AEO Tool RFP Template: How to Evaluate and Compare AEO Platforms

Today, businesses need AEO platforms and technology that can track and measure AI visibility, explain what’s driving performance, and provide clear, prioritized recommendations for what to do next.

But, evaluating emerging AEO tech is tricky. The AEO landscape is relatively new, and it’s shifting quickly. That rapid timeline doesn’t fit well with a more standard procurement process. That means buying the right platform requires a meticulously crafted AEO tool RFP that clearly defines organizational requirements, scoring criteria, and vendor-fit questions.

Our AEO tool RFP ensures that you can build a more complete and objective evaluation process, compare AEO vendors consistently, identify potential methodology and workflow gaps, and use a weighted scoring framework to narrow down the solution that best fits your needs.

What is an AEO tool RFP?

An AEO tool RFP, or request for proposal, is a structured evaluation document used by organizations to solicit comprehensive bids from technology vendors that specialize in answer engine optimization (AEO). It serves as the formal framework for evaluating how different platforms track brand visibility, measure AI citations, analyze prompt-level data, and provide optimization recommendations for AI-driven search environments like Google AI Overviews, ChatGPT, Claude, Gemini, and Perplexity.

Rather than relying on vendor sales pitches, the RFP forces providers to document their underlying methodologies, data integrity standards, technical capabilities, and integration readiness. This standardized approach allows procurement and marketing teams to evaluate multiple platforms side-by-side on an objective scale.

What’s the difference between AEO RFPs and SEO RFPs?

While traditional SEO platforms and AEO platforms share some of the same foundations, evaluating an AEO platform introduces a very different set of requirements.

An SEO RFP typically focuses on crawler behavior, keywordKeyword
A keyword is what users write into a search engine when they want to find something specific.
Learn More search volumeSearch Volume
Search volume refers to the number of search queries for a specific keyword in search engines such as Google.
Learn More, backlink analysis, and traditional SERP ranking positions. It evaluates how well a tool measures a static list of organic search results on a search engineSearch Engine
A search engine is a website through which users can search internet content.
Learn More like Google or Bing.

An AEO RFP must address a highly dynamic, conversational searchConversational Search
Conversational search is an approach using natural language querying that allows users to ask questions as they would in human dialogue/voice search.
Learn More ecosystem. This requires extra attention in a few key areas:

• AI data collection: Unlike scraping a static SERP, collecting data from LLMs requires more sophisticated methods to capture unique conversational outputs, citations, and multi-turn dialogues.
• Prompt methodology: Traditional SEO tools measure keywords. AEO tools evaluate complex, natural language prompts and assess how intent changes based on the persona asking the question.
• Citation measurement: A top SERP ranking alone doesn’t tell you whether your brand is influencing an AI-generated answer. An AEO tool must accurately measure whether a brand is cited as a source or mentioned by the LLM and whether the context of that citation or mention is positive, negative, or neutral.
• Model coverage: An SEO tool primarily looks at Google and Bing. An AEO platform must evaluate visibility across a much more fragmented landscape of LLMs and answer engines, including ChatGPT, Perplexity, Claude, and Google's AI-driven search surfaces (AI Overviews, Gemini).

What should you look for in an AEO platform?

When conducting an AEO tool evaluation, organizations must look far beyond a basic feature checklist. The AI search landscape moves so quickly that a one-off feature might help in the short term, but not provide long-term value. Instead, buyers should evaluate the foundational architecture of the platform.

The major capabilities organizations should prioritize center around data quality and methodology transparency. Because generative AIGenerative AI
Generative AI is a class of AI that creates content like text, images, and code rather than analyzing existing data, powering tools like AI search.
Learn More outputs fluctuate, a vendor must be able to prove how they gather their data, how often it refreshes, and how they ensure its integrity. Usability and workflow integration are equally critical; a tool that isolates AEO data from the rest of the marketing ecosystem will inevitably create organizational silos and limit results.

