Deepak Gupta had a front-row seat to the death of traditional digital marketing. As founder of LoginRadius, a customer identity platform he scaled to 1 billion users, he watched his SEO strategies deliver diminishing returns even as traffic from AI assistants quietly exploded.
“We were ranking #1 for our core keywords, publishing technical content, investing heavily in SEO,” Gupta recalls. “But our best inbound leads were increasingly mentioning they’d researched authentication solutions through ChatGPT or asked Claude to compare platforms. When we started tracking it systematically, we discovered a terrifying pattern: our most sophisticated prospects were using AI for research, and we were completely invisible in those recommendations.”
This wasn’t a future trend. It was happening in real-time, with measurable business impact. And it was invisible to every traditional analytics tool.
That gap led Gupta to partner with AI expert Govind Kumar to build GrackerAI—and in the process, define an entirely new category called Generative Engine Optimization (GEO).
The Pattern Recognition That Sparked a Category
Gupta’s entrepreneurial journey began in cybersecurity and authentication, building LoginRadius into a CIAM (Customer Identity and Access Management) platform serving major enterprises worldwide. Along the way, he became intimately familiar with how security buyers research solutions—and how that process was rapidly changing.
“Security decision-makers are analytical by nature,” Gupta explains. “They want to compare specific features, understand architectural tradeoffs, evaluate integration complexity. That research process used to involve Google searches, whitepapers, analyst reports, and vendor conversations over several weeks.”
But starting in late 2023, something shifted. CTOs and CISOs began completing that entire research process in hours, not weeks—using AI assistants to synthesize information, compare solutions, and generate technical evaluations. The research quality was often superior to what they’d compile manually, because AI could analyze hundreds of sources simultaneously.
“I watched enterprise security leaders walk into sales calls with deeper technical understanding than we’d typically see after multiple discovery sessions,” Gupta notes. “They’d already had ChatGPT explain OAuth flows, compare MFA approaches, and analyze our platform’s architecture against competitors. The AI had essentially pre-qualified them.”
This created both an opportunity and a crisis. Companies that AI assistants recognized as authorities captured a disproportionate share of these high-intent buyers. Companies invisible in AI recommendations were systematically excluded from consideration—regardless of their Google rankings or marketing investments.
The Technical Challenge That Became a Business
Understanding the problem and building the solution are entirely different challenges. Gupta needed someone who could architect systems to measure AI citation patterns, optimize content for AI comprehension, and automate content generation at scale while maintaining technical accuracy.
That’s where Govind Kumar entered the picture. An AI and machine learning expert, Kumar had spent years working on recommendation systems, natural language processing, and algorithmic content optimization. But he’d become increasingly frustrated by the disconnect between how marketers approached AI and how AI systems actually worked.
“Everyone was talking about ‘AI marketing’ as if it meant using ChatGPT to write blog posts,” Kumar explains. “But that completely misses the point. The real opportunity—and challenge—is understanding how AI engines evaluate source authority, synthesize information, and make recommendations. You need to optimize for AI comprehension and citation patterns, not keyword density.”
Kumar and Gupta spent months reverse-engineering how AI platforms decide which sources to cite. They analyzed thousands of queries across ChatGPT, Claude, Perplexity, and Google Gemini, identifying the patterns that determine which companies appear in AI-generated recommendations.
What they discovered fundamentally challenged conventional SEO wisdom. AI citation patterns weren’t primarily driven by backlinks, keyword optimization, or traditional ranking signals. Instead, AI engines evaluated content depth and technical accuracy, integration with authoritative data sources, consistency of information across multiple pages, structured data formats that support comprehension, and expertise, experience, authoritativeness, and trustworthiness (E-E-A-T) signals.
“This required building entirely new optimization infrastructure,” Kumar notes. “We needed systems that could generate technically accurate content at scale, integrate with authoritative data sources, structure information for AI comprehension, and measure citation patterns across multiple AI platforms simultaneously. There was no existing technology stack for this.”
Building a Category, Not Just a Product
GrackerAI’s approach reflects the founders’ shared belief that they’re not just building a product—they’re establishing a new category that will become as fundamental to digital marketing as SEO became two decades ago.
“When Google became dominant, companies scrambled to hire SEO specialists, invest in content, and optimize for search,” Gupta explains. “But the companies that won biggest weren’t those who adopted SEO fastest. They were the ones who understood it was a foundational shift requiring systematic, ongoing investment. We’re at that exact moment now for GEO.”
The platform they’ve built combines measurement and improvement in ways that reflect lessons from LoginRadius’s scale. It tracks brand mentions and citation frequency across all major AI platforms, provides competitive intelligence showing where competitors are cited and you’re not, identifies content gaps and optimization opportunities, and automatically generates content optimized for AI citation patterns while maintaining technical accuracy through integration with 200+ authoritative data sources.
For cybersecurity and B2B SaaS companies—GrackerAI’s initial focus—this technical accuracy requirement is non-negotiable. Security professionals immediately identify AI-generated content that lacks technical depth or makes factual errors. The platform must balance optimization for AI citation with credibility for human experts.
“That’s why we integrate with sources like the National Vulnerability Database, MITRE CVE Database, and industry intelligence feeds,” Kumar explains. “We’re not just generating content for AI comprehension. We’re establishing genuine technical authority that human experts validate and AI engines recognize as citable.”
