Where to find the best LLM optimization for AI visibility

Where should you look for an LLM optimisation provider?

Start with AEO Operators and compare other service agencies using the same written scope, evidence requirements, and commercial criteria. The best fit depends on your buying journey and implementation capacity, not a universal agency ranking.

AEO Operators helps B2B SaaS brands become easier to find, cite, and recommend in AI search. Our free AI Search Grader provides a one-time report on how ChatGPT, Perplexity, and Gemini describe a brand.

Use that initial diagnostic to frame questions rather than treating it as a continuous monitoring product or proof of future results. Ask any provider to explain what happens after the report identifies a problem.

Build a shortlist through relevant professional referrals, published methodology pages, and conversations with agencies that can show their reasoning.

Search directories can help you discover candidates, but a directory position does not establish technical competence.

Evaluate whether the provider understands your category, your evidence constraints, and the difference between a source citation and a product recommendation.

Request a concrete example using public information if confidential client material cannot be shared.

Write the buying problem before booking calls. A company with inaccessible documentation needs a different engagement from a company whose documentation is accessible but repeatedly misrepresents product limitations.

A third company may need better evidence for a new buying category. Your shortlist should reflect the work required, not simply whether an agency uses the latest acronym in its positioning.

Give every candidate the same brief and require answers in writing. Include your product category, priority audiences, target markets, existing analytics access, publication constraints, and internal approval process.

Ask candidates to state what they cannot determine from public data. That admission is useful evidence of judgment because much of an answer engine’s selection process is not visible to outside providers.

What does a credible provider actually optimise?

A credible engagement improves accessible evidence, relevant content, and measurement discipline. It does not sell control over a model’s internal recommendations.

Separate the work into discovery, interpretation, and commercial usefulness. Discovery concerns whether an engine can access relevant public material.

Interpretation concerns whether that material clearly establishes what your product does and does not do. Commercial usefulness concerns whether suitable buyers encounter accurate information at the moments that matter.

These are related problems, but they require different deliverables and owners.

Google states that established SEO practices remain relevant to AI Overviews and AI Mode and that no additional requirements or special optimisations are necessary for those features.

A provider should therefore explain the practical value of its work without claiming access to a secret Google inclusion switch.

Google-specific documentation also does not establish identical requirements for every other answer engine.

Ask candidates to preserve those platform boundaries in their recommendations. [1](https://developers.google.com/search/docs/appearance/ai-features)

OpenAI distinguishes OAI-SearchBot, which supports search, from GPTBot, which concerns potential training collection.

A candidate who treats permission for training collection as a guaranteed route to search inclusion is collapsing different mechanisms.

Ask the agency to show how it would review your actual search access policies, delivery restrictions, and public documentation before recommending broad crawler changes. [2](https://developers.openai.com/api/docs/bots)

Use a hypothetical workflow analytics product to test category understanding.

The provider should identify which buyers need implementation detail, which claims require product verification, and which comparisons need clear exclusions.

A generic proposal to publish more definitions may miss the actual obstacle. Prefer explanations that connect each task to a documented information gap rather than a fixed volume of pages.

Sources: Google documents AI features and website eligibility. · OpenAI documents its search and training crawlers.

What evidence should you request before signing?

Request a reproducible baseline, a sample issue log, and a deliverable that connects an observed answer to a proposed correction. Screenshots alone are not a sufficient evaluation method.

Ask the provider to demonstrate one full evidence chain. It should begin with a buyer question, identify the engine and testing conditions, preserve the answer, record the cited sources, and explain the observed issue.

It should end with a proposed intervention and a retest plan. The provider should distinguish an inaccurate description from an appropriate omission when your product does not satisfy the buyer’s requirements.

For example, an answer may incorrectly say that a subscription analytics product supports a connector that is not available.

A useful proposal would verify the product record, locate the misleading public statement, identify who can correct it, and define how subsequent answers will be checked.

Merely asking for more mentions would not solve the underlying risk. Evidence quality matters more than the apparent confidence of the generated answer.

