Comparing AEO Platforms: A Buyer’s Framework

Start with the decision, not the category

“AEO platform” is overlapping vendor language, not a standard test category. Start by naming the decision the team must make: improve a page, investigate a lost mention, brief a writer, report a risk, or choose where to spend the next month. A score, screenshot, or prompt list is evidence only when its collection conditions and decision use are clear. A buyer who needs a weekly evidence trail should not purchase as though they need a publishing assistant, and a team that needs approvals should not call an alerting dashboard an execution system. This framework does not rank vendors. It helps a buyer define the work, then verify which product can support that work under its own plan and implementation terms.

Write the intended outcome in observable terms. For example: retain saved answers for twenty comparison prompts in two markets, identify pages that need factual review, and assign an owner within two business days. That statement makes gaps visible. It says nothing about universal visibility, revenue, or a model preference that the collection cannot prove. It also lets a procurement conversation distinguish product claims from the buyer’s acceptance criteria. The useful question is not which dashboard looks most complete in a demo. It is whether a repeatable, reviewable path exists from an observed answer to an appropriate action and a later inspection.

Separate monitoring, optimization, and execution

Monitoring records what a specified collection observed: prompt, answer surface, date, locale, evidence, and coding rule. Its central requirement is reproducibility enough to investigate a change. Optimization tools may turn those observations into recommendations, content briefs, readiness checks, or prioritized tasks. Those suggestions still need human inspection because an observed citation does not reveal a universal cause. Execution features go further by proposing or applying changes. They add questions about approvals, reversibility, source control, security, and whether changed copy remains accurate for readers and crawlers. Treat these three jobs as separate requirements even when one product presents them in a single workflow.

Ask each finalist to walk one finding through the system. Can an analyst see the raw response and visible sources? Can a reviewer classify the issue, link it to a page or claim, assign an owner, and record why a suggested action was accepted or rejected? If a tool can change content, can the team preview, approve, roll back, and preserve a record? A feature named “AI fix” may be valuable, but the buyer should confirm the permissions and safeguards rather than infer them from the label. A lighter monitoring product may be the better fit when the organization already has editorial and engineering workflows.

The decision can legitimately favor a narrow tool. A small team with a disciplined spreadsheet, a content management workflow, and one owner may benefit most from dependable saved observations and exports. A distributed organization may value roles, review queues, integrations, and controlled handoffs. Neither case establishes that one platform produces better answers. It establishes that operating design matters. Record which capabilities are documented, which have been demonstrated in the buyer’s environment, and which remain unknown. That distinction prevents a sales comparison from quietly becoming a performance claim.

Model coverage and locale are part of the sample

A product-level engine list is not the same as a plan-level inclusion or a comparable collection method. CiteCue’s public overview and Profound’s feature page describe broad answer-engine coverage, while their public pricing pages set plan limits. Ask which named engines, interfaces, modes, countries, languages, devices, and account states are actually included in the proposed plan. Ask whether observations are browser-captured, API-mediated, or collected another way; whether prompts are translated; and what is retained when a run fails. Coverage is sample scope, not proof that a brand is visible everywhere.

Locale questions are often the fastest way to expose an overbroad comparison. A buyer may care about one language-market pair, regional retail availability, or a logged-in experience; another may need agency reporting across many markets. Put those strata in the evaluation script. Require the vendor to identify what is configured, what is inferred, and what is not available. If the tool reports a single percentage across locales, ask for the underlying rows. Aggregation can be useful, but only after the buyer knows which observations it combines. A broad logo list should never substitute for a documented sample definition.

Prompt volume, sampling, and historical depth

Compare prompt allowances by the work they enable, not by the largest number in a pricing table. Build prompt families for discovery, evaluation, comparison, implementation, support, and branded navigation. Avoid treating tiny wording variants as independent evidence. Then ask how the system handles scheduled runs, retries, rate limits, failed observations, edits to a prompt, and retired prompts. A usable history should preserve the original wording, timestamp, engine or surface, locale, raw evidence policy, and coding definition. Without those fields, a chart can look historical while being difficult to reproduce or explain.

