self-audit export and article for durable product-level proof.
Citations and source evidence
Inspectable nowBuyers need to see whether owned pages, third-party reviews, or comparison pages are actually supporting the answer.
Inspect by
Inspect detected source URLs, provider-verified flags, top industry sources, and source labels in the live checker result or the checker-run exports.
Share policy
Treat citation lists from one checker run as directional evidence. Use the self-audit export to explain what the product can prove publicly today.
Recommendations
Inspectable nowDiscovery tooling is only useful when the answer evidence turns into concrete next actions instead of a generic score.
Inspect by
Review the checker recommendations below the answer snapshot and compare them with the documented workflow in the public docs.
Share policy
Recommendations from one run are acceptable to share as tactical diagnostics, but not as recurring trend proof.
Opportunity backlog
Inspectable nowOperators need a clear backlog that connects missing mentions or weak citations to pages, briefs, FAQs, or source fixes.
Inspect by
Inspect the opportunity cards and exported opportunity payload from the live checker run for prompts-gpt.com.
Share policy
Use checker-run exports for run-specific opportunities. Use the self-audit export to explain the proof boundary between one run and recurring monitoring.
Scoring and confidence
Directional onlySingle-run scores are easy to overclaim. Public proof should state that a preview score is directional until repeated prompt tracking exists.
Inspect by
Compare the preview score against the benchmark prompt pack and the scoring limitation notes before treating it as a durable benchmark.
Share policy
Do not use a single rerun score as a long-term performance claim. Use repeated monitoring for trend or share-of-voice claims.
Crawler-to-citation logic
Monitor onlyConnecting crawler events to later citations is valuable, but the free checker should not imply that capability without a saved monitor.
Inspect by
Review the public feature and docs pages for the workflow description, then confirm the actual crawler-to-citation evidence only inside a saved monitor.
Share policy
Keep crawler-to-citation claims at the workflow and methodology level in public materials. Do not present them as checker output.
Project guidance and recurring setup
Requires authA public self-check should hand off cleanly into a real recurring project when the platform's own prompt set is worth monitoring.
Inspect by
Use the built-in Prompts-GPT.com project preset and baseline monitor prompts after sign-in, then rerun the benchmark prompt pack as a saved project.
Share policy
Public proof can link to the authenticated handoff path, but project creation and recurring monitor evidence remain account-bound.
A credible self-test needs more than one branded rerun prompt.
The branded checker prompt is useful for a first-pass diagnostic, but recurring self-evaluation should also test category, competitor, recommendation, local, and buying-intent prompts. Those benchmark prompts are exported with the self-audit so the proof path stays reproducible.
Analyze AI visibility for prompts-gpt.com. Is the brand understood, mentioned, cited, and positioned accurately in AI answers?
diagnosticUseful as a first-pass branded diagnostic, but still biased because the prompt names the brand directly.
best AI search visibility platforms for marketing teams
categoryExpands self-evaluation beyond the branded diagnostic into category-level AI visibility prompts.
tools for monitoring brand visibility in ChatGPT and Perplexity
categoryExpands self-evaluation beyond the branded diagnostic into category-level AI visibility prompts.
prompts-gpt.com alternatives for AI visibility monitoring
competitorChecks whether prompts-gpt.com is explainable in comparison and alternative workflows instead of only branded prompts.
prompts-gpt.com vs PromptWatch for answer engine optimization
competitorChecks whether prompts-gpt.com is explainable in comparison and alternative workflows instead of only branded prompts.
which AI visibility tools should an SEO agency compare
recommendationTests whether the platform appears in shortlist and buyer-guidance prompts where evaluators expect independent recommendations.
recommend software for tracking AI answer citations and brand mentions
recommendationTests whether the platform appears in shortlist and buyer-guidance prompts where evaluators expect independent recommendations.
AI search visibility tools for teams in the United States
localVerifies whether region-specific discovery claims hold outside the core branded story.
