Discovered Labs vs. Growthx: Evaluating AEO scalability for enterprise teams

Updated January 19, 2026

Marketing leaders at high-growth B2B companies face a scalability challenge. Your company launches a new product line, expands into EMEA, or acquires a complementary solution. Suddenly you need to replicate AI visibility across three products, two regions, and five buyer personas. Traditional agencies scale by adding headcount and extending timelines by quarters.

We'll analyze how Discovered Labs and Growthx AI approach enterprise AEO scalability. Growthx positions itself as "expert-led, AI-powered growth" with "a proven process to accelerate time to outcomes you care about: AI visibility, traffic, conversions." Discovered Labs engineers AI citations through proprietary technology and a structured methodology built specifically for Large Language Model retrieval.

For VPs of Marketing and CMOs managing $2M-$50M revenue companies where prospects ask ChatGPT and Perplexity for vendor recommendations, choosing the right partner determines whether you appear in AI-generated shortlists or remain invisible while competitors dominate.

Why traditional scaling breaks in the age of AI search

Traditional content marketing agencies scale by hiring more writers, account managers, and strategists. This linear model worked when Google ranked individual pages based on backlinks and keyword density. More content meant more rankings meant more traffic.

AI search works on entirely different mechanics. When a healthcare technology buyer asks Claude "What are the best patient engagement platforms for mid-sized health systems?", the LLM synthesizes information across hundreds of sources, looking for entity consistency, verifiable facts, and structured data relationships. It doesn't care about your domain authority score. It needs to understand what your product is, who it serves, and why multiple authoritative sources validate your claims.

According to HubSpot's 2025 State of Sales Report, 74% of sales professionals believe AI is making it easier for buyers to research products. This shift creates a critical problem for agencies scaling through headcount. When you manage multiple products across different content teams, each team creates slightly different descriptions of your company. Product A's content says you serve "mid-market healthcare providers." Product B's content targets "regional health systems." Your website claims "enterprise healthcare solutions."

AI models read these inconsistencies as conflicting data and refuse to cite you. They prioritize sources with clear, unified entity definitions. The more writers you add without systematic entity management, the worse your AI visibility becomes.

Ahrefs documented that AI search visitors convert at a 23x higher rate than traditional organic search visitors. Losing visibility in AI answers means losing your highest-intent prospects. Throwing more bodies at content production creates a scalability trap because you optimize for the wrong outcome: ranking pages instead of earning citations.

How Discovered Labs engineers scalability with the CITABLE framework

Discovered Labs breaks the linear scaling model through the CITABLE framework, a seven-part methodology engineered specifically for LLM retrieval. This isn't adapted SEO tactics. It's a system designed around how AI models decide what to cite, allowing replication of AI visibility across new products in weeks rather than quarters.

The framework components work as an integrated system:

C - Clear entity & structure: Open every page with a 2-3 sentence BLUF (bottom line up front) that explicitly identifies who you are and what you do. This gives AI models an unambiguous entity definition to reference. When you launch a new product, we replicate this entity clarity pattern across all content.

I - Intent architecture: Answer the main question and adjacent questions users are likely to ask next. We map the question clusters buyers actually ask AI about your category, then structure content to address these related intents. This increases the surface area for citations across multiple queries.

T - Third-party validation: Include reviews, user-generated content, community mentions, and news citations. AI models weight external validation heavily. We systematically build this across Reddit, G2, industry forums, and technical communities to create citation confidence.

A - Answer grounding: Provide verifiable facts with sources, not vague claims. Every quantitative statement links to source data. For regulated industries like healthcare and fintech, this ensures AI models cite information that passes compliance review.

B - Block-structured for RAG: Use structured blocks of 200-400 words that AI retrieval systems can cleanly extract and quote. This formatting optimization makes your content "quotable" in the technical sense that LLMs prefer discrete, complete passages.

L - Latest & consistent: Timestamp all content and maintain unified facts everywhere. Conflicting information across pages destroys citability. Our knowledge graph tracks every entity definition across all content to maintain consistency as you scale.

E - Entity graph & schema: Implement explicit relationships in copy using schema markup. We define product-to-brand, person-to-organization, and service-to-category connections using structured data patterns that help AI models understand your offering architecture.

The economic advantage is that CITABLE scales through replication, not reinvention. When you launch Product B, we replicate the entity structure rather than starting from scratch. We adapt the intent map to Product B's use cases, connect it to the existing knowledge graph, and inherit the third-party validation you've already built. Discovered Labs' methodology documentation explains how we test content in proprietary sandbox environments before publishing, allowing us to ship with higher citation probability from day one.