The ideal AEO platform should connect AI visibility metrics to tangible business outcomes, seamlessly integrate with existing SEO and content workflows, translate millions of search signals and visibility data into prioritized next steps, and showcase the technical infrastructure to scale alongside an enterprise's growing needs.

Full transparency: Conductor is an AEO platform, so we have a point of view on what that foundation should look like. We believe the strongest platforms connect AI visibility with the search, content, technical, and audience intelligence behind it, translate that intelligence into clear prioritized next steps, make that intelligence available wherever teams and their AI agentsAI Agents
AI agents are autonomous systems that analyze data, make decisions, and take action to complete tasks with minimal human intervention.
Learn More already work, and help teams understand how those actions correlate with changes in performance over time.

Use the questions below to make every vendor—including Conductor—show you how their AEO technology actually works to ensure you make the right investment.

How to use the AEO tool RFP template

To streamline and standardize the procurement process, organizations should leverage a structured AEO RFP template.

Keep in mind that an RFP is most effective when it’s highly customized to an organization's specific needs and priorities. Before sending a document to vendors, teams should align the RFP with their internal goals.

Here’s how you can tailor our downloadable AEO tool RFP template to your organization:

1. Define your business requirements and primary use cases. A B2B enterprise focused on technical and security documentation will have very different requirements than an eCommerce retailer optimizing product pages for conversational shopping queries.
2. Review the template and mark criteria as required, preferred, or optional. Weigh the categories based on your organization's specific priorities. If technical website monitoring is a critical pain point, assign a higher weight to that category.
3. Standardize the questions and format across each vendor. It’s crucial that you ask vendors to substantiate all important claims with live demonstrations, technical documentation, or proof-of-concept data.

Once you’ve followed these steps, score the vendor responses using an objective, pre-defined rubric to eliminate bias.

The AEO tool RFP template outline

The following categories outline the core sections that should be included in a comprehensive AEO tool RFP. This framework serves as a starting point. Teams can and should add, remove, or reprioritize questions based on their specific AEO strategy and technology requirements.

As you work through each category, try not to stop at “Does the platform have this feature?” Ask what data powers it, how the capability connects to the rest of your workflow, and whether the resulting intelligence can actually be used outside of the platform.

Data collection & integrity

Because answer engines don’t provide public organic visibility metrics in the same way traditional search platforms do, data collection methodologies can vary significantly across AEO vendors. That makes it essential to assess how vendors collect AI search data and how transparent they are about that methodology.

Buyers also have to evaluate data reliability, consistency, source access, refresh cadence, and how the vendor handles collection disruptions when LLMs update their interfaces or models.

How Conductor approaches it: Conductor takes an API-first approach to AI data collection, sourcing response data directly from official model providers rather than relying solely on front-end scraping. This gives teams a more reliable foundation for measuring AI visibility while preserving access to the underlying responses, prompts, citations, and sources needed to validate what they’re seeing.

Example RFP questions:

• How many distinct AI engines do you track today? Please list them.
• How does your platform collect AI answer data—official model provider APIs, front-end scraping, or panel/clickstream data? Explain the legal (ToS, GDPR) and data-stability implications of your method.
• If your data collection relies on scraping AI interfaces, what happens to our historical data and reporting continuity when a provider blocks access or changes its terms?

Prompt generation & methodology

The shift from keywords to prompts completely changes how content opportunities are surfaced. Evaluate how AEO platforms identify, generate, organize, and prioritize these prompts.

The tool has to demonstrate that its prompts accurately reflect real search demand, different buyer personas, conversational intent, regional markets, and specific product categories.

A large prompt database alone doesn’t necessarily make the data useful. Buyers need to understand why the prompts being tracked matter to their business and whether the platform can organize them around the audiences, topics, intents, products, and markets they actually care about.

How Conductor approaches it: Intelligent Prompt Generation & Tracking gives teams control over prompt tracking based on audience personas, user intent, topics, brands, and regions. AI Audience & Intent Analysis adds another layer of context by connecting prompts to the “who” and “why” behind the search, helping teams evaluate visibility against the customer journeys that matter to their business.

