Why Isn't Your SEO Agency Getting You Cited by AI? (7 Mistakes to Fix)

Updated November 21, 2025

The invisible brand problem

Last month your agency sent their report showing organic traffic up 12% and a #3 ranking for your core keyword, meanwhile competitors are dominating your category's narrative in ChatGPT, Claude and Perplexity.

HubSpot's research shows that nearly half of B2B buyers now use AI for vendor research, yet most agencies still optimize for traditional search. We're not saying your agency is incompetent, they're just solving the wrong problem.

Mistake 1: Confusing SEO with AEO (the methodology gap)

Traditional SEO agencies optimize content to rank in a list of ten blue links. Answer Engine Optimization addresses a different problem: getting your brand cited when AI systems synthesize a single, personalized answer.

The core difference is fundamental. While SEO targets keyword placement and backlink velocity, AEO targets entity clarity, verifiable facts, and third-party consensus signals that Large Language Models trust. When your agency pitches "AI-optimized content" but their methodology includes keyword density targets and H1 tag placement, they're applying 2018 tactics to 2025 technology.

Here's what distinguishes real AEO methodology from rebranded SEO:

The gap matters because research from Ahrefs demonstrates that AI-sourced traffic can convert at significantly higher rates than traditional search. Your agency's "great SEO work" might be invisibly bleeding your pipeline to competitors who understand this shift.

Red flag test: Ask your agency to show you a screenshot of your brand appearing in a ChatGPT response for a core buyer query. If they can't produce one within 48 hours, they aren't measuring what matters. We document exactly how B2B SaaS companies get recommended by AI search engines, and it starts with visibility you can actually verify.

Mistake 2: Signing 12-month lock-in contracts

AI search changes weekly. OpenAI updates its citation preferences constantly. Google's AI Overviews shift what they surface. Yet most software marketing agencies lock you into rigid 12-month retainers with early termination penalties.

This destroys your agility. If a strategy isn't working in month two, you shouldn't be paying for it in month ten.

The financial trap is well-documented. Industry analysis reveals that early termination fees and hidden costs can make exiting expensive. For a $10,000 per month retainer, termination clauses often require payment of a substantial portion of the remaining contract value, plus unbilled work and setup fees you've already paid.

Beyond direct termination fees, you face:

• Non-refundable setup fees that were front-loaded into the first 60 days
• "Kill fees" for in-progress projects that must be abandoned
• Retroactive rate adjustments if you received an annual-commitment discount
• Data transfer costs to move analytics and campaign assets to a new partner
• Lost productivity as your team scrambles to fill the sudden marketing gap

The fix: Demand month-to-month terms after an initial 90-day strategy alignment period. At Discovered Labs, we operate on month-to-month terms with no 12-month lock-in, so we earn your business every 30 days with measurable results.

When agencies resist flexibility, ask this directly: "If your methodology works as promised, why do you need a 12-month lock-in to prove it?" Marketing experts note that confident agencies will accept performance pressure.

Mistake 3: Ignoring the entity layer (the CITABLE gap)

Your agency publishes blog posts optimized for keywords. But LLMs don't retrieve content based on keyword density. They retrieve based on entity clarity, verifiable facts, and structured information architecture.

This is why a competitor with "worse SEO" than you appears in ChatGPT responses while you remain invisible. They've structured their content for machine comprehension.

The CITABLE framework: To get cited, you must engineer content the way LLMs retrieve it. We use a seven-part framework to provide the structure AI systems trust:

• C - Clear entity & structure: Start with a 2-3 sentence BLUF (bottom-line up front) opening that explicitly names your company, product, and value proposition.
• I - Intent architecture: Answer the main question and 3-5 adjacent questions a buyer would naturally ask next.
• T - Third-party validation: Include citations from reviews on G2, mentions on Reddit, and references in industry publications that LLMs can verify.
• A - Answer grounding: Use verifiable facts with sources rather than unsubstantiated marketing claims.
• B - Block-structured for RAG: Format content in 200-400 word sections with clear headers, tables, and FAQs that retrieval systems can extract cleanly.
• L - Latest & consistent: Add timestamps and ensure your facts match across your website, Wikipedia, G2, and LinkedIn. LLMs skip brands with conflicting information.
• E - Entity graph & schema: Implement Organization, Product, and FAQ schema markup to explicitly define relationships.

If your agency's "content optimization checklist" includes "keyword in first paragraph" but not "entity disambiguation" or "schema implementation," they're operating from an outdated playbook. One B2B SaaS company went from 550 AI-referred trials to 3.5k+ in seven weeks after restructuring their content using this framework.

Mistake 4: Tracking vanity metrics instead of pipeline impact

Your agency's monthly report highlights impressive numbers: 47,000 impressions, 2,300 clicks, 15% engagement rate increase. Your CEO asks one question: "How many deals did this generate?"