AI visibility software pricing and features for a growing brand
buying_intentChecks whether pricing and feature pages support commercial AI answers with enough proof.
which AI brand monitoring platform should I buy
buying_intentChecks whether pricing and feature pages support commercial AI answers with enough proof.
Public AI visibility products are judged on proof, not only feature copy.
Competitor research consistently raises the same bar: exportable evidence, shareable reporting, public methodology, and a clean handoff into recurring monitoring. This kit exists so prompts-gpt.com reads like an AI Search visibility platform rather than a generic dashboard with AI-themed marketing.
Stable public proof export
supported publiclyBuyers need a shareable artifact that does not mutate when answer providers change.
Machine-readable evidence export
supported publiclyAI systems, evaluators, and internal tools need a structured payload instead of screenshot-only proof.
Live diagnostic rerun
supported with limitsOperators need a fresh answer snapshot to inspect citations, entities, and recommendations on demand.
Public methodology and claim boundaries
supported publiclyTrust depends on explaining what scoring, citations, and crawler context can prove versus what requires saved monitoring.
Recurring reporting handoff
monitor onlyA credible platform must convert a public diagnostic into a saved project when visibility work becomes ongoing.
PDF and CSV export with citation detail
supported with limitsStakeholders expect downloadable brand reports and citation spreadsheets with full and summary modes.
Scheduled report delivery
supported with limitsRecurring automated email reports eliminate manual export steps for teams that report AI visibility to leadership.
Shareable online dashboards
monitor onlyAgencies and teams need live dashboard links that stakeholders can access without account setup.
BI connector (Looker Studio)
monitor onlyData teams need live structured feeds into existing dashboards and reporting pipelines.
Content briefs from AI insights
supported with limitsVisibility data should produce concrete content recommendations, not just metrics.
Prompt gap and competitor gap analysis
supported with limitsBuyers need to identify exactly which prompts competitors own and where the brand is absent.
Geo distribution tracking
supported with limitsGlobal brands need to understand which countries and regions provide their highest AI visibility.
Public proof expectations were checked against live category examples.
This self-audit did not invent a reporting standard. The research pass checked how adjacent AI visibility products explain citations, documentation, and exports in public before setting the proof policy for prompts-gpt.com.
Semrush AI Visibility Toolkit
Recurring reporting, prompt research, Reddit analysis, negative sentiment detection, and shareable AI visibility dashboardsReviewed 2026-05-17Semrush publicly documents prompt research, visibility overview, brand performance reporting, PDF exports, CSV exports, shareable online dashboards, Reddit thread analysis for AI citation gaps, and automated negative sentiment detection with domain-level source attribution inside the AI Visibility Toolkit.
Why it matters: Semrush launched Reddit analysis and negative sentiment detection in May 2026 — buyers now expect AI visibility tools to surface social threads influencing AI answers and flag negative brand framing automatically.
Semrush KB - Getting Started with the AI Visibility ToolkitPeec AI
AI search analytics, source influence metrics, BI-friendly reporting, and $29M total funding ($21M Series A)Reviewed 2026-05-17Peec's public materials and funding coverage document a $21M Series A in November 2025 on top of earlier seed funding, plus daily AI-search monitoring across major answer surfaces. As of May 2026, its public pricing emphasizes brand and agency packaging with model and credit allocation details rather than one stable universal entry tier.
Why it matters: Peec is a serious mid-market competitor with public documentation for AI-search metrics, packaging, and MCP-style access. Its packaging complexity is relevant when buyers compare simple all-in plan messaging against model- or credit-shaped pricing.
Peec AI Docs and TechCrunch Series A CoverageProfound
Enterprise GEO platform with $96M raised (Series C at $1B valuation), 10+ engine coverage, agent analytics, and content agentsReviewed 2026-05-17Profound's public materials document enterprise AI-visibility coverage across major answer surfaces, agent analytics, and prompt or conversation drill-down workflows. Separate funding coverage reports a $96M Series C in February 2026.
