LLM Search Optimization: How to Evaluate an Agency Proposal

Start with the buyer decision, not the new label

LLM search optimization, answer engine optimization and generative engine optimization are labels for work intended to improve visibility in AI-assisted discovery. The useful question is not which label sounds newest. Ask what business decision the engagement should support and which discovery surfaces matter.

A B2B software company might need its product to be considered in comparison questions, while a local provider may care about accurate service, location and contact information. A publisher may prioritize source citations. These require different page sets and evidence.

Google currently describes optimization for its generative AI features as part of SEO. Its guidance says the same foundations still matter, including technical access and useful, original content, and warns that special AI files, forced content chunking and AI-only rewrites are not required for Google Search. [1] That statement is specific to Google. A proposal should name each engine it covers instead of treating every AI product as one system.

Write one sentence into the scope: “Help qualified buyers find verifiable answers about these products, in these markets, across these named search experiences.” Then list the page groups, languages, competitors and exclusions. If the agency cannot translate “increase AI visibility” into a bounded investigation, the proposal is not ready to price.

Gate one: prove that intended pages are accessible

Before producing more content, require a technical eligibility check. Important pages should return the intended response, be internally discoverable, use coherent canonical URLs and avoid accidental robots, noindex, authentication or rendering barriers. The sitemap should list preferred public URLs, but sitemap submission is not a guarantee of selection or visibility.

For Google AI Overviews and AI Mode, Google says a supporting page must be indexed and eligible to appear in Search with a snippet. It lists no additional technical requirement for those features. [2] An agency should therefore show evidence from the actual page, Search Console where authorized and the rendered result, rather than claim a private AI submission route.

Engine controls differ. OpenAI documents OAI-SearchBot for surfacing websites in ChatGPT search and treats it separately from GPTBot, which concerns potential model training. A site can allow the search crawler while disallowing the training crawler. [3] Ask the agency to record the current robots policy, CDN or firewall behavior and the business decision behind any change. “Allow all AI bots” is not an acceptable default.

The deliverable is a tested access matrix: crawler or engine, policy, affected page group, observed status, owner and proposed action. This is more useful than a generic screenshot saying the site is “AI ready.”

Gate two: fund source pages that deserve to be cited

Crawl access only creates eligibility. The proposal should identify where your site can offer information that is specific, current and useful enough to support a buyer’s answer. Ask for a page inventory tied to real decisions: comparisons, implementation requirements, limitations, pricing structure, integration details, policies, evidence and frequently misunderstood terms.

Require the agency to distinguish three work types. A factual correction fixes an inaccurate statement. A content gap adds information buyers need. An evidence gap adds support for a claim that is currently asserted without proof. Publishing twenty lightly rewritten articles is not equivalent to closing one material decision gap.

Each recommended page should have an intended audience, primary question, evidence owner, source inputs, update trigger and conversion path. For original experience or data, state how it was obtained and what it does not prove. Google’s current generative-search guidance favors non-commodity, people-first content and warns against scaled pages created mainly to manipulate search systems. [1]

Ask for examples before signing. A useful brief might specify an implementation comparison using your real integration requirements and maintained documentation. A weak brief says only “write an authoritative article about the best tools.” The former can be checked; the latter invites unsupported summaries.

Gate three: reconcile the facts that identify the business

Buyers and search systems encounter your company across the homepage, product pages, profiles, app listings, documentation and third-party references. A proposal should inventory material facts such as legal name, brand name, product names, ownership, locations, service areas, support contacts and current capabilities. Contradictions should become owned correction tasks.

Structured data can provide explicit clues about a page, but it must describe visible content. Google explains that markup classifies page information and should not contain facts absent from the page. [4] Schema.org defines sameAs as a URL that unambiguously identifies the item, such as an official profile. [5] Neither source promises an AI citation. Treat markup as a consistency and eligibility aid, not a ranking product.

Ask for an entity register showing each important fact, its authoritative owner, the approved wording, the pages or profiles where it appears and the review date. The agency should not invent awards, customer counts, certifications or founder biographies to make an entity appear stronger.

If the proposal includes digital PR or third-party mentions, require publication standards and disclosure rules. Inauthentic profiles or paid mentions presented as independent proof create reputational risk. Correct first-party facts and earned, relevant references are a safer foundation.

Compare deliverables, responsibilities and evidence

Convert every proposal into the same comparison table. Activities describe effort; deliverables describe what you can inspect and reuse.