You don't know. The agency doesn't track it. You're flying blind on the only metric that matters, which is pipeline contribution.

Marketing research consistently shows that social media likes, impressions, and follower counts create a false sense of progress while providing no strategic direction. An agency that focuses on these is either hiding poor pipeline results or doesn't know how to track attribution properly.

For AI visibility specifically, track these indicators:

• Citation rate: Percentage of relevant buyer queries where ChatGPT, Claude, or Perplexity mention your brand
• Share of voice: Your citation frequency vs. top three competitors across 50-100 core queries
• Position in AI responses: Are you mentioned first, third, or buried in a list of ten options?
• Sentiment of mentions: Does the AI recommend you enthusiastically or add caveats?

The attribution fix: Move beyond last-click attribution models that misleadingly credit only the final touchpoint. Implement multi-touch attribution integrated with your CRM. For software companies with 3-6 month sales cycles, a W-shaped or time-decay model captures the true influence of mid-funnel AI research on pipeline.

Proper tracking reveals the real impact of AI optimization. Companies that implement comprehensive attribution often discover that AI-referred leads, while smaller in volume initially, convert at substantially higher rates and influence deals traditional analytics never captured.

Mistake 5: Accepting low content velocity

Your agency delivers 8-12 blog posts per month. They're well-written, thoroughly researched, and published on a predictable schedule. And completely insufficient for AI visibility.

LLMs train on vast, constantly updated datasets, and as we detail in our AEO playbook, AI platforms update their training data and retrieval systems continuously. A publishing cadence of 2-3 articles per week means you're always 30-60 days behind current signals while competitors publishing daily stay fresh in AI citations.

Higher volume doesn't mean lower quality. It means comprehensively answering the long tail of buyer questions. When a prospect asks Claude "What's the best project management software for remote teams with compliance requirements in healthcare?" you need content that explicitly addresses that specific combination of entities: remote teams, compliance, healthcare vertical, project management category.

The strategic gap: Traditional agencies designed their model around 10-15 pieces per month because Google's crawl frequency rewarded that cadence. AI retrieval works differently. It's probabilistic and query-specific. More targeted answer blocks means more opportunities for citation across the vast spectrum of how buyers phrase their questions.

We focus on high-velocity, targeted content that answers specific buyer questions identified through query analysis, competitive gap mapping, and actual conversations prospects have with AI assistants. Each piece addresses a distinct question rather than recycling generic thought leadership.

Mistake 6: Lacking proprietary tracking technology

Your agency reports that "AI visibility is improving." They cite anecdotal examples of ChatGPT mentioning your brand. They reference general industry trends. But they can't show you concrete data on your citation rate vs. competitors because they don't have the technology to measure it.

Our analysis shows that most AI visibility tracking platforms have fundamental limitations. They test in incognito mode, missing the personalized, logged-in experience where real buyers research. They sample 50-100 queries when comprehensive visibility requires tracking 500+ query variations. They measure once per week when citation rates fluctuate daily.

Without proprietary tracking infrastructure, your agency is guessing. They can't tell you which of your 200 blog posts are actually being cited, what percentage of buyer-intent queries surface your brand, how your share of voice compares to competitors, or whether your citation rate is improving week-over-week.

Professional AEO tracking provides:

• Citation frequency: Your brand mentioned in X out of Y tested queries (your citation rate)
• Competitive benchmarking: Side-by-side screenshots of AI responses showing who appears and in what position
• Trend analysis: Citation rate trajectory over 4-8 weeks with variance calculations
• Content performance: Which specific pages and passages AI systems are retrieving

We built specialized tracking infrastructure and evaluation tools for this purpose. Our system tests hundreds of queries weekly across ChatGPT, Claude, Perplexity, Google AI Overviews, and Copilot. We track not just whether you're cited but how you're positioned and what competitors are saying. This data advantage informs strategy rather than relying on educated guesses.

Ask your current agency: "Can you show me our citation rate for the last 30 days across the top 50 buyer-intent queries in our category?" If they can't produce that data immediately, they're not actually measuring your AI visibility.

Mistake 7: Hiring generalists without B2B software expertise

Your agency has an impressive portfolio. They've grown e-commerce brands, scaled D2C subscriptions, and built awareness for consumer apps. They're smart, creative, and completely unprepared for B2B software marketing.

B2B software sales involve 3-6 month cycles, multiple stakeholders, technical evaluation criteria, and complex pricing models. Agencies without software industry experience lack the nuanced understanding of how your buyers think, what pain points drive urgency, and which proof points overcome objections.

