Case Study: B2B Company Captures AI-Driven Leads

The Company: NexaTech Solutions

NexaTech Solutions is a cybersecurity company offering endpoint detection and response (EDR) solutions for mid-market companies with 100-500 employees. Founded in 2019 with 85 employees and $18M ARR, NexaTech competed against well-known enterprise vendors (CrowdStrike, SentinelOne) and smaller niche players.

Their product was strong -- 4.6/5 on G2 with 280+ reviews -- but brand awareness lagged behind larger competitors. Traditional marketing channels were expensive and dominated by bigger budgets.

The Challenge: Competing Against Enterprise Giants

NexaTech's core problem: when enterprise buyers researched cybersecurity solutions, AI assistants consistently recommended CrowdStrike, SentinelOne, and Palo Alto Networks. NexaTech was never mentioned, despite having a competitive product for the mid-market segment.

We audited NexaTech's AI visibility across 20 relevant queries:

• "Best endpoint security for mid-size companies" -- NexaTech absent
• "EDR solutions comparison" -- NexaTech absent
• "Cybersecurity platforms for companies under 500 employees" -- NexaTech absent

Initial AI Score: 22/100.

The root causes: thin product pages with marketing language instead of technical detail, no comparison content, no structured data beyond basic website schema, and thought leadership content locked behind gated forms that AI crawlers could not access.

For B2B AI visibility fundamentals, see our B2B companies AI visibility guide.

The AI SEO Strategy

The strategy centered on a core insight: B2B buyers ask AI for comparisons and recommendations, not product descriptions. NexaTech needed to create the comparison and framework content that AI models cite.

For the full approach to building an AI SEO strategy, see our AI SEO strategy from scratch guide.

Three content pillars

1. Comparison content -- Objective comparisons including NexaTech and competitors
2. Framework and methodology content -- Proprietary cybersecurity frameworks positioned as industry resources
3. Buyer's guide content -- Decision-making guides for the mid-market cybersecurity buyer

The ungating decision

NexaTech made a critical strategic decision: ungating their best content. Previously, whitepapers and guides required email capture to access. This meant AI crawlers could not read the content, making it impossible to cite. They moved their highest-value content to publicly accessible pages with optional email capture for additional resources.

Content That AI Models Cited

The comparison article (highest performer)

"Endpoint Security Comparison: 7 Platforms for Mid-Market Companies (2026)" compared NexaTech alongside 6 competitors using standardized criteria: price per endpoint, detection rates, deployment complexity, support quality, and mid-market fit. The article included an HTML comparison table with structured data.

This article was cited in 34% of relevant AI queries within 6 weeks of publication. Key success factors: objective tone (acknowledging competitor strengths), structured comparison table, and specific mid-market positioning that matched how buyers actually query AI.

The proprietary framework

"The 5-Layer Cybersecurity Framework for Companies Under 500 Employees" presented NexaTech's approach to mid-market security as an educational resource. The framework defined five layers (Endpoint, Network, Identity, Data, Response) with specific guidance for each.

AI models began citing this framework as an authoritative reference for cybersecurity planning queries. The article generated 18% of total AI-referred traffic.

Buyer's guide content

"How to Choose an EDR Solution: Mid-Market Buyer's Guide" walked through the evaluation process with specific criteria, questions to ask vendors, and red flags to watch for. This content captured early-stage buyer queries.

Expert-authored technical content

NexaTech's CTO published three technical articles on specific threat detection methodologies. These expert-attributed pieces built E-E-A-T signals. See our E-E-A-T guide for why author attribution matters. For writing guidance, see writing for AI citation.

Technical Implementation

Schema markup

• SoftwareApplication schema on the product page with features, pricing model, and operating system compatibility
• Organization schema with founding date, employee count, and specialization
• Article schema on all content with author attribution to named experts
• AggregateRating schema pulling from G2 review data
• FAQPage schema on product and comparison pages

AI crawler access

Robots.txt updated to allow AI search crawlers. llms.txt file created describing NexaTech's products, content library, and target audience.

Content structure

All articles followed the BLUF format with key findings in the first two paragraphs, comparison tables in semantic HTML, and FAQ sections at the bottom of every article.

Results and ROI

After 4 months:

| Metric | Before | After | Change | |---|---|---|---| | AI Score | 22/100 | 71/100 | +223% | | AI mentions (20-query test) | 0/20 | 13/20 | From invisible to cited in 65% | | AI-referred leads/month | 0 | ~12 | New channel | | AI lead conversion to demo | N/A | 31.9% | vs 7.8% organic | | Average deal size (AI leads) | N/A | $104,000 | 23% larger than other channels | | Pipeline from AI leads | $0 | $4.1M | 4-month cumulative | | Total investment | -- | $24,000 | Content + technical + monitoring |

The conversion rate differential was remarkable. AI-referred leads requested demos at 31.9% vs 7.8% for organic search. NexaTech's sales team reported that these leads often said "ChatGPT recommended you" or "I read your comparison on Perplexity" -- arriving with pre-established trust.

Lessons for B2B Companies

1. Ungate your best content

The single most impactful strategic decision was making comparison and framework content publicly accessible. Gated content is invisible to AI. The leads lost from ungating were more than compensated by AI-driven discovery.

2. Include competitors in your comparisons

Objective comparisons that include competitors build the credibility AI models need to cite your content. Self-promotional comparisons are ignored.

3. Create proprietary frameworks

A named, structured framework becomes a reusable reference that AI models cite repeatedly. This is compound-interest content -- it generates citations long after publication.

4. Track AI leads separately

Standard analytics miss AI-influenced leads. Implement multi-touch attribution that captures AI as a channel, including form fields that let leads self-report AI discovery.

5. Expert attribution matters

AI models give more weight to content attributed to named experts with verifiable credentials. Anonymous "brand" content underperforms in AI citation rates.