How to get cited by ChatGPT, Claude & Perplexity: Managed AEO vs. DIY for B2B SaaS companies

Updated November 24, 2025

Nearly half your prospects now use AI to find vendors. When they ask Claude for key category queries such as "What's the best project management software for distributed teams?" they receive detailed recommendations with specific reasons. If your brand never appears in those answers despite ranking well on Google, you're missing a measurable opportunity to influence buyers at the research stage.

The question we hear from VPs of Marketing isn't whether you need an AI search strategy. It's whether your internal team has the bandwidth and specialized expertise to outpace competitors who are already securing citations daily across ChatGPT, Perplexity, and Google AI Overviews.

Why your SEO agency probably cannot fix your AI visibility problem

You likely assume your current SEO retainer covers AI search, or at least you did until recently. This assumption creates a gap between where buyers research and where your brand appears.

Traditional SEO focuses on ranking individual pages through keyword optimization, backlink building, and meta tag refinement. Answer Engine Optimization targets passage retrieval by Large Language Models, which requires entity structure, third-party validation signals, and content formatted for machine comprehension.

We've measured significant differences between traditional search rankings and AI citations. You can dominate Google for "best CRM for startups" while remaining invisible when a buyer poses the same question to ChatGPT.

The disconnect exists for three reasons:

1. Different retrieval mechanisms: Google ranks pages using authority signals like domain rating and backlinks. AI models use Retrieval Augmented Generation to fetch fresh data and synthesize answers from multiple sources. What wins in one system often fails in the other.
2. Citation versus ranking: Traditional SEO optimizes for clicks from results pages. We optimize for zero-click answers where the AI provides information directly. The metrics shift from rankings and traffic to citation frequency and share of voice.
3. Content velocity requirements: Most SEO agencies deliver 8-12 posts monthly. AI models favor recency and content freshness, which means our daily publishing cadence often outperforms less frequent content.

Your SEO agency built their expertise on Google's algorithm. AI search represents a fundamentally different channel, and most SEO professionals have not yet developed specialized expertise in optimizing for AI citations. The skills that drive Google rankings don't automatically translate to earning mentions in ChatGPT or Claude responses.

B2B SaaS AEO case study: Our B2B SaaS client had worked with an SEO agency for 4 years. This agency took a hard position of "AEO is the same as SEO", yet after only 7 weeks of us working with this client we were able to take them from ~500 to 3.5k+ free trials from AI search. Is it's all the same how were we able to achieve such an impact?

See a long-form video of this case study here:

The hidden costs of a DIY AEO approach

Building AEO capability in-house appears cost-effective on a spreadsheet. However, three structural challenges make DIY difficult to scale profitably:

1. Volume and velocity gap

To signal authority to AI models, you need consistent content answering specific buyer questions. Most internal teams manage 4-8 pieces monthly while maintaining existing responsibilities. Business leaders attempting DIY marketing can spend 10-20 hours weekly on execution and learning.

We publish minimum 20 optimized pieces monthly for clients, with larger engagements reaching 2-3 daily. This volume comprehensively covers the question clusters buyers actually ask AI, formatted as direct answers with clear entity structure.

Your content team cannot triple output without sacrificing quality or abandoning other priorities. The opportunity cost is real: every hour your demand gen director spends learning schema markup is an hour not spent on pipeline reviews or strategic planning.

2. Technical infrastructure requirements

AI models rely on structured data to understand your product. Implementing schema markup requires developer resources, not just copywriters. You need Organization schema, Product schema, FAQ schema, and HowTo schema properly configured.

Beyond structured data, you need tracking infrastructure to measure what matters. Traditional analytics show traffic and conversions. AEO requires monitoring citation frequency across multiple platforms, share of voice versus competitors, and which content pieces earn mentions.

Building this infrastructure means investing in specialized tools, hiring technical talent with AI optimization expertise, and continuous education as platforms update. One client told us they spent three months building tracking dashboards before publishing a single optimized page.