Why it matters: Profound is positioned as a high-end enterprise reference point in the category. The relevant buyer signal is less the exact list price and more the combination of broad engine coverage, enterprise workflow depth, and sales-led expansion.
Profound - Enterprise GEO Platform and PricingScrunch
Citation drill-down and source-level influence reviewReviewed 2026-05-16Scrunch publicly documents citation grouping by domain or URL, platform filtering, and citation-level influence analysis in its help center.
Why it matters: Answer evidence without source drill-down is weak; buyers want to inspect which domains influence mentions and citations.
Scrunch Help Center - Understanding the Citations Tab in ScrunchOtterlyAI
GEO Landing Page Creator, export-oriented reporting, and Google AI Mode/Gemini add-onsReviewed 2026-05-17Otterly's public help and pricing materials describe three plans (Lite, Standard, Premium), prompt allowances, daily tracking, exports, a GEO audit workflow, and Google AI Mode or Gemini as paid add-ons.
Why it matters: Otterly's public materials make clear that AI Mode and Gemini require add-ons, which matters when buyers compare all-engine packaging across tools.
Otterly AI Pricing and FeaturesAhrefs Brand Radar
Search-backed prompt database with free AI visibility checker and cited domain analysisReviewed 2026-05-16Ahrefs publicly provides a free no-signup AI visibility checker with mentions by platform, top 5 topics, top 5 cited domains, and top 5 cited pages. Brand Radar tracks 400M+ monthly search-backed prompts derived from real search behavior across ChatGPT, Gemini, Perplexity, Copilot, AI Overviews, AI Mode, and Grok.
Why it matters: A free checker with no signup creates a strong top-of-funnel acquisition loop. Search-backed prompts from real user behavior are more trustworthy than synthetic question sets. Cited domain and cited page analysis gives actionable source intelligence.
Ahrefs - Free AI Visibility Checker and Brand RadarHubSpot AI Search Grader
Free AEO audit plus paid monitoring with a 5-dimension scoring modelReviewed 2026-05-17HubSpot provides a free one-time AEO Grader and a paid AI Search monitoring tool. The scoring model uses five weighted dimensions: sentiment (40 pts), presence quality (20 pts), brand recognition (20 pts), share of voice (10 pts), and market position (10 pts). The paid tier adds continuous monitoring across ChatGPT, Perplexity, and Gemini with week-over-week trend tracking and competitor comparison.
Why it matters: A structured scoring model with explicit dimension weights gives buyers a concrete evaluation framework. Free-to-paid funnel with HubSpot brand trust creates high-volume acquisition that category-specific tools must compete with.
HubSpot - AEO Grader and AI Search ToolPromptEye
Multi-LLM brand visibility tracking with snapshot reporting and industry reportsReviewed 2026-05-17PromptEye tracks brand visibility, ranking position, and source reliance across GPT, Perplexity, Gemini, and DeepSeek. Claude and Grok are available through paid industry reports. The tool provides baseline audits, ongoing change tracking, and client-ready snapshot reports for agencies.
Why it matters: A clear free trial tier with prompt-limited tracking and separate paid industry reports shows that the market expects both self-serve monitoring and one-time audit products.
PromptEye - Track Your Brand Visibility in AI ModelsRank Prompt
AI visibility scoring with prompt discovery, citation tracking, and agent modeReviewed 2026-05-17Rank Prompt monitors visibility across ChatGPT, Perplexity, Google AI Overviews, Claude, Gemini, and Grok with 0-100% visibility scores. The platform includes competitor mention tracking, citation source analysis, AI Content Studio for content generation, scheduled reporting with email alerts, and a natural-language Agent Mode for querying visibility trends.
Why it matters: Agent Mode for natural-language visibility queries and integrated content generation show the market shifting from dashboards to conversational AI workflows that close the measurement-to-action loop.
