Outcomes measured in Pipeline

Challenge

Completely absent from buying questions

When buyers asked ChatGPT for "HR systems for mid-sized companies," the answer list held nothing but competitors. The audit's starting point was brutal: in evaluation-stage target questions, the brand's citation rate was effectively zero: the product was strong, but AI couldn't see it and didn't yet trust it.

Method

Audit → content engine → knowledge graph

We didn't start by writing content. We started by quantifying the gaps, then sequenced the attack by Pipeline impact.

• 30-day GEO audit: pinpoint citation gaps across six engines and reverse-engineer the structural reasons competitors get cited
• GEO content engine: rebuild citable content for the "choosing an HR system" scenario: definition blocks, comparison tables, original data
• Knowledge-graph build: use entities and structured data to lock the brand firmly to the "HR systems" category in the AI engines' minds

Results

40+ inbound demos a month

Within 6 months, AI-engine citations grew 312%, making the brand a fixture in ChatGPT and Perplexity answers for "HR system recommendations." The source field on demo forms started reading "ChatGPT recommended you": for the first time, Pipeline could be attributed directly to AI search.

"For the first time, a demo form's source field read 'ChatGPT recommended you.'"

Challenge

Competitors monopolized the comparison questions

High-intent questions like "A vs. B" and "best marketing automation tool" were almost entirely owned by two competitors in the AI answers. At launch, answer share was just 11%: at the exact moment buyers decided, the brand wasn't even in the conversation.

Method

320 questions targeted + a 3-week rewrite loop

Comparison questions are trench warfare, won one position at a time, so we reclaimed each one on a fixed cadence.

• Targeted 320 high-intent questions, ranked by Pipeline impact rather than search volume
• Weekly citation tracking quantified the share shift on every question; gaps auto-queued for rewriting
• 3-week rewrite loop: diagnose → rewrite → verify, iterating each piece until it got cited

Results

SQL cost cut in half

Within 6 months, answer share climbed from 11% to 58%, reclaiming the primary recommendation slot on high-intent questions one by one. AI-referred buyers arrived with most of their research already done, cutting SQL cost in half: the same marketing budget, twice the qualified pipeline.

"For the first time, I had a report that told the CEO: these SQLs came from AI search."

Challenge

Traffic anxiety

Organic traffic was sliding quarter over quarter, and the team's first instinct was to "win the traffic back." But the audit showed the real problem wasn't volume: it was that the brand never appeared in AI's shopping-recommendation conversations at all.

Insight

AI-referred traffic is small, but the trust is already there

When buyers ask AI for product recommendations, they don't get ten blue links: they get a short list that's already been filtered and compared. A brand that makes that list earns trust close to a friend's referral, so we shifted the focus from "winning back clicks" to "owning the recommendation slot."

Results

A quality-over-quantity view of GEO traffic

Over a 90-day window, AI-engine traffic was a fraction of Organic by volume, yet it converted at 4.7×. This case changed how the team measures traffic: rather than chasing declining clicks, own the recommendation slot inside AI answers, because every visitor who arrives already trusts you.

"Customers from AI don't ask 'who are you?': they ask 'how do I order?'"