3. Off-site validation requirements

Here's what most teams miss: AI models trust consensus more than your claims. When Claude evaluates vendors, it cross-references your website against Wikipedia, Reddit, G2 reviews, industry forums, and tech blogs.

If your brand information is inconsistent across sources, AI systems may skip citing you because they cannot verify which details are accurate. Managing this validation layer requires PR coordination, community management on platforms like Reddit, review campaigns, and constant monitoring.

The hidden costs of DIY marketing include not just tool subscriptions but the expertise gap and trial-and-error waste. Your first attempts at schema implementation often fail. Your first 20 articles published without proper entity structure rarely get cited. Each mistake represents wasted investment.

How managed AEO works: The engineering approach

At Discovered Labs, we treat AEO as an engineering problem with measurable inputs and predictable outputs. We use internal technology and statistical testing to understand how AI models retrieve and cite content, then apply those findings systematically.

Step one: AI visibility audit

Before we write anything, we map exactly where you appear when prospects ask AI for vendor recommendations. Our proprietary auditing technology tests thousands of buyer-intent queries across ChatGPT, Claude, Perplexity, Google AI Overviews, and Copilot.

The audit reveals competitive gaps with precision. One client discovered they were cited in 5% of relevant queries while their top competitor appeared in 38%. This data becomes your roadmap, showing which question clusters to target first.

Comprehensive AI visibility tracking requires automation and scale that manual spot-checks cannot match.

Step two: CITABLE framework implementation

We produce content using our proprietary CITABLE framework, which ensures every piece is structured for LLM retrieval while remaining valuable for human readers.

Step three: Off-site authority building

We orchestrate third-party mentions across platforms AI models trust most. This includes strategic Reddit marketing with dedicated account infrastructure, review acquisition on G2 and Capterra, and PR outreach to industry publications.

Our research shows specific content formats perform better in AI citations. We structure content and distribution strategies around these patterns to maximize citation likelihood.

The goal is creating consistent narrative across all sources AI consults when evaluating your category. When every platform tells the same story with aligned facts, citation likelihood increases.

Step four: Continuous optimization

AI platforms update constantly. What worked last month may not work this month. We track citation rates weekly, monitor algorithm changes across all major platforms, and adjust strategy based on what earns citations versus what gets ignored.

You receive weekly reports showing citation frequency by query cluster, competitive share of voice trends, and which specific content pieces earned citations that week. This enables data-driven decisions about where to double down.

Comparison: Managed AEO vs. in-house vs. traditional SEO

Your investment decision depends on growth stage, internal capabilities, and strategic priorities. Here's how the three approaches compare:

The math shifts at different revenue stages. For companies under $500K ARR, the ROI timeline may not justify managed services yet. Between $2M and $50M ARR, where marketing budgets support specialized services, managed AEO typically delivers faster results.

Calculate the value of one additional deal per quarter. If securing 2-3 extra opportunities annually from AI-referred leads justifies the investment, managed services make sense. When your average deal size is $50K and improving AI visibility could add meaningful pipeline, the math becomes compelling quickly.

Case study: From 550 to 3,500+ AI-referred trials in seven weeks

We worked with a B2B SaaS company that noticed competitors consistently appeared in ChatGPT responses while their brand remained invisible. Despite strong Google rankings and a healthy content library, they were losing deals to vendors prospects discovered through AI search.

The starting position:

Our AI visibility audit revealed they were cited in fewer than 5% of relevant buyer queries. When prospects asked "What's the best [category] software for [use case]," competitors dominated answers. The strategy we implemented:

Over seven weeks, we executed a focused campaign:

• Content velocity: Published 100+ (66 in month 1) optimized answer pages targeting specific buyer questions, structured using our CITABLE methodology
• Technical optimization: Implemented comprehensive structured data across their site so AI models could clearly understand product features, pricing, and integrations
• Off-site validation: Secured 20+ mentions on high-traffic subreddits with 100s of engagements.