Rank Prompt - AI Visibility Monitoring PlatformSurfaced
13-model AI visibility monitoring with AEO Score and content recommendationsReviewed 2026-05-17Surfaced monitors 13 AI models including ChatGPT, Perplexity, Gemini, Claude, Grok, Meta AI, and DeepSeek with daily automated scans. The platform provides a proprietary AEO Score (0-100) benchmarking visibility, accuracy, sentiment, and competitive position. It includes competitor intelligence, AI-generated content blueprints, citation analytics, and weekly executive reports.
Why it matters: Tracking 13 AI models with a standardized AEO Score and content blueprints shows the bar rising for breadth of coverage and actionability in the AI visibility category.
Surfaced - AI Answer Engine Optimization PlatformPromptwatch
GEO platform with citation-optimized content generation and AI traffic attributionReviewed 2026-05-22Promptwatch tracks brand mentions across 10+ AI engines with daily refreshes, offers answer gap analysis showing competitor-owned prompts, and includes a built-in writing agent that creates citation-optimized articles based on 880M+ analyzed citations. The platform connects AI visibility signals to actual site traffic attribution.
Why it matters: Bundling citation-optimized content generation with visibility monitoring closes the measurement-to-action gap. AI traffic attribution connects visibility changes to business outcomes beyond vanity scores.
Promptwatch - GEO Platform for AI Search VisibilityQwairy
Complete GEO platform with 6 integrated modules, AI crawler analytics, and ROI attributionReviewed 2026-05-17Qwairy offers Cockpit (dashboard), Monitor, Act, Analyze, Optimize, and Measure modules covering 10+ AI engines with no per-provider surcharges. Includes AI crawler analytics, referrer tracking, ROI attribution, multi-locale monitoring, and REST API plus MCP integration. 40+ features across all modules.
Why it matters: Qwairy's six-module structure with ROI attribution and crawler analytics shows the market expects full-stack platforms that connect monitoring to measurable business outcomes.
Qwairy Features - Complete GEO PlatformAthenaHQ
Enterprise AI visibility with dynamic crawler management, citation engine, prompt volume estimation, and credit-based pricingReviewed 2026-05-17AthenaHQ monitors 8+ AI platforms with real-time sentiment intelligence, prompt volume estimation, and dynamic crawler-management positioning. Public materials describe self-serve and enterprise packaging built around credits and analytics integrations.
Why it matters: AthenaHQ's credit-based packaging makes actual cost less obvious from a single headline price. Its crawler-management and analytics-integration positioning shows the market moving toward platforms that connect AI visibility with traditional analytics.
AthenaHQ - Action on AI SearchWritesonic
Full-stack GEO+SEO platform with ChatGPT Shopping visibility and AI traffic analyticsReviewed 2026-05-17Writesonic combines AI search tracking across major answer surfaces with traditional SEO tooling, citation-source identification, shopping visibility, sentiment analysis, and an integrated SEO content agent.
Why it matters: Writesonic's bundling of GEO monitoring with full SEO content generation at $249/month sets a price-value benchmark for platforms offering both monitoring and content creation.
Writesonic GEO PlatformSemrush AI Visibility Toolkit
Enterprise AI Optimization with Reddit analysis, negative sentiment detection, 261M+ prompt database, and multi-model visibilityReviewed 2026-05-17Semrush launched Enterprise AI Optimization (AIO) automations in May 2026 adding Reddit Analysis (identify threads where competitors are cited but your brand is absent) and Negative Sentiment Analysis (surface domains contributing to negative brand perception). The core toolkit tracks visibility across ChatGPT, Gemini, Google AI Mode, AI Overviews, and Perplexity with AI Visibility Scores, brand performance, competitor research, and prompt research from a 261M+ prompt database across 15 regional databases. Their 248K Reddit post study confirmed Reddit is the #1 cited domain on Perplexity (4% share) and #3 on Google AI Mode (9% share).
