Discovered Labs vs Otterly: Which GEO partner is best for B2B SaaS?

Updated January 12, 2026

The execution gap: Why monitoring alone won't win AI citations

You're reading this because your CEO asked "What's our AI search strategy?" at last quarter's board meeting, and you need a confident, data-backed answer. The challenge is that 48% of marketers now use AI for research, and your buyers are asking ChatGPT and Perplexy which vendors to shortlist before they ever visit your website.

The problem isn't lack of awareness. Nearly every VP of Marketing I talk with understands AI search matters. The friction point is execution: how do you close citation gaps when your 5-8 person team is already managing demand gen, ABM, and quarterly launches?

This comparison clarifies the strategic choice: invest in a monitoring tool that reveals your citation gaps (Otterly), or partner with a managed service that fills those gaps daily (Discovered Labs). Both operate on flexible, month-to-month terms. Both serve B2B companies. But they solve fundamentally different problems.

Core difference: Managed execution vs self-service monitoring

Otterly positions itself as a cloud platform purpose-built for tracking brand and competitor mentions across AI-powered search engines. According to their features page, the platform automatically scans answers from Google AI Overviews, ChatGPT, Perplexity, Gemini, Microsoft Copilot, and AI Mode, then aggregates data on frequency, sentiment, link citations, and keyword prompts. The platform serves 15,000+ marketing professionals who need visibility into how AI systems represent their brands.

What Otterly does exceptionally well is observation. You get dashboards showing citation frequency, sentiment analysis, and competitive benchmarking. You discover which competitors dominate AI recommendations while your company remains invisible.

You identify which prompts trigger mentions and which leave you invisible. One case study shows how Videoloft used these insights to refine messaging and create new content.

What Otterly doesn't do is produce that content for you. Multiple reviews note that Otterly "remains primarily observational, providing data and reports without built-in tools for content creation, optimization, or strategy execution." Teams pair it with other platforms or internal resources to translate insights into action.

Discovered Labs operates as an execution engine. We start with comprehensive AI visibility audits testing high-intent buyer queries across major platforms. Then we build the content infrastructure needed to close citation gaps.

Our service includes end-to-end content production using the CITABLE framework, third-party validation campaigns across Reddit and review platforms, technical schema implementation, and ongoing citation tracking. You get both the scoreboard and the daily execution that moves the numbers.

The model you choose depends on internal capacity:

• Have a content team with bandwidth for 20+ pieces monthly? Otterly provides the intelligence layer to guide their work.
• Team at capacity managing demand gen, ABM, and campaigns? Discovered Labs removes the execution burden entirely.

Feature comparison: Discovered Labs vs Otterly at a glance

This table reveals the strategic trade-off. Otterly delivers exceptional monitoring at accessible price points for teams with internal content operations. Discovered Labs delivers execution at agency service pricing for resource-constrained marketing teams who need the operational burden removed entirely.

Methodology battle: CITABLE framework vs standard optimization

The reason your $60,000-$100,000 annual content investment fails to get cited by AI systems isn't poor quality or lack of backlinks. It's structural. Large Language Models retrieve information differently than Google's algorithm, prioritizing clarity, verifiability, and entity relationships over keyword density and domain authority.

Discovered Labs developed the CITABLE framework specifically for LLM retrieval optimization:

C – Clear entity & structure: Every piece opens with a 2-3 sentence BLUF (bottom line up front) that explicitly states what entity we're discussing and the core answer. AI models scan for immediate clarity, not narrative buildup.

I – Intent architecture: We map not just the primary question but adjacent questions buyers ask in the same research session. If someone asks "best CRM for healthcare," they also ask about HIPAA compliance, integration requirements, and implementation timelines. Covering the question cluster increases retrieval probability.

T – Third-party validation: AI models trust external sources more than owned content. We orchestrate reviews on G2 and Capterra, secure relevant subreddit mentions through our Reddit marketing infrastructure, and build Wikipedia presence where applicable. This validation layer signals credibility to retrieval systems.

A – Answer grounding: Every claim links to verifiable sources. We avoid vague assertions like "industry-leading" without substantiation. Instead, we cite specific studies, customer outcomes, and third-party benchmarks that AI systems can verify during fact-checking passes.

B – Block-structured for RAG: Content is organized in 200-400 word sections with clear headings, tables for feature comparisons, FAQ blocks with schema markup, and ordered lists. This structure aligns with how Retrieval Augmented Generation systems chunk and retrieve passages.

L – Latest & consistent: We timestamp content and ensure information remains consistent across all platforms. AI models skip citing brands with conflicting data across Wikipedia, website, and review sites.