Example RFP questions:

• Describe how prompts are generated. Can you generate thousands of prompts in minutes, grounded in our own site content and real search demand data?
• Can prompts be customized by buyer persona and journey/intent stage? How many intent stages does your model support?
• Is prompt creation automated at scale, or does it depend on manual entry, GSC-query import, or a pre-built industry prompt library requiring user review?

AI visibility measurement

Traditional rank tracking doesn’t apply 1:1 to AI answers. Content doesn’t rank in AI search; it’s either mentioned/cited or it isn’t.

Compare how vendors measure brand mentions/recommendations, direct citations, overall AI visibility, sentiment, and competitive performance. Explore exactly how these metrics are calculated and whether users can drill down into the underlying prompts, specific LLM responses, and exact sources cited to verify the data.

An executive-level summary metric on visibility is helpful, but it shouldn’t be a black box. Your brand could perform extremely well for a user who already knows your name, while barely appearing when a prospective customer asks an unbranded category question. Look for the ability to understand what a higher-level or aggregate metric represents and drill into performance by audience, intent, topic, prompt, engine, competitor, mention, and citation.

How Conductor approaches it:

• AI Search Performance shows not only whether a brand is visible, but where and why. Teams can evaluate mentions, citations, and sentiment separately, compare performance across topics and prompts, and understand visibility by persona and intent.
• Ask Conductor adds a conversational way to explore that performance data: teams can query the data directly to ask what changed in their own words, then follow the thread across engines, prompts, competitors, and cited sources without building a new analysis for every question.
• Mentions Quality is a tiered classification coming soon from Conductor that moves beyond counting how often a brand appears to evaluate how it appears in AI responses. The metric classifies brand mentions from weaker or negative placements through to cases where the brand is disproportionately recommended over competitors, giving teams a clearer north star for the quality of their AI visibility.

Example RFP questions:

• Which of the following do you report: brand visibility/share of model, sentiment of brand mentions, and exact cited source URLs?
• Can you measure AI referral traffic and connect AI citations to specific pages alongside organic performance data?
• Can you track AI crawler activity (GPTBot, PerplexityBot, Googlebot, Bing) on our site at the page level via continuous log file analysis?
• Can users ask questions about performance in natural language, ask contextual follow-up questions, and trace answers back to the same underlying account data used in platform reporting directly in the tool or platform?
• Does the platform turn visibility gaps into prioritized recommendations across content, third-party, and technical factors? Can teams track implementation and see correlated changes in performance over time?

Comprehensive visibility insights

AEO is about earning visibility and influence within AI-generated answers as buyers discover, compare, and shortlist their options. Those answers increasingly compress more of the customer journey into the response itself. At the same time, AEO builds on many of the same foundations as traditional search: useful content, technical accessibility, audience demand, and authority all continue to shape whether brands are discovered and cited.

That’s why teams need to understand traditional search performance and AI visibility together rather than through disconnected, siloed tools.

This is one area where Conductor has a clear point of view: AEO shouldn’t become another marketing data silo. Traditional search and AI search share too much of the same audience demand, content, authority, and technical foundation to evaluate them completely separately.

A unified platform should help buyers answer questions like:

• Where are we strong in traditional search but weak in AI?
• Which high-demand topics still have an AI visibility gap?
• And when visibility drops, is the issue related to content, competition, or the technical health of the site?

How Conductor approaches it: AI Search Performance sits alongside Conductor’s existing SEO, content, and technical intelligence rather than operating as a standalone AEO point solution. That gives teams a common foundation for understanding traditional search demand, AI visibility, content performance, and technical health—and Conductor’s MCP ServerMCP Server
The MCP server hosts the tools and resources for AI agents to use via the model context protocol, bridging the agent and external systems.
Learn More can extend that combined intelligence into external LLMs, agents, and custom AI workflows.