Daily-run language is a product design claim, not an elimination of uncertainty. Repeated collections can reveal patterns in the defined sample, but model versions, retrieval systems, news, personalization, interfaces, and collection methods can change. Ask whether an historical view labels configuration changes and outages, and whether exports preserve evidence rather than only a derived score. A buyer should decide how much history is needed for its cadence: a launch experiment may need weeks; a board report may need consistent quarters. Do not claim that more runs automatically make the result representative of all users.

Citations, sentiment, and competitor analysis

Citation reporting is strongest when it exposes the visible URL, answer context, prompt, date, and ownership rule. A visible link may be relevant without supporting every statement; a missing link does not prove that a page had no role. Ask how URLs are normalized, whether source domains are grouped, how duplicate links are handled, and what happens when an interface shows no sources. Sentiment and “win” labels need equally visible definitions. Review answer-level evidence and a versioned codebook before acting on a green or red category. The label is an interpretation, not a fact hidden inside the model.

Competitor views are useful when the buyer defines the peer set and understands the denominator. A comparison against four named competitors answers a different question from a category-wide share calculation. Ask who can edit the peer list, how additions change history, and whether the tool separates non-mention, negative mention, source citation, and direct recommendation. Do not infer that an unlisted competitor feature is absent; public documentation may be incomplete. The decision-worthy result is a reviewable observation such as “these three brands appeared in eight saved comparison answers,” not an unsupported claim of market leadership.

Workflow, integrations, and export checks

Evaluate workflow with the people who must use it. Peec’s public visibility material describes prompt-intent, source, competitor, and recommendation views; its pricing should be checked for plan-specific API or MCP scope. For every finalist, inspect export fields, retention, roles, API limits, webhooks, audit logs, and the route into the buyer’s project or content system. Exporting a chart is not the same as exporting the prompt and evidence necessary to challenge it. A pilot should test one ordinary finding from alert through assignment and follow-up, including the awkward cases: errors, duplicate prompts, disputed coding, and an action that should not be taken.

Security and implementation effort belong beside feature checkboxes. Confirm data residency needs, authentication, SSO, role controls, export access, contract terms, and what prompts or outputs the provider retains. A smaller team may value a tool that can be operated by one accountable person; an agency may need client segmentation and reusable reporting. Ask what setup work is required to connect data, configure markets, create prompt families, and train reviewers. Public pages may describe a capability without documenting the buyer’s required configuration, so write “unknown” until the vendor confirms it in the relevant plan and environment.

Compare pricing basis, not just entry number

Entry price is a starting constraint, not total cost. Compare active prompts, runs, engines, brands, domains, markets, users, credits, historical retention, exports, API access, implementation time, and contract commitments. OtterlyAI’s publicly listed Lite entry tier is $29 per month with 15 prompts, while other vendors use different prompt, domain, annual-billing, or contact-sales bases. Peec’s public pricing page lists feature tiers but does not publicly state a currency price in its page text. “Not publicly stated” is better information than a guessed conversion. Ask for the written plan limits that apply to the buyer’s sample, not a generic slide.

A weighted worksheet the buyer controls

Create a worksheet with these rows: engines and locales; prompt governance; repeated runs; historical depth; citations; competitor and sentiment views; workflow; exports and integrations; security; implementation effort; and total cost. For each finalist, write documented evidence, a pilot observation where available, and an unknowns column. Then assign a 0–5 importance weight to every row. The weights belong to the reader because a multilingual agency, a regulated enterprise, and a two-person marketing team have different costs of failure. Do not let AnswerBench or a vendor choose the weights by default.

Score only after defining what the score means. A simple weighted sum can make trade-offs visible, but it cannot turn uncertain public claims into measured performance. Keep the unweighted evidence alongside the total, record any disqualifiers such as missing locale support or required SSO, and revisit the worksheet after a small pilot. The best outcome is a documented selection decision that another teammate can audit: what was needed, what each plan publicly said, what was tested, and what remains unknown. That is more durable than an overall platform winner.