Why it matters: Semrush's 261M+ prompt database, Reddit analysis, and negative sentiment detection set the enterprise bar. Their 248K-post Reddit study is the strongest public evidence linking social discussion to AI citation outcomes. Available standalone or with Semrush One subscription.
Semrush - Enterprise AI Optimization New FeaturesFoglift
Daily-to-hourly AI brand monitoring with position tracking, sentiment analysis, webhook alerts, and planned AI content writerReviewed 2026-05-17Foglift monitors 5 AI engines (ChatGPT, Perplexity, Google AI Overview, Gemini, Claude) with daily to hourly frequency depending on tier. Features include visibility score percentage, citation URL tracking, position tracking (1st/2nd/3rd in responses), sentiment analysis, prompt-level breakdown, weekly digest emails, webhook alerts, and competitor comparison. 2026 roadmap includes browser-based GEO queries (Q1), citation tracking expansion (Q2), multi-language monitoring (Q2), and AI content writer (Q2). Pricing: Launch $49/mo (daily), Growth $129/mo (twice-daily), Enterprise $299/mo (hourly).
Why it matters: Foglift's hourly monitoring cadence is the fastest in the market — no other competitor offers sub-daily refresh. Their planned multi-language support and AI content writer show the market converging on full-stack GEO workflows.
Foglift - GEO Monitor and RoadmapPeec AI
AI search analytics with multi-country monitoring, citation source analysis, sentiment scoring, Actions module, and $29M total fundingReviewed 2026-05-17Peec AI tracks brand visibility across 6+ AI engines (ChatGPT, Perplexity, Google AI Overviews, Gemini, Claude, DeepSeek — with Grok as add-on) with daily automated tracking. Features include mention monitoring with position and sentiment scoring, citation source analysis, competitive benchmarking, multi-country monitoring, and an Actions module. $21M Series A (Nov 2025, led by Singular) + seed = $29.1M total. 1,300+ brands, $4M+ ARR, 300+ new customers/month. Valuation above $100M. As of May 2026, public pricing emphasizes brand or agency packaging plus credits and add-ons rather than one stable universal starter tier. NYC sales office planned Q2 2026.
Why it matters: Peec's $29M funding and 1,300+ customer base make it the second-most funded mid-market AI visibility tool. Extra fees for Claude/Gemini and credit-model packaging create cost uncertainty that full-access platforms can exploit. NYC expansion signals aggressive US market push.
Peec AI - AI Search Analytics and TechCrunch Series AProfound
Enterprise GEO platform with $96M total funding at $1B valuation, AI-powered content agents, crawler analytics, and Fortune 500 logosReviewed 2026-05-17Profound raised $96M total through Series C at a $1B valuation (Feb 2026). Tracks 10+ AI engines with prompt volumes from opted-in user panels, answer engine insights, citation share analysis, and AI-powered content agents generating briefs and article drafts. Enterprise tier includes Agent Analytics for crawler tracking, SOC 2 Type II compliance, SSO, and API access. Ranked #34 on G2's Top 50 AI Products of 2026. Serves ~10% of Fortune 500.
Why it matters: Profound's $96M raise at a $1B valuation makes it the highest-valued pure-play AI visibility company. Their 10% Fortune 500 penetration and G2 #34 ranking set the enterprise credibility bar.
Profound - Enterprise GEO PlatformSurva.ai
AI visibility tracking with auto-generated content, direct CMS publishing, and tiered prompt/article plansReviewed 2026-05-17Surva.ai monitors ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews with daily scans. Features include one-click AI-optimized article generation, direct CMS publishing (WordPress, Webflow), competitor gap analysis, citation tracking, GEO content optimization, AI crawler logs, and Slack/Teams integrations. Pricing materials reviewed in May 2026 list Starter, Growth, and Business tiers with prompt/article capacity and API access on higher tiers.
Why it matters: Surva.ai's direct CMS publishing to WordPress and Webflow raises the implementation bar for monitoring tools. Their bundled article generation per plan creates a clear content creation value proposition.
