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I Compared 7 AI Search Visibility Tools (With Real Pricing). Here's the Honest Breakdown

IdeaSniper — Fri, 09 Oct 2026 01:29:43 +0000

I Compared 7 AI Search Visibility Tools (With Real Pricing). Here's the Honest Breakdown

Disclosure: I'm the maker of Quotalis (one of the tools below). I tried to make this comparison fair — every fact about competitors comes from their official site as of today, and I'll say plainly where each tool (including mine) is not the best pick.

Last week I asked four AI engines about my own brand 168 times. They mentioned me zero times. That experiment taught me something uncomfortable: if AI can't see your brand, your customers can't either.

So I went shopping for AI search visibility tools — the ones that track how often ChatGPT, Claude, Gemini, and Perplexity mention and cite your brand. Here's what I found, with real pricing pulled from official sites today (October 9, 2026).

The shortlist

Tool	Engines monitored	Entry price	Free option
Surva.ai	ChatGPT, Perplexity, Claude, Gemini, Google AI Overviews	$49/mo	7-day trial
Foglift	ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews	$49/mo	Free tier ($0, weekly Perplexity)
Seeqly	ChatGPT, Claude, Gemini, Perplexity, Grok	$21/mo	7-day trial (card required)
Citany	8 incl. Kimi + Doubao	$49/mo	14-day trial + free brand audit
Finseo	Pick any 3 of 10 (+ regional add-ons)	Not published	None listed
Profound	Trial: ChatGPT, Gemini, AI Overviews	Enterprise custom	7-day free trial
Quotalis	ChatGPT, Claude, Gemini, Perplexity	$29/mo	Free audit, no signup

What each one is actually best at

Surva.ai — the "done" button. Its pitch is "most tools stop at monitoring, Surva.ai closes the loop": one click generates citation-optimized articles and publishes straight to WordPress/Webflow. If you want monitoring plus content production in one place, this is the most complete loop. The price reflects it ($49–$349/mo).

Foglift — the developer's pick. Free tier, public REST API, CLI, and an MCP server so your AI agent can query visibility data directly. If you live in the terminal, nothing else here speaks your language. The free tier (weekly Perplexity checks) is genuinely useful.

Seeqly — the cheapest paid entry at $21/mo, and the only one here doing grounded-vs-ungrounded answer comparison (how AI answers with vs without web grounding). Heads-up: the trial requires a card and bills on day 8. Also the only one with Hinglish/Indian regional language coverage.

Citany — the coverage king. Eight engine paths including Kimi and Doubao — the only tool here that watches Chinese AI engines. Its "Citation Intelligence" shows why competitors get cited, not just that they do. If your market includes China, shortlist this one.

Finseo — the enterprise attribution play. "Attribution from AI answer to revenue" plus 40+ integrations. Pricing isn't published (talk to sales), which tells you who it's for.

Profound — the best-funded name, now positioned as an "AI Marketer" agentic teammate. Note: their old $99/$399 self-serve tiers are gone from the pricing page — it's free trial or enterprise custom now.

Quotalis (mine) — the transparency-first indie option. $29/mo gets you weekly measurement across 4 engines. Two things I do differently: every score is reported as mean + range over 3 independent runs (never a misleading single number), and the methodology page publishes the limitations openly. Plus a free audit that needs no signup — run it on your own site in 30 seconds.

The uncomfortable question I ask about all of them (including mine)

How do you know the numbers are real? Most tools show you a single visibility score with no explanation of how it was measured. One lucky run and you look like a genius; one bad run and you're invisible. That's why I built Quotalis around 3 runs per prompt per engine with mean + range — and why I'd push any vendor on this question before paying:

How many runs back each data point?
Official APIs or UI scraping? (They answer different questions.)
What are the published limitations?

If a tool can't answer those three, you're buying a vibe, not a metric.

My honest take

Indie/small team, want honest numbers cheap: Quotalis ($29) or Foglift (free tier).
Need Chinese engine coverage: Citany, no contest.
Want monitoring + content in one loop: Surva.ai.
Enterprise with attribution needs: Finseo or Profound.
Cheapest paid seat: Seeqly at $21/mo.

No single tool wins everything. Pick the one that matches your situation — and whatever you pick, ask those three methodology questions first.

I write about AI visibility measurement in public. The 168-query experiment that started this: I Asked AI About My Brand 168 Times. It Mentioned Me Zero Times. Try the free audit on your own site — no signup.

I Asked AI About My Brand 168 Times. It Mentioned Me Zero Times.

IdeaSniper — Thu, 08 Oct 2026 07:36:26 +0000

Your customers don't Google anymore. They ask ChatGPT what to buy.

A few weeks ago I ran an experiment on myself. Fourteen questions about my own X account — who I am, what I build, whether I'm worth following — asked across ChatGPT, Claude, Gemini, and Perplexity. Three independent runs per question per engine. 168 queries total.

Strict mentions of my account: zero. Not a single one.