Example RFP questions:

• Is AI visibility natively unified with traditional SEO (rank tracking, keyword research, content workflows) in one platform and data model—or delivered as separate tools, SKUs, or stitched acquisitions?
• Can we see organic rank, GSC, GA4, technical health, and AI visibility metrics for a single page in one view, without exports or manual blending?
• When we create or modify keyword/topic groups or taxonomy, is historical data preserved retroactively, or is history lost for periods before the group existed?

AI content creation

Identifying where your AI visibility is lacking is only half the battle; creating or optimizing content to fill those gaps is the other.

Not only should your AEO tool offer insights into your content’s performance in traditional and AI search, but it should also provide AI-assisted content creation tools, optimization recommendations, grounding mechanics, and content workflow controls.

Almost every marketing platform can generate text now. “Does it have an AI writer?” isn’t as useful a question as asking what intelligence actually informs the content it creates.

Ideally, the audience, intent, search demand, competitive gap, content opportunity, and brand expertise that helped identify what content needs to be created should stay connected when teams move into executing the creation of that content.

How Conductor approaches it: Writing Assistant brings current AEO and SEO insights into content creation and combines them with approved voice, audience guidance, and proprietary business context. Conductor’s MCP capabilities extend that intelligence into the external AI environments where teams are already building content, rather than limiting optimization intelligence to a single writing interface or software.

Example RFP questions:

• Describe your AI content creation product: launch date, number of releases shipped since launch, and whether it is native to the platform or a recently added v1.
• How does your content generation stay grounded—e.g., RAG over proprietary crawl data and configurable expert 'wisdom sources'—to reduce hallucination?
• Can output be personalized to our brand voice and audience via configurable content profiles, rather than generic templates?

Technical AEO & website monitoring

An organization's content can’t be cited if answer engines can’t access or understand it.

The RFP should cover technical AEO areas like AI crawler monitoring, rendering issues, robots.txt directives, structured dataStructured Data
Structured data is the term used to describe schema markup on websites. With the help of this code, search engines can understand the content of URLs more easily, resulting in enhanced results in the search engine results page known as rich results. Typical examples of this are ratings, events and much more. The Conductor glossary below contains everything you need to know about structured data.
Learn More, technical site health, automated website change monitoring, and other capabilities that directly affect discoverability and accessibility across AI systems.

This is where it’s important to distinguish between measuring an AI visibility problem and actually diagnosing one. An AI search visibility tool might tell you citations declined. Strong technical monitoring and alerting within the same AEO platform can help you determine exactly what happened on your website that contributed to the decline so you can take action to resolve it ASAP.

How Conductor approaches it: Conductor Monitoring uses log data to show how AI crawlersCrawlers
A crawler is a program used by search engines to collect data from the internet.
Learn More are interacting with a website in real time—including which pages they’re accessing, how often they return, and where discovery barriers may exist. Teams can also instantly identify site changes and technical issues alongside AI visibility performance with 24/7 monitoring rather than waiting for the next scheduled audit.

Example RFP questions:

• Does the platform include continuous, real-time website monitoring with alerting—not scheduled multi-day crawls? How many distinct technical AEO issue types are detected?
• Are Core Web Vitals monitored continuously with real-time alerts on critical changes?
• Can your crawler compare crawls over time, audit staging environments, and render JavaScript?
• Which AI crawlers does the tool track and report on? Specify by AI engine and crawler type.
• What page volume can monitoring support? Provide evidence of the largest sites under continuous monitoring.

Analytics, ROI & reporting

Analytics are only useful when it can be tied back to business value.

Evaluate how platforms connect AEO performance to engagement, conversions, and broader business outcomes. Assess reporting flexibility, custom dashboards, analytics integrations, and the ability to roll up data for executive-level measurement.

At the same time, be wary of focusing on AI referral traffic. Many AI interactions can influence awareness, consideration, and purchase decisions without producing a clean referral click. A mature measurement strategy should combine visibility, mentions, citations, audience coverage, analytics, and downstream outcomes.