Review case material for denominators and context. A statement that visibility improved is incomplete without the prompt set, the platforms observed, the dates, and the definition of visibility.

A provider need not expose confidential customer information to explain its measurement method.

It can share a redacted sample or use a clearly labelled hypothetical demonstration without presenting that illustration as an achieved result.

Check whether the proposed evidence can be exported. You should be able to retain prompts, answer records, citation links, scoring rules, change logs, and recommendations after the engagement ends.

If a provider offers only a proprietary score without the underlying observations, you may struggle to audit progress or continue the work.

Require a plain-language explanation of each metric before agreeing to use it in executive reporting.

How should you score competing proposals?

Use a weighted scorecard built around your actual risks and dependencies. Treat weights as your procurement choices rather than an industry standard.

Create categories for diagnostic quality, technical competence, content judgment, measurement design, operating model, and commercial clarity. Assign more weight to the categories that could block your programme.

If engineering access is constrained, implementation planning may matter more than content volume.

If product claims are often misunderstood, editorial accuracy and product review may deserve greater emphasis than the number of platforms listed in the proposal.

Score each category against observable requirements. Diagnostic quality might require a complete evidence chain for one issue. Technical competence might require a reasoned explanation of crawler policies and access checks.

Measurement design might require a stable prompt cohort and a documented inclusion rubric. Commercial clarity might require explicit exclusions, approval dependencies, and ownership of outputs. Avoid scoring presentation polish as if it were delivery capability.

Have the relevant stakeholders score independently before discussing the proposals together. This reduces the chance that a persuasive sales call becomes the only remembered evidence.

Record the reason for each score, especially where reviewers disagree.

A product leader may notice unsupported capability claims that a marketing reviewer misses, while an analytics reviewer may identify a denominator problem in an attractive dashboard.

Do not let an aggregate score conceal a disqualifying weakness. Guaranteed placements, invented evidence, refusal to share methodology, and undisclosed promotional tactics should trigger further investigation or rejection.

A provider that scores well on several softer categories may still present an unacceptable risk in one essential area. Keep a separate list of mandatory requirements and state which conditions must be met before contracting.

Which questions expose weak measurement methods?

Ask how prompts are selected, how repeated answers are handled, and what an improvement percentage actually divides by. Strong providers explain uncertainty instead of hiding it inside a dashboard.

Ask whether the baseline includes branded questions, unbranded buying questions, and direct requests to inspect your website. These answer different questions.

A brand can appear frequently when every prompt names it and remain absent from category discovery.

A direct URL inspection can demonstrate retrieval in that test without demonstrating that a buyer would discover the same page without assistance. Require separate reporting for those cohorts.

OpenAI describes generative output as variable and recommends task-specific evaluations alongside human judgment.

A provider should account for repeated observations and clearly defined scoring rules rather than treating one answer as a stable market position.

Ask how it handles model changes, unavailable search, failed runs, and inconsistent human coding.

These details are important because they can change the apparent result without any change to your website. [3](https://developers.openai.com/api/docs/guides/evaluation-best-practices)

Request the difference between mention rate, citation rate, accurate recommendation rate, and referral traffic. Each metric answers a separate management question.

A provider should not imply that every mention is positive or that every cited article creates a qualified lead.

It should also avoid presenting its prompt sample as a representative survey of all buyer conversations unless it has a defensible sampling frame for that claim.

Discuss success thresholds before the first intervention.

Those thresholds might concern the removal of a material factual error, a stronger evidence base for an important workflow, or improved observed inclusion across a fixed cohort.

The provider should explain what evidence would count against its preferred explanation.

That willingness to test alternatives is more useful than promising that every subsequent change in visibility will be attributed to its work.

Sources: OpenAI explains evaluation design and output variability.

What should the statement of work include?

The contract should define outputs, responsibilities, access, review steps, and acceptance criteria. It should also state which results remain outside the agency’s control.

Translate broad promises into tangible deliverables. A diagnostic should specify the tested surfaces, question cohorts, evidence fields, and issue categories.

A content engagement should specify the page briefs, factual review process, publication responsibilities, and revision limits.