Surva.ai - AI Visibility Tracking PlatformSEOmonitor
Unified SEO + AI search tracking from existing keyword lists with 50K+ prompt variations per keywordReviewed 2026-05-17SEOmonitor uses existing keyword lists (not separate prompts) to automatically generate 50,000+ prompt variations per keyword across ChatGPT, Gemini, and Perplexity. Tracks AI Search Presence (% of search volume mentioned), AI Search Cited (% cited as source), Citation Rank, Context Analysis, and Sentiment. March 2026 update added LLM Switcher for real-time model comparison. Builds independent historical data per model.
Why it matters: SEOmonitor's keyword-to-prompt approach eliminates the prompt setup barrier — existing SEO workflows extend directly to AI visibility. 50K+ prompt variations per keyword creates comprehensive coverage from minimal user input.
SEOmonitor - AI Search Tracking SoftwareSona
LLM-native competitor analysis with 17-check AI visibility audit and structured benchmarking frameworkReviewed 2026-05-17Sona provides AI visibility tracking across ChatGPT, Perplexity, Claude, and Google AI Overviews with competitor Share of Voice calculation. Recommends 50-100 query test suites organized by buyer journey stage. Free AI Visibility audit covers 17 technical checks (crawlability, schema, structure, freshness) in under 30 seconds. Competitive benchmark of 20%+ Share of Voice. Research finding: brands mentioned in first two sentences are 5x more likely to enter consideration.
Why it matters: Sona's structured benchmarking framework (50-100 queries by journey stage, 20% SoV target) and 17-check audit create a repeatable methodology that buyers find credible.
Sona - LLM Competitor AnalysisTargetlytics
AI brand ranking tracker with daily monitoring across ChatGPT, Perplexity, and Google AI OverviewsReviewed 2026-05-17Targetlytics tracks AI brand visibility with daily monitoring across ChatGPT, Perplexity, and Google AI Overviews. Features include brand mention tracking, competitor comparison, citation source analysis, and weekly email digests. Pricing starts at $49/mo for 50 prompts.
Why it matters: Targetlytics' affordable entry point ($49/mo) and daily monitoring cadence appeal to SMBs. Their focus on three core platforms keeps the product simple but limits multi-engine coverage.
Targetlytics - AI Brand Ranking TrackerSE Ranking AI Visibility Tracker
Hybrid SEO + AI visibility tracking with SERP-integrated monitoring and content optimizationReviewed 2026-05-17SE Ranking added AI Visibility Tracker alongside its established SEO toolkit. Monitors brand mentions and citations across ChatGPT, Perplexity, and Google AI Overviews. Integrates AI visibility data with traditional SERP tracking for a unified SEO + AI view. Pricing starts at $129/mo with a 14-day free trial.
Why it matters: SE Ranking's hybrid approach bundling traditional SERP tracking with AI visibility appeals to teams already using SEO tools. The 14-day trial removes adoption friction.
SE Ranking - AI Visibility TrackerTrakkr AI
SMB-focused AI visibility monitoring with competitive benchmarking and weekly reportsReviewed 2026-05-17Trakkr AI offers AI brand visibility monitoring targeted at SMBs. Tracks brand mentions across ChatGPT, Gemini, Perplexity, and AI Overviews with automated weekly reports, competitor benchmarking, and citation source analysis. 14-day free trial. Rated 4.7/5 by DemandSage.
Why it matters: At $79/mo, Trakkr fills the affordable SMB gap between free checkers and enterprise platforms. Their 4.7/5 rating suggests strong user satisfaction for the price point.
Trakkr AI - SMB AI Brand MonitoringAlertmouse
Real-time AI visibility alerts with free tier and lightweight monitoringReviewed 2026-05-17Alertmouse provides real-time monitoring alerts for AI search visibility changes. Offers a free plan with basic monitoring and paid tiers at $120/year. Focuses on alert-first workflows rather than comprehensive dashboards.