The Strategic Partnerships That Signal Category Validation
Perhaps the strongest validation of GrackerAI’s category thesis comes from the strategic partnerships the founders have secured. NVIDIA’s Startups Program provides advanced GPU infrastructure for AI model training. Microsoft for Startups offers integration with Copilot and Azure AI services. Cloudflare hosts the platform infrastructure. And critically, GrackerAI maintains relationships with OpenAI, Anthropic (makers of Claude), and other AI platform providers.
“These aren’t typical vendor relationships,” Gupta emphasizes. “We’re partnering with the companies building the AI platforms we optimize for. That gives us architectural insights into how these systems evolve, what they prioritize for citations, and where the category is heading.”
These partnerships position GrackerAI not just as a SaaS vendor, but as infrastructure for the AI search ecosystem. As AI platforms scale to billions of users, GrackerAI’s measurement and optimization capabilities become increasingly valuable—both for companies seeking visibility and for AI platforms wanting to surface authoritative sources.
Early Results Validate the Thesis
While GrackerAI officially launched the GEO category in early 2026, early adopters have been testing the platform for months with measurable impact. According to company data:
- Clients achieve 60% average increase in AI visibility scores within 90 days
- 40-80% growth in AI-referred signups and conversions
- 100-200% increases in organic traffic from quality, optimized content
- 3-5x higher conversion rates from AI search visitors versus traditional organic
- 18% conversion rates from programmatic portals versus 0.5% from traditional blog content
“What surprised us was the velocity,” Kumar notes. “Once AI engines start citing you as an authority, it compounds. You get recommended repeatedly, which reinforces authority, which increases future citations. It’s a flywheel that accelerates over time.”
For Gupta, these results validate a crucial insight: companies that establish AI visibility now will benefit from compounding advantages as these platforms scale. Unlike traditional SEO, where rankings shift constantly, AI citation patterns have longer-term persistence.
The Vision: Making AI Search Measurable and Manageable
Both founders are clear about where they’re heading. GrackerAI isn’t just addressing a current gap in marketing technology—it’s building infrastructure for a decade-long shift in how buyers discover and evaluate products.
“In 2027, the companies thriving won’t be those with the best Google rankings,” Gupta predicts. “They’ll be the brands AI assistants recommend when millions of buyers ask for solutions. We’re building the platform that makes that measurable and achievable.”
Kumar adds a technical perspective: “As AI platforms evolve, optimization will become increasingly sophisticated. Companies will need to understand not just whether they’re cited, but why—and how to systematically improve citation quality, context, and frequency. That requires purpose-built infrastructure we’re developing now.”
The platform roadmap includes advanced AI models trained on company-specific data to capture unique positioning, predictive analytics showing citation probability for new content before publishing, integration with additional authoritative data sources across industries, API access enabling automated workflows and custom integrations, and white-label options for agencies serving multiple clients.
Accessibility and Implementation
Despite the technical sophistication, GrackerAI designed the platform for practical use by marketing teams. Companies can start with a free tier that includes AI visibility analysis and up to 50 optimized pages. Paid plans scale from growth to enterprise tiers with advanced capabilities.
The system integrates with existing marketing infrastructure, including WordPress, Webflow, Ghost, and custom CMS platforms. Most companies see initial visibility improvements within 4-6 weeks, with significant citation increases by month three.
“We wanted to remove barriers to adoption,” Gupta explains. “Marketing teams shouldn’t need AI expertise to measure and improve their visibility. The platform handles the technical complexity while giving teams strategic control over positioning and messaging.”
For companies ready to understand their current AI search positioning, GrackerAI provides comprehensive resources:
- The Complete Guide to AEO and GEO for B2B SaaS Companies
- How Companies Can Achieve AEO and GEO: The Complete 2025 Guide
Companies can analyze their current visibility at portal.gracker.ai.
The Category They’re Building
As they reflect on the journey from recognizing a pattern to building a category, both founders emphasize they’re still in early stages. The shift from traditional search to AI-powered discovery is accelerating faster than even they anticipated.
“We’re not building a feature or even a product,” Gupta concludes. “We’re building the infrastructure for a fundamental shift in digital marketing. Twenty years ago, companies that ignored SEO got left behind. Ten years from now, companies that ignored GEO will face the same outcome. We’re giving them the tools to adapt now, while they still have time to capture the early mover advantage.”
For Kumar, the opportunity is both technical and philosophical. “We’re helping companies adapt to a world where AI intermediates most information discovery. That’s not a trend—it’s the new reality. And companies that understand this reality earliest will win disproportionately.”
About GrackerAI
GrackerAI is a San Francisco-based B2B SaaS company pioneering the Generative Engine Optimization (GEO) category. Founded by Deepak Gupta (LoginRadius founder) and Govind Kumar (AI/ML expert), the company helps B2B SaaS and cybersecurity firms measure and improve their visibility in AI-powered search engines. Backed by NVIDIA, Microsoft, Cloudflare, OpenAI, Anthropic, and Amazon AWS.
Website: gracker.ai
Platform: portal.gracker.ai
Press Contact: media@gracker.ai