How Conductor approaches it: AI Search Performance gives teams a system of record for visibility, mentions, citations, sentiment, and competitive performance, while Conductor’s broader analytics integrations and Data API allow that intelligence to be combined with the reporting and business-performance systems an enterprise already uses.

Example RFP questions:

• Show how you would answer, in a single view today: 'Did our AI visibility gains last quarter actually drive traffic, conversions, and revenue?'
• Is your integration with GA4 and Adobe Analytics a bidirectional API that maps real-time traffic and conversion data to your organic and AI tracking groups natively, or does it require manual mapping files or the purchase of a secondary analytics SKU?
• What reporting flexibility is available: year-over-year and monthly/quarterly views, custom dashboards, multi-brand/multi-domain roll-ups, and large-scale data export?

Agentic & AI integrations

As organizations build internal AI ecosystems, AEO data needs to be portable and easily accessible where your teams already work. Assess how the vendor's data can connect with your broader technology stack.

Make sure that your RFP covers questions around APIs, workflow integrations, data exports, programmatic access, and Model Context Protocol (MCP)Model Context Protocol (MCP)
MCP is an open standard that enables AI agents to securely use external tools and data, acting as a universal API for LLMs.
Learn More.

But “Does the platform have an MCP?” is quickly becoming another checkbox question like “Does this platform create content with AI?” The more important evaluation is what intelligence the MCP actually gives your agents and whether that data is structured properly to provide reliable insights.

Buyers should understand whether an MCP is just exposing raw platform data or provides structured, contextualized intelligence designed for LLM reasoning. That distinction can affect the usefulness of the output, how much additional processing the external model needs to perform, and how easily teams can operationalize the data.

How Conductor approaches it:

• Ask Conductor gives teams a setup-free, no-code way to investigate their performance data directly inside the Conductor platform. Teams can ask questions in their own words and get grounded answers without adopting a separate AI tool, configuring MCP, or setting up an external connection.
• Conductor’s MCP goes one step further and makes AEO, SEO, content, sentiment, and more available to external LLMs, agent platforms, and custom workflows. Conductor uses a split-reasoning approach to transform that intelligence into LLM-ready signals rather than treating MCP solely as a connection to static raw data. By structuring more of the intelligence before it reaches the external model, the LLM has less interpretation to do at runtime—helping produce more reliable, traceable answers, reducing calls and token usage, and making it easier for agents to use the right metric or signal for the task at hand.
  • For content-specific use cases, Conductor’s MCP extends that model into content workflows, giving teams a way to bring Conductor’s optimization intelligence into the AI environments where they’re already researching, creating, and improving content.

Example RFP questions:

• Do you offer a production MCP server and LLM apps (e.g., a ChatGPT app) so our teams can use your data in agentic workflows today—not on a roadmap?
• When data is made available to LLMs or agents, is it exposed primarily as raw data, or does the platform provide structured, contextualized tools and signals designed for model reasoning? Demonstrate how this works with a real use case.

Security & enterprise readiness

Enterprise organizations can’t compromise on data security. An AEO platform may connect to proprietary content, analytics, internal datasets, and external AI models, creating new pathways for sensitive information to move across systems. Without the right controls, that could introduce risks around data exposure, unauthorized access, or proprietary information being used in ways the organization didn’t intend.

Evaluate whether the platform meets strict enterprise requirements for security, data privacy, user permissions, governance, and compliance. Specifically, the platform's ability to scale across global teams, large websites, multiple domains, distinct geographic markets, and complex organizational structures—while remaining secure—is critical.

As AEO platforms become more connected and agentic, enterprise readiness also extends beyond the core application. Buyers need to understand what data external agents can access, whether proprietary data is used for model training, what permissions apply to AI connections, and what security standards govern MCP and API access.

How Conductor approaches it: Conductor’s MCP is designed for enterprise AI environments and is backed by controls and certifications, including SOC 2 Type 2 and ISO 42001. Also, the data in Conductor’s MCP is not used to train public AI models.