E – Entity graph & schema: We explicitly state relationships in copy and implement structured data (Organization, Product, FAQ schemas) so AI systems understand context. Instead of "our platform integrates with popular tools," we write "our platform integrates with Salesforce, HubSpot, and Microsoft Dynamics" with proper entity markup.

This methodology differs from traditional SEO optimization in intent. Research shows AI-sourced traffic converts 23 times better than traditional organic search for companies like Ahrefs. The optimization target shifted from ranking algorithms to retrieval systems, and the framework must adapt accordingly.

Otterly's approach focuses on identifying what currently works. Their GEO audit delivers competitor analysis, citation patterns, Reddit analysis, and Wikipedia insights. They recommend tactics like getting featured on Wikipedia, engaging in relevant subreddits, and structuring content for AI readability. These recommendations align with effective AEO principles, but executing them requires separate resources.

Content velocity: Why consistent publishing matters for AI authority

One client came to us publishing 8 blog posts monthly through a traditional agency. They ranked page 1-2 on Google for target keywords but remained invisible when prospects asked ChatGPT for vendor recommendations.

The reason wasn't content quality but topical coverage and consistency signals.

Large Language Models assess topical authority through breadth and depth of coverage combined with publishing consistency. A company consistently publishing on CRM integrations, compliance requirements, and implementation best practices signals expertise. This holds true whether you publish daily or weekly, as long as coverage is comprehensive and consistent.

Our packages start at 20 content pieces monthly, with larger clients reaching 2-3 pieces daily. This isn't generic blog content but researched, structured pieces designed as direct answers to specific buyer questions. Each piece targets a distinct query cluster, expanding your citation surface area across hundreds of potential retrieval scenarios.

The compounding effect matters more than individual article impact. Citations typically begin appearing within the first few weeks as topical authority accumulates. Over time, the volume of structured, verified content gives AI systems multiple high-quality sources to pull from.

According to our case study research, one B2B SaaS client went from 500 AI-referred trials monthly to over 3,500 in approximately seven weeks through consistent content production combined with off-site validation campaigns.

Otterly provides the intelligence to identify which topics and queries need coverage. You can see exactly where competitors are cited and where gaps exist. But translating that content gap list into published, optimized pieces requires internal writing resources, editors, technical SEO implementation, and project management. For teams already at capacity, that execution gap becomes the bottleneck between insight and impact.

Pricing and contracts: Flexibility without lock-in

Both platforms offer month-to-month flexibility, differentiating them from traditional agencies requiring 6-12 month commitments.

Otterly's pricing model uses prompt-based tiers. According to multiple 2025 reviews, the Lite plan at $29/month includes 15 search prompts. The Standard plan at approximately $160/month includes 100 prompts. The Premium plan at $422-489/month provides 400 prompts with advanced features. Teams can cancel anytime through account settings with no hidden fees.

This pricing structure works well for companies in early testing phases or those with limited budgets who need visibility intelligence before committing to execution. A marketing manager can start with $29/month to understand the citation landscape, then decide whether to scale monitoring or invest in content production separately.

Discovered Labs' pricing reflects managed service delivery. Our starting package at €5,495/month includes 20+ content pieces, comprehensive AI visibility tracking across ChatGPT, Claude, Perplexity, Google AI Overviews, and Copilot.

You also get Reddit marketing with dedicated account infrastructure. We operate on month-to-month terms.

The price difference reflects the deliverable difference. You're comparing a software subscription that provides data to a managed service that produces content, executes off-site campaigns, implements technical optimizations, and delivers ongoing strategic consultation.

For budget comparison context, traditional SEO agencies charge $5,000-$10,000 monthly for 15 blog articles optimized for Google rankings. Our pricing includes higher volume (20+ pieces) optimized for AI retrieval, plus third-party validation campaigns that traditional agencies don't provide. If you're already spending $8,000-$15,000/month on SEO with stagnant pipeline, reallocating that budget makes financial sense.

The month-to-month structure in both cases reduces commitment risk. You can test Otterly for $29-$160 to see if the insights drive internal action. You can test Discovered Labs for one month to see if citation rates improve and whether AI-referred traffic increases. Neither requires betting a year of budget on an unproven channel.

Final verdict: Choosing the right partner for your growth stage

The decision framework comes down to three questions: Do you have internal content capacity? Do you need both visibility and execution? What's your timeline for measurable pipeline impact?

When to choose Discovered Labs

Choose Discovered Labs when you need pipeline impact within 90-120 days and lack internal capacity to execute high-velocity AEO content production. Our managed service model works best for:

Resource-constrained marketing teams where your 5-8 person team is already managing demand gen, ABM, product launches, and existing content commitments. Adding 20+ monthly AI-optimized pieces to their workload isn't realistic.