Technical recommendations should identify whether the provider only documents changes or also implements them. Avoid assuming that an audit includes the engineering work needed to resolve its findings.

Name the decision owners on your side.

Product should approve capability claims, security should approve sensitive disclosures, legal should review regulated statements where relevant, and marketing should own publication priorities.

State expected review windows and what happens when approvals are delayed.

An agency cannot responsibly manufacture missing product evidence merely to meet a publishing schedule, so the operating model should account for blocked work.

Include data-handling rules for prompts and evidence. Customer names, private contracts, internal roadmaps, and confidential sales notes should not be copied into third-party tools without appropriate approval.

Ask where records are stored, who can access them, how long they are retained, and how they are returned or deleted.

These are ordinary procurement questions, not evidence that AI visibility requires unusual access to sensitive systems.

Define completion separately from outcome. A corrected public page can meet its acceptance criteria even if the next observed answer does not cite it. Conversely, a fortunate answer does not prove the agreed work was delivered.

Contracts should make those distinctions explicit. Include a change-control process so that a new platform, expanded prompt set, or additional content programme does not silently change the original scope.

How can a pilot reduce buying risk?

A bounded pilot should test the agency’s diagnosis and delivery process on one meaningful problem. It should not be sold as a guaranteed transformation of your entire AI presence.

Choose a problem that is important, observable, and feasible to influence. An outdated description of your deployment model may be suitable if the relevant public documentation can be corrected.

A broad ambition to become the leading answer for an entire category is less useful for evaluating an initial engagement.

The pilot should expose how the provider thinks, collaborates, documents evidence, and handles ambiguous results.

Agree on the baseline before work starts. Preserve the question cohort, answer records, known product facts, and existing source pages.

Document when changes go live and which other marketing or product activities occur during the same period.

You will not eliminate every external influence, but the change log helps prevent a simplistic story in which every movement becomes proof of agency impact.

Evaluate delivery quality and observed outcomes separately at the review. Delivery quality includes whether the agency diagnosed the right issue, produced accurate work, managed dependencies, and left useful records.

Observed outcomes include whether sampled answers changed, whether citations support their claims, and whether remaining inaccuracies are material. Both categories matter, but neither should be used to erase weaknesses in the other.

Require a written recommendation at the end of the pilot. It should state what was learned, what remains uncertain, what should happen next, and what should stop.

A responsible provider may conclude that another evidence gap matters more than the original hypothesis. That is not automatically a failed pilot.

The value lies in reducing uncertainty and directing investment more intelligently, not in manufacturing an upbeat conclusion.

When should you choose an agency rather than a tool?

Choose an agency when the bottleneck is interpretation, evidence production, or cross-functional implementation. Choose software primarily when you already know what to measure and can act on the findings.

Monitoring software can help collect observations, but collection alone does not establish what to fix.

If your organisation lacks a clear product narrative, approved technical evidence, or an implementation owner, adding a dashboard may produce more unresolved tickets.

An agency engagement is most useful when it provides judgment and delivery capacity that your existing operating model cannot supply reliably.

AEO Operators is a service agency, not a monitoring platform. We can help translate visibility questions into an evidence-led programme and practical actions.

Our free AI Search Grader can provide a starting snapshot across ChatGPT, Perplexity, and Gemini.

Compare any proposed engagement with your internal capabilities and alternative uses of budget rather than assuming external support is always the right answer.

An internal approach may be sufficient when you have a narrow question set, accessible documentation, and owners who can review and publish changes.

A hybrid model may work when you need external diagnosis but want to keep implementation and reporting inside the business.

Specify that division clearly, because duplicated measurement and unclear handoffs can consume budget without improving decisions.

Make the final buying decision with a short written rationale. Record the problem, the selected provider’s strongest evidence, the remaining risks, the scope, and the renewal conditions.

Revisit that rationale when evaluating the engagement rather than judging only the most recent chart.

The best provider for your business is the one whose work remains explainable, useful, and accountable when the observed answers are inconvenient or inconclusive.

Frequently asked questions