Example RFP questions:

• Which certifications do you currently hold: SOC 2 Type II, ISO 27001, and ISO 42001 (AI management systems)? Provide reports.
• Do you support SSO and granular user/group permissions? Is pricing affected by the number of users?
• Do you offer a contractual SLA with an availability guarantee and service credits?

Company maturity & innovation

The AEO technology market features both established search platforms and brand-new startups. Evaluate the vendor’s experience, stability, enterprise customer adoption, and track record of consistent product development.

Given how fast AI search evolves, it’s also critical to assess how consistently the company is shipping, learning, and adapting its platform to market shifts.

But age alone isn’t a reason to choose a vendor, just as launching a long list of AI features doesn’t necessarily demonstrate maturity.

Once again, what’s more important than features is what those innovations are built on. It all comes back to the data foundation, technical infrastructure, enterprise experience, and existing workflows underneath the newest capability.

How Conductor approaches it: Conductor’s AEO capabilities build on its existing search, content, and technical intelligence rather than operating as a standalone AI visibility product. Features including AI Search Performance, Conductor Monitoring, Writing Assistant, and the MCP Server extend that foundation across measurement, optimization, technical diagnosis, content execution, and agentic workflows.

Example RFP questions:

• How long have you operated proprietary crawling and data-collection infrastructure at enterprise scale?
• What material AEO capabilities have you moved from development to production in the past 12 months? Provide release dates and examples of how the platform has evolved in response to changes in AI search.
• Which AEO capabilities are generally available today versus beta or roadmap? For roadmap items, provide expected availability and explain how you communicate changes in timing or scope to customers.

Support & strategic partnership

Technology alone won’t build a successful AEO program.

When comparing solutions, get to the bottom of exactly what’s required to deploy and operationalize the platform. Assess the vendor's onboarding process, end-user training, customer support SLAs, strategic consulting services, implementation resources, and capacity for ongoing, long-term partnership.

AEO is still new enough that many organizations aren’t just implementing another tool. They’re developing new KPIs, responsibilities, workflows, and ways for SEO, content, analytics, technical, and AI teams to work together.

How Conductor approaches it: Conductor pairs its platform with enterprise onboarding, customer success, education, and strategic services designed to help teams operationalize AEO at scale—not just learn where features live in the platform.

Example RFP questions:

• Describe your customer success model: named CSM, AEO domain expertise, and enterprise focus. Is this provided at no additional per-user cost?
• What are your support hours and channels?
• Will you provide a hands-on trial or proof of concept on our own domain and data before purchase?

Pricing & total cost of ownership

Finally, carefully compare pricing structures and understand what is included in the base platform versus what is billed as an add-on.

Account for all potential variables that impact the total cost of ownership, such as the number of users, volume of prompts tracked, answer engines monitored, geographic markets, API usage, MCP access, implementation costs, and strategic services.

This becomes especially important when comparing an integrated AEO platform against multiple point solutions. A lower entry price can look very different once an organization needs separate tools for AI visibility, traditional SEO, technical monitoring, content optimization, APIs, and agentic integrations.

Instead of comparing subscription prices alone, calculate the cost of the complete technology and workflow stack you would actually need whether it’s from one vendor or multiple.

Example RFP questions:

• Enumerate in writing every add-on required for full functionality: per-prompt increments, per-user fees, per-country research fees, data-refresh surcharges, and trend add-ons.
• Does full functionality require owning other products from your suite (specific analytics platform, CMS, CDN, or Ecommerce stack)?
• What is the typical time-to-value and onboarding ramp for a team of marketers (not data scientists)?

AEO RFP scoring rubric

A robust AEO RFP scoring rubric provides a framework for turning vendor responses into easily comparable numerical scores.