B2B companies in competitive categories where prospects use AI for vendor research and you're currently invisible in ChatGPT recommendations. You're losing deals before sales conversations start because competitors dominate the AI consideration set.

Marketing leaders (VP/CMO level) who need to demonstrate measurable ROI to justify continued investment. We provide weekly citation tracking reports showing competitive positioning gains, AI-referred MQL growth, and conversion rate advantages that tie directly to pipeline.

Organizations where speed matters and you can't afford 6-9 months of internal ramp-up. Our team starts producing content week one, with initial citations typically appearing within 1-2 weeks for high-priority queries.

Companies requiring off-site validation execution through Reddit marketing, Wikipedia presence, and review platform campaigns. We maintain dedicated aged Reddit accounts and infrastructure to shape narratives in relevant subreddits, something internal teams rarely have time to build.

The investment makes sense when you calculate opportunity cost. If your average deal is $80,000 and your close rate is 25%, capturing 10 additional AI-influenced opportunities quarterly generates $200,000 in pipeline from a €16,485 quarterly investment. That's a 12:1 return, and research shows AI-referred leads convert at significantly higher rates than traditional organic search, lowering your effective CAC.

When to choose Otterly

Choose Otterly when you have internal content production capabilities and need intelligence infrastructure to guide optimization strategy. The platform works best for:

Companies with established content teams that can produce 15-20+ pieces monthly and need data on which topics, formats, and approaches drive AI citations. Otterly shows what's working for competitors so your team can adapt.

Organizations in testing phases wanting to understand the AI visibility landscape before committing to managed services. At $29-$160/month, you can run 60-90 day experiments to validate whether AI search matters for your category and buyer personas.

Marketing leaders building internal AEO capabilities rather than outsourcing. Otterly provides comprehensive audits covering competitor citations, Reddit analysis, and Wikipedia opportunities that help train internal teams on effective tactics.

Agencies managing multiple client accounts who need scalable monitoring. Otterly's higher-tier plans offer tracking across hundreds of prompts for agencies running their own content operations.

Companies with technical SEO resources in-house who can implement schema markup, optimize entity structure, and execute on audit recommendations without external help.

One limitation to consider: multiple reviews note that Otterly "remains primarily observational" with "limited actionability beyond observation." If your team identifies 100 citation gaps through Otterly's audit but lacks bandwidth to close them over the next quarter, the intelligence doesn't convert to pipeline impact.

Frequently asked questions

How long does it take to see results with Discovered Labs?
Initial AI citation signals typically appear within 1-2 weeks for high-priority queries. Most clients see 20-30% citation rates within 1-3 months with consistent content production and third-party validation building.

Does Otterly offer content production services?
No. Otterly positions as a monitoring platform that tracks brand mentions and provides optimization recommendations. Content creation, technical implementation, and off-site campaigns require separate resources or agencies.

Can I use both Otterly and Discovered Labs together?
Yes, though potentially redundant for most teams. Both provide AI visibility tracking. The value would come from using Otterly's granular prompt-level analysis to inform content strategy while Discovered Labs handles execution and ongoing monitoring.

What's the difference between AEO and traditional SEO?
Traditional SEO optimizes for ranking algorithms using keywords, backlinks, and domain authority. AEO (Answer Engine Optimization) optimizes for AI retrieval systems using clarity, entity structure, and verifiable sources. Research shows AI-sourced traffic converts significantly better than traditional search.

Do month-to-month contracts mean lower commitment to results?
The opposite. Month-to-month terms force agencies to deliver measurable value every 30 days or risk cancellation. It aligns incentives around continuous improvement rather than locking in revenue regardless of performance.

Key terminology

GEO (Generative Engine Optimization): The practice of optimizing content and brand presence to increase citations and mentions in AI-generated answers from platforms like ChatGPT, Claude, and Perplexity.

Share of voice: The percentage of relevant AI-generated answers that cite your brand compared to total citations in your category. Higher share means you appear more frequently in AI responses within your market.

CITABLE framework: Discovered Labs' proprietary methodology for optimizing content for LLM retrieval, covering Clarity, Intent architecture, Third-party validation, Answer grounding, Block structure, Latest information, and Entity relationships.

Citation rate: The percentage of tested buyer-intent queries where AI systems mention or recommend your brand. Higher citation rates correlate with increased AI-referred traffic and pipeline.

Ready to move from AI invisibility to consistent citations? We're currently working with 36 B2B companies, and clients typically see first citations within 1-2 weeks. Book a strategy call to discuss your specific citation gaps and get a custom 90-day roadmap, or explore our AEO Sprint for a 14-day intensive delivering 10 optimized articles and a complete visibility audit.