Name who writes, approves, publishes and verifies each change. Clarify whether engineering, design, subject-matter review, outreach and analytics setup are included. Your SEO and LLM search optimization scope should be evaluated against the same standard, including when TechLithic is the provider.

Google’s guidance for hiring an SEO warns against ranking guarantees and providers who will not explain their methods. [6] Apply that warning to AI citations too. A provider can commit to research, implementation and measurement quality, but not to a third party’s future answer.

Measure coverage without manufacturing a ranking score

Measurement should begin with a saved set of realistic buyer questions grouped by journey stage, market and product. Record the engine, account or location conditions where relevant, test date, answer presence, cited domains, your cited page, factual accuracy and referral traffic. Archive enough context to explain changes, but respect each platform’s terms and avoid pretending one sample is universal.

Bing Webmaster Tools now provides an AI Performance report describing how site content is used in AI-generated answers across Microsoft Copilot and partner experiences. [7] Google’s current guidance points site owners to its generative AI reporting in Search Console. [1] ChatGPT can return current web answers with source links, while OpenAI separately documents the crawler used for search visibility. [8] These are different evidence streams, so a single proprietary “AI rank” should not replace platform data, referral analytics and manual verification.

Agree which outcomes are directional. Citation presence can vary by wording, time, model and user context. Referral sessions may be small or partially attributed. Assisted conversions need an agreed analytics model. Report these limitations beside the numbers.

A credible dashboard separates completed inputs, observable visibility and business outcomes. For example: pages corrected, source briefs published and facts reconciled are controlled inputs; citation coverage is observed visibility; qualified enquiries are a business outcome influenced by many factors.

Worked example: specify a six-week evaluation pilot

Consider an invented B2B software company evaluating a six-week pilot. The company selects forty buyer questions across four clusters: category comparison, integration, implementation and risk. The agency checks the baseline across the named engines, audits twelve priority pages and identifies six decision gaps and three contradictory product facts.

The pilot scope includes correcting the confirmed facts, improving four existing pages, publishing two evidence-led pages, validating crawler access and setting up a repeatable result archive. This is an illustrative scope, not a universal workload or timeline.

Do not make “gain ten citations” a delivery acceptance condition. The team can verify that the agreed system and content shipped. Citation change belongs in the monitored outcome section, with no guaranteed direction or timing.

Compare the pilot fee together with internal review time, development effort, publishing capacity and ongoing measurement. The cheapest proposal may be expensive if it leaves your team with unapproved drafts, inaccessible dashboards or no way to repeat the test.

Use this proposal and acceptance checklist

Before signing, trace one promised recommendation from diagnosis to evidence, implementation owner and acceptance test. Then confirm the full boundary:

• Are the engines, markets, languages, page groups and exclusions named?
• Does the baseline preserve exact questions, dates and collection conditions?
• Are crawler access and training permissions treated as separate decisions where applicable?
• Does the content plan close specific decision or evidence gaps?
• Are material business facts assigned to authoritative owners?
• Does structured data match visible content and use appropriate types?
• Are production changes, outreach, design and subject review included or excluded?
• Are platform reports, analytics and manual checks kept distinct?
• Are ranking, citation and traffic guarantees absent?
• Will you receive editable files, access ownership and an operating handover?

At delivery, sample the work. Fetch agreed pages, inspect visible facts and markup, trace sources, repeat part of the question set and reconcile the change log. Accept the engagement on completed evidence and controls, then monitor discovery outcomes over the agreed observation period.

If your site is not technically accessible, buy a focused SEO audit with implementable findings first. If the website lacks reliable product information, content and subject review may be the real need. If you already have those foundations and want a controlled cross-engine pilot, this framework can help you compare search and LLM visibility programs without confusing marketing language with proof. The broader SaaS buying checklist covers security, portability and total-cost questions for supporting tools.

Walk away from guarantees and hidden methods

Reject proposals that guarantee placement, cite a secret relationship with an engine, promise hundreds of instant mentions, add unsupported claims, or refuse to show the question set and work log. Be cautious when the entire strategy is an AI-only file, schema on invisible content, mass-produced FAQs or profiles created only to manufacture authority.

Also reject overbroad access requests. An initial assessment rarely needs ownership of your analytics, Search Console, CMS or business profiles. Use least privilege, retain account ownership and record when temporary access will be removed.

Do not buy a separate LLM search engagement merely because the label is fashionable. A focused technical repair, product-documentation project or editorial program may solve the actual constraint. The right proposal makes that diagnosis visible, even when it reduces the work being sold.

Frequently asked questions