Explain to your evaluation committee how to assign priorities and weights to requirements based on specific organizational needs. A standard best practice is to use a 0 to 5 scoring scale for each individual question:

• 0: Does not meet the requirement at all.
• 1: Fails to meet the requirement but offers a complex, resource-heavy workaround.
• 2: Partially meets the requirement with significant limitations.
• 3: Adequately meets the requirement with standard functionality.
• 4: Exceeds the requirement, offering advanced functionality and ease of use.
• 5: Significantly exceeds the requirement, providing innovative methodology or capabilities that create a meaningful advantage.

Once raw scores are assigned, apply category weights. If Agentic & AI integrations and technical AEO are key to your strategy, a category weight of 1.5x or 2.0x will ensure that platforms focused on those areas rise to the top of the evaluation.

Keep in mind that a roadmap commitment shouldn’t receive the same score as functionality that your team can see and test. For your most important criteria, ask vendors to demonstrate the capability against your own use cases and data wherever possible.

How to evaluate AEO vendor responses

A strong response should demonstrate clear technical methodologies, provide screenshots or workflow examples, and explicitly define the limits of the platform's capabilities.

If a vendor responds with generic statements like "our AI does this automatically" without explaining the data inputs or underlying LLM processing involved, buyers should ask targeted follow-up questions.

For newer categories like MCP Server, AI content creation, conversational data analysis, prioritized AEO recommendations, and technical AEO, ask vendors to demonstrate what’s actually happening under the hood:

• What intelligence does the MCP provide?
• What data and model grounds conversational analysis?
• What inputs determine which actions are recommended and prioritized?
• What context grounds content generation?
• Is AI crawler activity directly observed or inferred?

These questions help separate meaningful capabilities from familiar labels applied to fundamentally different products.

Common AEO RFP mistakes

When running an AEO tool evaluation, procurement teams often make predictable errors that result in selecting the wrong platform. Be on the lookout for these common mistakes before you make your investment.

A primary mistake is treating every requirement equally. This goes back to our callout earlier to customize the RFP to your specific needs. If you’re focused more on the technical side of your AEO strategy, a feature that measures prompt-level AI citations accurately won’t be as important to you as 24/7 monitoring + alerting and a comprehensive changelog.

Another common error is focusing too heavily on feature counts rather than investigating underlying methodologies.

The same principle applies to emerging categories. “Has an MCP,” “offers AI content creation,” or “supports technical AEO” may all earn a checkmark while representing dramatically different levels of capability. Evaluate what powers the feature, what it connects with, and whether it actually solves the use case your team cares about.

Finally, teams often create an AEO technology RFP that’s overly broad or disconnected from tangible business goals, resulting in a selection process that values theoretical capabilities over practical, daily workflow improvements.

Common red flags in AEO RFP responses to look out for

During the evaluation, specific warning signs should trigger deeper scrutiny.

• Unclear methodologies around how AI data is collected are a major red flag. Look out for unsupported claims of "100% real-time data," hidden add-on costs for essential functionality, or roadmap features presented as current capabilities.
• Limited access to underlying data—where a tool provides a score but won’t let you see the prompts and sources behind it—can also make it difficult to validate and act on the insight.

For newer AEO capabilities, dig one level deeper. Does an MCP just expose the same raw data as an API? Does an AI writer have access to the platform’s actual AEO intelligence? Does technical AEO mean observing real AI crawler activity and website changes, or just basic audits of a few static directives?

Request live demonstrations of specific use cases and documented evidence for the capabilities that matter most to your evaluation.

FAQs

The AEO tool RFP in review

AI search requires investing in the right infrastructure to measure, optimize, and capitalize on AI-driven visibility, and utilizing a comprehensive AEO RFP template ensures that organizations approach this investment with rigor and objectivity.

But the goal isn’t to identify the vendor with the longest feature list or busiest roadmap. It’s to understand what sits behind those features: the quality and context of the data, the ability to diagnose both content and technical gaps, and how easily that intelligence can move into the AI systems, agents, and workflows where your organization actually gets work done.

That’s the standard we believe buyers should hold Conductor—and every other AEO platform—to.