This gap becomes catastrophic in AI optimization. When your agency writes content claiming "our AI-powered platform delivers unprecedented efficiency gains," they're using generic marketing language that LLMs flag as unverifiable puffery. A software-specialized agency knows to write "Our workflow automation reduced manual data entry by 47% across 200 customer implementations, according to our Q2 2025 user survey" with a link to the actual data.

Red flags of generalist positioning:

• Generic case studies that could apply to any industry
• No probing questions about your sales cycle, technical architecture, or competitive positioning during discovery
• Surface-level content that doesn't demonstrate understanding of your product's technical complexity
• Inability to speak your language regarding metrics like MRR, churn rate, expansion revenue, or PLG motion

The evaluation test: Ask any prospective agency about successful campaigns for companies in your specific software category. If they pivot to "transferable principles" or "our process works across industries," you're about to pay for their education on your dime.

How to audit your current agency in 30 minutes

Use this checklist to evaluate whether your existing marketing partner is equipped for AI-era visibility:

Methodology Assessment:

• Can they explain the difference between optimizing for Google's crawler vs. LLM retrieval?
• Do they have a documented framework specifically for AI citation (equivalent to our CITABLE approach)?
• Can they show specific AI tools they use and explain why they chose them?

Measurement Capability:

• Can they produce your current citation rate in ChatGPT and Claude within 48 hours?
• Do they provide competitive AI visibility benchmarking in monthly reports?
• Can they track attribution from AI-referred traffic to closed deals in your CRM?

Contract Flexibility:

• Does your contract have a month-to-month option after an initial trial period?
• What is the termination fee if you cancel early (should be $0 for month-to-month)?
• How much notice is required to pause or cancel service (should be 30-60 days maximum)?

Industry Expertise:

• Have they successfully worked with B2B software companies in your revenue range?
• Can they provide case studies with specific pipeline metrics from similar clients?
• Do they understand your sales cycle, buyer personas, and technical evaluation criteria?

Content Strategy:

• Are they publishing high-velocity content specifically optimized for AI retrieval?
• Do they implement schema markup on every content piece?
• Can they show examples of content structured using entity-first architecture?

Third-Party Validation:

• Do they have a strategy for building your presence on Reddit, G2, and industry forums?
• Are they actively managing review responses and ensuring fact consistency?
• Do they coordinate with your PR team on media mentions that AI systems will trust?

If you scored fewer than 12 "yes" answers, you're likely working with an agency built for traditional SEO rather than AI visibility.

Frequently asked questions

How quickly can a new agency fix AI visibility gaps?
Initial citations appear in 2-4 weeks, meaningful share of voice improvements take 8-12 weeks. Full competitive parity requires 3-6 months of consistent execution.

What's a realistic citation rate target for B2B software companies?
Aim for 35-45% citation rate across your core 50-100 buyer-intent queries within four months. Established market leaders often achieve higher rates but typically invested 12+ months building that position.

Can I keep my current SEO agency and add AEO services separately?
Yes, if your SEO agency handles technical optimization and traditional link building well. Just ensure content strategy is coordinated to avoid conflicting priorities.

How do I know if an agency's AI claims are legitimate vs. hype?
Ask for three things: their documented methodology with specific examples, access to their tracking technology showing real data, and case studies with attributed pipeline results. Legitimate agencies provide all three immediately.

What contract terms should I absolutely refuse?
Reject any annual lock-in without a month-to-month option after 90 days, termination fees exceeding one month's payment, and clauses that don't clearly grant you full content ownership.

Key terms glossary

Answer Engine Optimization (AEO): The practice of structuring content so AI-powered search tools (ChatGPT, Claude, Perplexity) understand, verify, and cite it as the authoritative answer to user questions.

Citation rate: The percentage of relevant buyer-intent queries where an AI system mentions your brand when synthesizing an answer. Measured across a defined set of core queries in your category.

CITABLE framework: Discovered Labs' proprietary seven-part structure for creating content optimized for LLM retrieval: Clear entity, Intent architecture, Third-party validation, Answer grounding, Block structure, Latest consistency, and Entity relationships.

Share of voice: Your brand's citation frequency relative to competitors across the same set of queries. If your brand appears in 20% of responses and the category leader appears in 50%, your share of voice is 40% of theirs.

Multi-touch attribution: A measurement model that distributes credit across all marketing touchpoints a prospect interacts with during their buyer journey, rather than crediting only the final click before conversion.

Ready to see where your brand actually appears when prospects ask AI for recommendations? Our AI visibility audit tests 50+ buyer queries across ChatGPT, Claude, Perplexity, and Google AI Overviews with transparent pricing and month-to-month terms, then delivers side-by-side screenshots showing exactly where you appear vs. your top three competitors. Book a strategy call and we'll walk you through the findings, honest about whether we're the right fit or not.