The problem nobody is measuring

Think about what that means at scale. Every day, buyers ask AI which CRM to pick, which agency to hire, which tool solves their problem. If your brand never appears in those answers, you don't exist for that entire channel of demand. And your competitors might.

SEO taught us to measure Google. Nobody is measuring AI answers — the fastest-growing discovery channel in history. So I built the radar I wished existed.

What I built: Quotalis

Quotalis tracks your brand's mentions, citations, and share of voice across ChatGPT, Claude, Gemini, and Perplexity — plus competitor tracking, so you can see who's eating your lunch.

The methodology, stated honestly:

Every prompt × engine cell runs 3 independent times, and reports the mean with a min-max range. Single AI responses are fluky; I never want you acting on one lucky output.
Full limitations published. Measurements route through a third-party gateway (OpenRouter) by default — I say so on the methodology page instead of pretending otherwise. Your own official API keys take priority when you bring them.
API results ≠ consumer apps. The models and their search backends change constantly. I publish that caveat rather than selling certainty.

What you can do in 30 seconds

I made a free AI-readiness audit — no signup. It checks your llms.txt, robots.txt, sitemap, and whether AI crawlers can actually reach your site. Most sites I tested fail at least one check.

Try to break it

I'm an indie developer building in public. If you run the audit or try the tracking, tell me what's missing for this to be genuinely useful to you. The harshest feedback so far has been the most valuable.

Also launching on Product Hunt today — would appreciate your eyes there too.

I built an open-source Rust memory engine that cuts AI agent tokens by 96% across 1,000+ turns

IdeaSniper — Wed, 16 Sep 2026 12:56:10 +0000

If you use autonomous coding agents (Cursor, Claude Code, Antigravity, OpenClaw, Hermes, Codex), you've likely hit these long-sprint pain points:

The "Turn 200" Amnesia: You set a database or security rule at step 10. By turn 250, the agent has completely forgotten it and breaks your build.
The "Alert Storm" Doom Loop: A test fails and dumps 100 lines of error logs. Recency bias drowns out the real root cause that happened 50 turns ago.
Runaway Token Costs: Re-sending full conversation history on turn 500+ burns 100k+ input tokens per prompt, quickly draining your wallet.

To solve this, I built and open-sourced Continuum — an ultra-lightweight, zero-dependency continuous temporal memory engine written in 100% pure standard library Rust.

Real Hardware Benchmarks (Apple M4)

Instead of stuffing 100k+ histories or spinning up heavy 2GB vector databases, Continuum maintains a strictly bounded physical memory manifold (750 slots, < 75 KB RAM).

We benchmarked Continuum using OpenAI tiktoken (cl100k_base) on an Apple M4 across real coding sessions:

Metric	Full Context Appending	Standard Sliding Window (10 turns)	Continuum (Native Rust Engine)
100-Turn Cumulative Tokens	148,522 tokens	26,450 tokens	5,896 tokens (96.03% slash)
Multi-Depth Needle Recall	5/5 (100%)	0/5 (0% - Forgotten)	5/5 (100% at Rank #1)
Rule Override & Contradiction	Ambiguous prompt conflict	❌ 0% (Forgotten)	✅ 100% (Latest override at Rank #1)
Task Success / Build Pass	100% (High cost)	❌ 0% (Broken config)	✅ 100% (Tests pass, 96% token cut)
Engine Retrieval Overhead	1.2 ~ 2.5s (full history scan)	N/A (truncated)	60.46 µs (< 0.0001s, 16,540 QPS)
End-to-End Prompt Latency	12 ~ 18s (100k token load)	1.1s (shallow window)	1.2s (compact 256-token prompt)
100,000-Step Stress Test	Process Crash (OOM)	Memory leaks	Flat 750 slots (< 75 KB RAM, 0 leaks)

The Takeaway: Sliding windows save tokens but destroy outcomes (0% task success). Continuum cuts token usage by 96%~99% while guaranteeing 100% multi-depth recall and contradiction resolution.

How It Works in 4 Bullets

Physical $O(K)$ Bounded Memory: Exactly 750 slots (< 75 KB contiguous RAM). Memory usage stays a flat line forever across 100,000 steps.
Subspace Diversity Deduplication: Eliminates alert storms without naive FIFO eviction. 500 repetitive errors collapse into minimal slots, protecting ancient root causes.
Retrospective Causal Revision & Supersession: Bypasses decay for genuine anchors, while actively suppressing stale predecessors when rules are updated/contradicted.
Zero External Dependencies: 100% pure Rust std — single standalone binary, zero GC pauses, microsecond startup.

1-Minute Quickstart (MCP)

Continuum comes with a built-in Model Context Protocol (MCP) server for Cursor, Claude Desktop, and Antigravity:


bash
# 1. Install standalone CLI
curl -fsSL https://raw.githubusercontent.com/reacherwu/continuum/main/install.sh | bash

# 2. Init in any repo (< 75 KB memory manifold)
continuum-cli init .