Analyzing Growthx's approach to scaling growth operations

Growthx AI describes its service as a "growth operating system" that "maps opportunities to position brands as the best answer" and uses "agentic AI workflows with human review at critical moments." A job posting for SEO and GEO Specialist at Growthx reveals they explicitly offer Generative Engine Optimization services, with responsibilities including "design, develop, and run experiments in Generative Engine Optimization (GEO), sharing learnings back with the team."

The Growthx model centers on full-stack growth marketing. Their company profile on Himalayas positions them as solving "expert-led, AI-powered growth" across multiple marketing functions. This breadth is valuable when you need strategic guidance on pricing strategy, conversion rate optimization, and audience development alongside content production.

For AEO scalability specifically, the generalist approach creates trade-offs. Growth operations agencies typically scale through custom workflows per client. Each client engagement requires strategy sessions, custom playbook development, and ongoing account management. This works well for holistic growth advisory but adds 6-8 weeks of onboarding time when you need to rapidly deploy AI visibility for a new product line.

Growthx offers deep expertise in growth strategy and multi-channel execution. If your primary challenge is "we don't know what growth levers to pull" or "we need help with our entire go-to-market motion," a full-stack partner makes sense. If your specific bottleneck is "prospects ask ChatGPT for recommendations and we're invisible," specialized AEO execution delivers faster results.

The key differentiation is focus. Growthx builds growth operating systems. Discovered Labs engineers AI citation machines. Both are valuable, but they solve different problems at different speeds.

Critical comparison: Handling multi-product and international expansion

Enterprise scalability tests your AEO partner in two scenarios: launching additional products without cannibalizing each other in AI answers, and expanding into new geographic markets with localized content strategies.

Multi-product management: When you operate Platform A (project management software) and Platform B (resource planning software), AI models need to understand these are distinct offerings solving different problems. Poor entity management causes citation cannibalization where ChatGPT recommends your company but can't distinguish which product fits which use case.

Discovered Labs manages this through knowledge graphs and schema relationships. We define explicit @id patterns for each product entity, connect them to the parent organization entity using "brand" properties, and map product-to-category relationships. When content mentions Platform A, schema markup signals "this is Product Entity A, which has relationship type 'offers' to Organization Entity, and belongs to Category X." This prevents AI models from conflating your products.

The content structure mirrors this entity architecture. Each product gets its own intent map targeting the specific questions buyers ask about that solution category. Platform A content optimizes for "best project management for healthcare teams" queries while Platform B targets "resource planning for professional services firms." The third-party validation strategy builds separate G2 profiles, Reddit presence in different subreddits, and industry mentions that reinforce each product's distinct positioning.

International expansion: Scaling AEO into EMEA or APAC introduces language, cultural context, and regional platform differences. Google AI Overviews behave differently in the UK versus Germany. Perplexity usage patterns vary by region. Local competitors dominate regional AI citations.

Our approach replicates the CITABLE framework with localized intent mapping. We research what questions German healthcare buyers ask AI versus US buyers, identify regional competitors already earning citations, and adapt content to address market-specific concerns. The knowledge graph extends to include geographic relationships (Organization serves Region, Product available in Country) that help AI models surface you for location-specific queries.

Enterprise security and compliance requirements for AEO

Regulated industries like healthcare technology and fintech face a critical risk with AI search: hallucinations. When an AI model cites incorrect information about your product's compliance certifications, data security features, or regulatory approvals, you face severe reputational and legal exposure.

Traditional content marketing rarely addresses this because Google rankings don't create the same liability. A blog post that ranks on page two with vague claims is low-risk. That same content getting cited by ChatGPT in response to a CFO's question about SOC 2 compliance becomes a material problem if the information is outdated or imprecise.

The CITABLE framework's "Answer grounding" and "Third-party validation" components specifically mitigate this risk. Answer grounding requires that we "provide verifiable facts with sources, not vague claims." Every statement about compliance, security, integrations, or regulatory status links to authoritative documentation. When AI models cite your content, they're citing verifiable claims you can defend.

Third-party validation creates additional safety through external sources. We build presence in trusted sources AI models rely on, including "Wikipedia mentions, high-authority forums, industry publications, and genuine Reddit presence." When multiple authoritative sources confirm your claims, AI models cite with higher confidence and factual accuracy.

