Your Buyers Research in ChatGPT Before They Ever Reach Your Site. If You're Not in the Answer, You're Losing Deals You Never Knew Existed.

AI recommendation engines use a combination of structured data (schema markup), entity authority (how well-verified and cross-referenced your brand is across authoritative sources), content structure (whether your answers are extractable and citable), and crawl access (whether your site is even readable). Most SaaS companies fail at least the first two. A product with weaker features but better infrastructure will consistently outrank a better product with poor AI infrastructure. That's the gap we close.

No — it means your attribution is broken, not that AI isn't affecting pipeline. When a buyer researches in ChatGPT and then navigates directly to your site, that session shows up as "direct" in GA4. Most SaaS companies are currently losing significant pipeline to AI-recommended competitors without any visibility into it. One of the deliverables in Phase 9 is configuring GA4's AI channel grouping and UTM structure specifically to surface this traffic. Once configured, most clients find AI-referred traffic was already significant — it was just invisible.

Content marketing and SEO are optimised for human clicks on search results. AI citation infrastructure is built for machine trust — a completely different set of signals. A well-ranking blog post may never be cited by ChatGPT. A properly structured FAQ pair with the right schema and entity backing will be cited consistently. The two disciplines don't conflict — but they don't overlap. Your existing SEO investment doesn't protect your AI visibility, and your AI visibility work doesn't interfere with your SEO performance.

We run 50+ targeted prompts across all 5 AI platforms — covering pricing, features, integrations, use cases, and competitive comparisons. Any false or outdated claim we find is documented: the exact prompt, the platform, and the incorrect claim. We then build structured correction content — pricing accuracy pages, integration documentation with correct schema, and FAQ pairs that provide the accurate answer — and update the schema so the next crawl replaces the hallucination with verified data. Before and after evidence is included in the delivery documentation.

This is exactly what Phase 10 (the monthly compounding system) addresses. Every month, we update schema for new features and pricing changes, run a freshness pass on FAQ content, and re-run the 30-prompt SoLLM test to catch any new hallucinations that have appeared. Fast-moving SaaS products are actually the best candidates for ongoing monthly work — because the gap between what AI knows and what your product does can grow quickly without maintenance. The infrastructure we build in 24 days is the foundation; Phase 10 keeps it accurate as your product evolves.

This is a two-part problem: direct competitor mentions in queries about your brand, and competitor citations in category queries where you should appear. For brand queries, Phase 8 (hallucination correction) addresses misleading comparisons. For category queries, Phase 7 (prompt ownership) builds dedicated pages for the specific buyer prompts you need to own — each one structured and optimised to make your product the cited answer rather than a competitor. We also run a competitor gap audit monthly to track when a rival closes ground on specific prompts, so you can respond before it affects pipeline.

Yes. Multi-vertical SaaS products are common in our engagements. The schema architecture allows for multiple use-case and audience dimensions within a single entity. The FAQ content is segmented by buyer persona and vertical — so an HR Director and a CFO asking different questions about the same product each get accurate, relevant, directly cited answers. The prompt ownership work maps buyer queries by vertical, ensuring you appear in the right conversation for each audience segment. A brief that includes your primary verticals and buyer personas is part of the onboarding process.

Verification is built into the structure — not provided as a summary we wrote ourselves. At Day 6, you verify Phase 1 and 2 results live in your own browser using Google's Rich Results Test. The before-and-after screenshots are generated by Google's own tool, not by us. At Day 24, the SoLLM Baseline Report shows your citation rate across 30 buyer prompts on all 5 platforms — before and after. The hallucination audit includes the exact prompts run, the AI responses received, and the corrections made. Every deliverable is documented with evidence you can verify independently.

Frame it as infrastructure, not marketing — in the same budget category as SOC 2, GDPR compliance, or CDN infrastructure. The business case is straightforward: buyers are researching in AI before they reach your site, AI may be misrepresenting your product to those buyers, and the revenue leak has no attribution so it's invisible in your CRM. The free audit gives you a concrete before-state to present: "We appear in X/30 buyer prompts. Our main competitor appears in Y/30." That number, and the revenue implication of closing the gap, is the business case. We can also provide supporting data on AI research behaviour in B2B SaaS buying cycles if that's useful.