Why it matters: Alertmouse's free tier and alert-first approach appeals to teams wanting lightweight monitoring without dashboard overhead. At $120/year, it's the most affordable paid option in the market.
Alertmouse - Real-time AI Visibility AlertsGetMentions AI
AI visibility monitoring + managed brand placement execution across 192K+ partner websitesReviewed 2026-05-19GetMentions AI launched in April 2026 as an AI visibility platform combining analytics with execution. Tracks brand appearance across ChatGPT, Gemini, Google AI Mode, and Perplexity. Offers managed execution with page-level insertions and domain-level placements across 192,000+ partner websites. Pricing: Starter $85/mo (50 prompts), Growth $155/mo (100 prompts), Pro $289/mo (200 prompts), Scale $479/mo (350 prompts). Weekly refresh at lower tiers; daily at higher tiers.
Why it matters: GetMentions is the first platform to combine monitoring with managed execution, but execution means paid placements — not organic citability improvement. This is a fundamentally different value proposition from organic visibility optimization.
GetMentions AI pricing and launch announcementMeltwater GenAI Lens
Enterprise AI brand monitoring with AI-powered recommendations, LLM-generated reporting, and journalist source attributionReviewed 2026-05-19Meltwater GenAI Lens monitors brand presence across ChatGPT, Google AI Overviews, Gemini, Claude, Perplexity, Grok, and DeepSeek with 48-hour data refresh. Mid-year 2026 release added AI-powered recommendations and LLM-generated branded reporting. Source attribution identifies high-authority websites and journalists that AI models cite. Trusted by PR, communications, and marketing teams across 27,000+ organizations.
Why it matters: Meltwater's 27,000+ organization distribution is massive but serves PR/comms teams, not SEO or content marketing. 48-hour refresh is slower than daily monitoring competitors. Enterprise-only pricing excludes SMBs and agencies.
Meltwater GenAI Lens product page and mid-year 2026 releaseBrandlight
Enterprise AI visibility command center with technical crawler analysis, publisher intelligence, and cross-brand ROI planningReviewed 2026-05-21Brandlight publicly positions its platform around AI visibility, attribution, competitive intelligence, content optimization, publisher performance, technical crawl analysis, and enterprise command-center reporting. Public product copy highlights visibility across AI engines, query intent and citation analysis, crawl frequency and coverage monitoring, server-log analysis, multi-region deployment, and SOC 2 Type II compliance.
Why it matters: Brandlight expands the enterprise competitive set beyond SEO-native AI visibility vendors. Its technical crawler module and publisher intelligence reinforce that buyers expect AI visibility platforms to connect answer monitoring, crawlability, partnerships, and executive reporting.
Brandlight - AI Visibility Platform for Enterprise BrandsPublic proof should include what is still weak.
A credible AI visibility platform should publish risks discovered during self-testing, not only positive feature claims. These risks are mirrored in the self-audit markdown and JSON exports for external review.
Legacy prompt-library snippets still outrank AI visibility positioning for some queries
highEvidence: Public search results still surface the /prompts listing with a legacy 'Best AI Prompts Library' snippet while the homepage describes prompts-gpt.com as an AI visibility platform.
Impact: Evaluators can misclassify prompts-gpt.com as a generic prompt library instead of an AI Search Visibility platform.
Action: Keep /, /features, /pricing, /resources, /docs, /articles, and self-audit exports internally consistent; keep canonical AI visibility positioning in llms.txt and avoid reintroducing prompt-library-first metadata.
Live rerun links are easy to treat as permanent proof
mediumEvidence: The free checker URL is public and shareable, but the output can change as providers, citations, and model behavior shift.
Impact: External stakeholders may treat mutable diagnostics as stable evidence and misread score changes as regressions.
Action: Use markdown/JSON exports and the self-audit article as the default proof artifacts in public sharing and documentation.