For healthcare technology companies subject to HIPAA, this becomes critical. Content must avoid patient data references, accurately represent compliance certifications, and ensure product descriptions match regulatory filings.

The verdict: Which partner fits your growth stage?

The choice between Discovered Labs and Growthx depends on your primary growth constraint and organizational readiness for specialized versus holistic support.

Choose Growthx if: You need comprehensive growth operations guidance that extends beyond AI visibility. Your challenges include pricing strategy, conversion rate optimization, audience research, and multi-channel marketing execution. You value strategic advisory and want a partner who can address growth holistically, with AEO as one component among many. You have budget flexibility above $15K/month and prefer customized, high-touch engagements with dedicated account teams.

Choose Discovered Labs if: AI invisibility is your specific bottleneck. Prospects tell your sales team they researched vendors using ChatGPT or Perplexity and received competitor recommendations that excluded you. You've invested in traditional SEO and content but rankings haven't translated to pipeline because buyers have shifted to AI search. You need rapid deployment across multiple products or regions with predictable economics. You operate in regulated industries requiring verifiable, compliant content that AI models cite correctly.

Month-to-month flexibility matters here. Discovered Labs operates on 30-day rolling contracts with weekly citation tracking reports, letting you prove progress to your CEO each month or cancel if results don't materialize within 90 days. When your board asks "What's our AI search strategy?" in next quarter's review, you need a partner who delivers reportable metrics (citation rate, share of voice, AI-referred pipeline) on a timeline that matches executive scrutiny.

If you're a VP of Marketing or CMO managing $2M-$50M revenue B2B companies, run this practical test: Open ChatGPT and ask "What are the best [your category] for [your ideal customer profile]?" If competitors appear and you don't, specialized AEO execution fixes that faster than generalist growth advisory.

The Ahrefs study showing 23x higher conversion rates from AI search traffic proves the pipeline impact. One mid-market SaaS company working with Discovered Labs increased AI-referred trials from 550 to 2,300+ in four weeks by implementing the CITABLE framework and systematic citation building.

The economic model shapes your actual ROI. Traditional agency retainers create linear cost scaling where adding a second product increases costs by 50-80% through additional headcount. Technology-leveraged models add products at 20-30% incremental cost through replication rather than reinvention.

Ready to audit your AI visibility gaps? Book a consultation with Discovered Labs to benchmark where competitors earn citations while you remain invisible, and receive a custom roadmap for closing those gaps within 90 days.

Frequently asked questions

How long does it take to scale AEO for a new product line?
Initial citation signals appear in 2-4 weeks for new products using Discovered Labs' CITABLE framework. Full optimization with 40-50% citation rates takes 3-4 months as the knowledge graph connections and third-party validation accumulate.

Do I need separate contracts for international regions?
No. Discovered Labs centralizes AEO through month-to-month agreements that extend across products and geographies, with localized intent mapping and content execution managed within the same engagement.

How does Discovered Labs handle compliance in regulated industries?
Through Answer grounding (verifiable facts with sources) and third-party validation (external authoritative mentions) within the CITABLE framework. Every claim about compliance, security, or regulatory status links to authoritative documentation that AI models can verify.

What happens if AI platforms change their citation algorithms?
Continuous testing in proprietary sandbox environments allows rapid adaptation. The CITABLE principles (clarity, verifiability, authority, structure) focus on what AI models need to cite confidently, which remains consistent even as specific platform mechanics evolve.

How do you prevent citation cannibalization between multiple products?
Knowledge graphs define explicit entity relationships using schema markup, ensuring AI models understand each product as a distinct offering with unique use cases, categories, and target buyers.

Key terminology

Answer Engine Optimization (AEO): Optimizing content specifically for synthesis and citation by AI models like ChatGPT, Claude, and Perplexity, distinct from traditional search engine optimization.

CITABLE Framework: Discovered Labs' seven-part methodology (Clear entity, Intent architecture, Third-party validation, Answer grounding, Block-structured for RAG, Latest & consistent, Entity graph) for engineering AI citations.

Share of Voice: The percentage of high-intent buyer queries where an AI model cites your brand versus competitors, measured across platforms like ChatGPT, Perplexity, and Google AI Overviews.

Knowledge Graph: A structured data representation of entities (products, organizations, people) and their relationships that AI models use to understand offerings and prevent citation conflicts.

Linear Scaling: Traditional agency model where adding a second product increases costs by 50-80% through additional headcount, versus technology-leveraged models that add products at 20-30% incremental cost through replication.