AI systems now mediate how buyers discover, evaluate, and shortlist software. When a prospect asks ChatGPT "What is the best project management tool for remote teams?" and your product is absent, you lose pipeline before you ever knew you were in contention.
LLMs recommend competitors because they lack structured entity data about your product. No entity clarity means no citation, regardless of product quality.
When AI does mention you, it generates vague or incorrect descriptions synthesized from scattered, inconsistent sources rather than authoritative information you control.
AI systems require multi-source validation before recommending products. Without structured proof across reviews, docs, and expert mentions, LLMs default to established brands.
Well-optimized competitors own the semantic space in your category. They have established entity authority, consistent messaging, and the verification signals AI systems trust.
Generative Engine Optimization is not a bolt-on to traditional SEO. It is a distinct discipline that requires entity modeling, structured content architecture, and distributed proof systems designed specifically for how LLMs retrieve and synthesize information.
Our methodology is built on the Proof of Importance (PoI) framework: seven signals that determine which products LLMs choose to cite. Every engagement strengthens these signals systematically.
- Model your product as a disambiguated entity within your category's semantic landscape
- Run 100-250 prompt tests across ChatGPT, Perplexity, AI Overviews, and Claude to baseline visibility
- Build the category entity graph: features, use cases, integrations, alternatives, buyer personas
- Competitive entity analysis revealing where rivals hold semantic authority and where gaps exist
- Create modular, answer-ready content blocks that LLMs can extract and cite directly
- Deploy JSON-LD structured data (Product, Service, Organization, FAQ, HowTo) for machine readability
- Enforce cross-page entity consistency so AI systems encounter coherent definitions everywhere
- Build comparison blocks, evidence blocks, and FAQ pairs matching buyer query patterns
- Establish multi-platform presence: G2, Capterra, TrustRadius, Reddit, integration directories
- Structure case studies and customer results as citable proof blocks with specific outcomes
- Build authority through expert mentions, thought leadership, and industry participation
- Cultivate third-party validation in comparison articles and expert roundups
- Track LLM citation rates, description accuracy, and recommendation context across platforms
- Weekly prompt testing to identify new visibility gaps and optimization opportunities
- Pipeline attribution connecting GEO improvements to demo requests and influenced revenue
- Continuous block optimization based on which content AI systems extract most frequently
Most agencies have added "AI optimization" as a line item. We built our entire methodology around how LLMs retrieve, evaluate, and recommend products.
Jack Boutchard has built SaaS growth functions from Seed through Series C. Every engagement gets strategic depth from someone who understands PLG motions, B2B sales cycles, and how AI visibility ladders to pipeline.
Seven signals that determine which products LLMs cite: entity clarity, semantic authority, distributed proof, content structure, source diversity, recency, and cross-platform consistency. Every action maps to a signal.
GEO plugs into the Exalt Growth Operating System: a 12-module framework covering entity modeling, topical architecture, block factories, and proof systems. Not an isolated tactic but a compounding system.
Regular prompt testing across ChatGPT, Perplexity, Google AI Overviews, Claude, and Gemini. Platform-specific optimization because each AI system weights authority signals differently.
Clear connection between GEO improvements and business outcomes. We track leading indicators (citation rates, entity coverage) and lagging indicators (demos influenced, pipeline attributed) so you see the revenue impact.
We maintain a small, focused roster of SaaS clients. No account manager handoffs. No junior team members running strategy. Direct founder involvement on every engagement from entity mapping through optimization.
Four phases that take SaaS from fragmented SEO to a compounding organic growth engine. Each phase builds on the last, creating a system that accelerates over time.
Entity mapping, technical audit, competitive analysis, and knowledge graph assessment. We build a complete picture of where you stand and where the leverage points are. Weeks 1 to 4.
Information architecture redesign, schema deployment, content pillar mapping, and technical fixes. The structural work that determines whether everything else compounds or collapses. Weeks 4 to 8.
Content production, programmatic page builds, link authority development, and GEO optimization. Systematic execution against the roadmap with weekly reporting and sprint-based delivery. Months 2 to 6.
Performance analysis, content expansion, AI visibility optimization, and attribution refinement. The phase where organic becomes your most efficient pipeline channel. Month 6 onward.


Audit approach to SEO and isolates SEO from business challenges.
Top-down approach to SEO addressing core business challenges.
Can't forecast or ladder strategy up to revenue.
Clear forecast and reporting connecting strategy to revenue impact.
Implements same cookie cutter strategies in isolation from business.
Bespoke strategy tailored to your SaaS levers that integrates with GTM approach.
No knowledge of SaaS nuances, taking same approach to every client.
100% SaaS focused agency with highly contextual experience and expertise.
SaaS growth is a foreign language to these generic SEO agencies.
Understand how SEO fits into existing SaaS growth motions and models.
No personal guarantee, junior account manager developing your strategy.
Personal service guarantee, founder developed strategy.
Generative Engine Optimization delivers the strongest ROI for funded B2B SaaS companies where AI search directly influences how buyers discover and evaluate solutions.
✓ Prospects ask ChatGPT or Perplexity "What is the best [category] for [use case]?" and competitors get recommended instead of you
✓ AI systems describe your product with vague, inaccurate, or outdated language that misrepresents your actual value proposition
✓ You have strong case studies, customer results, and proof points but they are not structured for AI extraction and citation
✓ Your category has active buyer research happening in AI search based on query volume and community discussions
✓ You need durable brand authority that positions your product as the trusted choice, not just short-term traffic
✓ Your current SEO strategy produces rankings but you are losing ground as AI answers absorb clicks before they reach organic results
Company type: B2B SaaS ✓
Funding stage: Seed to Series C ✓
Product-market fit: Established ✓
Competitive landscape: Multiple alternatives exist ✓
Growth motion: PLG, sales-led, or hybrid ✓
Engagement model: Direct with founder ✓
Team involvement: 2-3 hrs/month ✓
Three engagement phases designed to match where your SaaS is in its organic growth journey. Every phase is built on our proprietary EGOS methodology.
Build the Fundamentals for AI-Era Visibility
Build the Engine & Prove Pipeline Impact
Category Leadership, Moats & ARR Attribution
Learn more about our approach to SaaS GEO
Covers entity modeling, content structuring, distributed proof systems, and default-answer positioning across ChatGPT/Perplexity/AI Overviews/Claude.
Entity graphs vs. keywords, block-readable content vs. page optimization, proof systems vs. link building, LLM citation tracking vs. rank monitoring.
60-90 days for improved citations/accuracy. 4-6 months for default answer positioning. Compounds over time.
ChatGPT, Perplexity, Google AI Overviews, Claude, and Gemini. Platform-specific testing since each weights signals differently.
Funded B2B SaaS from Seed through Series C with established PMF, validated use cases, and customer success stories.
Leading indicators (entity coverage, citation rates, accuracy scores) and lagging indicators (AI referral traffic, influenced demos, attributed pipeline).
Yes entity modeling + content structuring + proof systems correct most inaccuracies within 60-90 days.
No, complements it. Entity authority improves traditional rankings while driving AI visibility. Most clients run both.
Exalt Growth's 7-signal framework: entity clarity, semantic authority, distributed proof, content structure, source diversity, recency, cross-platform consistency.
2-3 hours/month from a PMM or product lead for strategic input. Technical implementation handled by Exalt Growth.