The results:

AI-referred trials increased from 550 per month to over 3,500+ within seven weeks. These leads converted to paid customers at 2.3x the rate of traditional organic traffic because AI had pre-qualified them based on their specific use case.

Read the full case study for details on specific tactics and timeline. The key insight: This was systematic execution of a technical methodology designed around how AI models retrieve and cite information.

When to build in-house and when to outsource

The build-versus-buy decision depends on your specific situation. Here's when each approach makes sense:

Build in-house if: You have excess content production capacity, operate in a highly technical niche where agencies would need months to understand your product, have under $500K annual revenue, or employ a technical marketer who enjoys mastering complex systems.

Outsource to managed services if: Your team is at capacity, you lack technical resources comfortable with schema markup, you need results within 3-6 months, your revenue is $2M+ annually where one extra deal quarterly covers the investment, or you want access to proprietary technology and methodologies that would take years to develop internally.

The decision hinges on opportunity cost. Every hour your VP of Marketing spends learning technical AEO is an hour not spent on strategic initiatives that drive immediate pipeline impact. For growth-stage B2B companies, executive time is your scarcest resource.

Making the decision: Calculate your invisible pipeline cost

Start with your average deal size and calculate potential impact. If your ACV is $50K and AI visibility could add one deal monthly, that's $600K annually. Against a $120K annual investment in managed AEO, you can model clear ROI.

Buyers who use AI for research often convert faster because AI pre-qualified them based on specific requirements. Companies that secure AI citations now will have compounding advantages as this shift accelerates.

If you're a VP of Marketing at a B2B SaaS company between $2M and $50M in revenue, the question is whether you have internal resources to execute faster than competitors already working with dedicated AEO partners.

We work with companies who want measurable results with transparent reporting and no long-term lock-in. Our month-to-month engagement model means we must deliver citation improvements every month to earn your continued business. Request an AI visibility audit to see exactly which queries your competitors own and which represent immediate opportunities.

FAQs

How long until I can show the board results?
Initial citations typically appear within 2-3 weeks of implementing schema markup and publishing optimized content. Measurable pipeline impact usually takes 3-4 months as citation frequency builds across multiple queries.

What metrics should I track to prove ROI to my CEO?
The primary metric is citation rate (percentage of relevant queries where AI engines mention your brand). Secondary metrics include share of voice versus competitors, position in AI-generated lists, and conversion rate of AI-referred traffic. Attribution tracking in your CRM shows pipeline impact directly.

Can we work with both our SEO agency and an AEO specialist?
Yes, they're complementary. Traditional agencies optimize for Google rankings while we focus on AI citations. Many clients keep both, adjusting allocation as buyer behavior shifts toward AI search.

Do managed AEO services require long-term contracts?
Not with us. We offer month-to-month terms because we understand skepticism around agencies that lock clients into 12-month contracts before proving value. You can scale up, down, or pause based on results.

What happens if AI platforms change their algorithms?
AI platforms update constantly, which is why continuous monitoring matters. We track changes daily and adjust content strategy accordingly. DIY teams often miss updates entirely, leading to sudden visibility drops they cannot diagnose.

Key terms glossary

Citation rate: The percentage of times a brand is mentioned when AI engines answer a specific set of buyer-intent queries. A 40% citation rate means your brand appears in 4 out of 10 relevant responses.

Share of voice: Your brand's visibility compared to competitors in AI-generated answers. If competitors are cited 3x more often across target queries, you have a negative share of voice gap.

Retrieval Augmented Generation (RAG): The process AI models use to fetch current information from external sources when answering queries. RAG enables AI to provide up-to-date facts despite training data cutoff dates.

Entity structure: How information about your company, products, and relationships is organized and labeled so AI models can parse who you are and what you offer. Proper entity structure uses schema markup and consistent naming conventions.

CITABLE framework: Our proprietary methodology for creating content that AI engines can reliably extract, verify, and cite in responses. The seven components ensure content meets both machine readability and human value requirements.