The main tools for measuring brand visibility in AI search in 2026 are Peec AI, Otterly.AI, Profound, Alhena AI and Arbling. They overlap on the core act — asking AI assistants about your brand and logging the answers — and differ sharply on who they serve and what happens after the measurement. Peec is analytics for marketing teams, Otterly is monitoring for SEO workflows, Profound is an enterprise platform with content generation, Alhena leads with a free audit and sells CX agents, and Arbling (the company whose blog you are reading — bias declared) wires measurement to merchant product-data repair.
This page compares them honestly, including where each one is the wrong choice. Descriptions reflect each vendor's own positioning as of August 2026.
What these tools actually do
Every tool in this comparison automates the same core protocol: send prompts to AI assistants on a schedule, parse the answers, and report whether your brand was mentioned, whether your domain was cited, and who else appeared. The honest technical differences hide in four places. Surface coverage: which assistants are polled, and whether the tool distinguishes an assistant answering from memory from the same assistant reading live search results — a distinction that produced opposite results in our own August 2026 testing, where the search-enabled surface cited sources on 47 of 50 calls and the memory surface on zero of 50. Sampling: single runs versus repeats with a stability measure; assistants sample answers, so unrepeated results are anecdotes. Adjudication: keyword matching misreads answers often enough that human review changes scores. And the follow-through: whether the tool stops at reporting or connects to the thing that fixes what it found. Judge every vendor on those four, ours included.
Peec AI
Peec AI positions itself as AI search analytics for marketing teams: it tracks brand visibility, position and sentiment across ChatGPT, Perplexity and Gemini, and benchmarks those numbers against competitors. As of August 2026 its pitch centers on giving marketing teams the metrics layer for AI search the way web analytics covered classic search — rankings over time, share of voice, sentiment shifts. It fits teams that already run brand marketing programmatically and want AI answers folded into their reporting stack. It is measurement-first by design: the deliverable is knowing where you stand, in enough analytical depth to defend budget decisions. Where it stops, by design, is the merchant's own data: Peec reports what assistants say, and acting on the findings — fixing product feeds, restructuring content, repairing entity inconsistencies — happens in other systems. For a marketing team with an execution arm already in place, that division of labor is fine. For a store owner with no such team, the dashboard is only half an answer. We compare alternatives in more depth in Peec AI alternatives for e-commerce brands.
Otterly.AI
Otterly.AI sells AI search monitoring aimed at SEO practitioners: it tracks brand mentions and website citations across ChatGPT, Perplexity and Google AI Overviews, runs prompt research, and audits content for AI-search readiness, with crawlability checks included. As of August 2026 it markets itself as a top-rated tool in its category and offers a self-serve trial, which makes it one of the lowest-friction entries in this list — an SEO freelancer or in-house marketer can have tracking running the same afternoon. Its center of gravity is the SEO workflow: prompts behave like keywords, citations behave like backlinks, and the reporting maps onto how search teams already think. The same caveat applies as with Peec, in mirror image: Otterly measures and recommends, and execution stays with you. For content-led brands whose fixes are mostly editorial, that gap is small. For merchants whose gaps live in product data — missing identifiers, stale availability, unstructured attributes — the recommendations point at work the tool does not do.
Profound
Profound sells answer-engine optimization to enterprises: monitoring of how AI assistants represent a brand, shopping and agent-traffic analytics, and AI-assisted content creation, packaged with the workflow layer larger organizations expect. As of August 2026 it is the most expansive product in this set — closer to a platform than a tool, with named modules for monitoring, content generation and prioritization, and positioning aimed at brands and agencies rather than individual store owners. If you are an enterprise with multiple brands, a content operation, and procurement requirements, Profound is built for the shape of your problem. The considerations cut the same way: platform breadth brings platform pricing and platform onboarding, and the content-generation module raises a question worth asking any vendor in 2026 — whether AI-generated optimization content will keep earning citations as assistants get better at detecting it. For an SMB merchant, Profound is usually more platform than the problem requires.
Alhena AI
Alhena AI approaches from a different direction: its core products are AI shopping assistants and customer-support agents for e-commerce stores, with AI search optimization (AEO and GEO) as part of the offer, and its best-known entry point is a free AI visibility audit with fast turnaround. As of August 2026 it presents case studies with consumer brands and emphasizes revenue and CX outcomes rather than search metrics. The free audit is genuinely useful as a first look — a no-cost way to discover whether assistants mention you at all — with the standard caveat that a lead-magnet audit is scoped to motivate the next conversation, and that conversation is about Alhena's assistant products. If what you want is an on-site AI concierge plus visibility improvements around it, the bundle makes sense. If what you want is deep, repeatable, multi-surface measurement with evidence files, a lead-magnet audit is a starting point rather than the instrument.
Arbling
Arbling — our product, so weigh this section accordingly — is built merchant-side rather than marketing-side. The measurement layer runs the same protocol described above, at audit depth: 8 surfaces including the memory-versus-search split, repeat sampling with stability scoring, human adjudication of every answer, and evidence files shipped with every report. We publish our methodology by running it on ourselves — our August 2026 self-audit, 290 live calls, is quoted throughout this blog, including the unflattering parts. What makes Arbling structurally different in this comparison is what surrounds the measurement: a scoring engine that reads the store's actual product data across nine retail verticals, repair of the gaps it finds, republishing of verified product facts to the surfaces and protocols agents read, and attribution that ties AI-driven sessions to orders. Monitoring tools report the score; our loop exists to move it. Audits start at $1,200, the platform at $349 a month, and 800+ stores have been analyzed to date. The fit boundary, stated plainly: if you need marketing-team analytics across a brand portfolio, Peec or Profound fit better. If you are a merchant and the question is "why do agents skip my store and what fixes it," that is the case we built for.
How to choose
Match the tool to who does the work after the report. A marketing team with its own execution arm gets the most from Peec or Otterly: focused measurement, clean reporting, no product opinions. An enterprise consolidating monitoring and content operations under procurement gets Profound. A store owner who wants a free first look gets Alhena's audit, understanding what it is scoped to sell. A merchant whose problem is the store itself — the product data, the crawlability, the absent citable content — gets the loop where measurement and repair are the same system, which is what an AI visibility audit checks and then some, and is what we run at arbling.com. Whichever you pick, insist on the four technical tests from the top of this page: surface split, repeat sampling, adjudication, and follow-through. The dashboards look similar. The measurement underneath them does not.
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