Pioneering
AI Search Innovation
Since 2023
We're the AI search experts leading the industry in Google AI Mode and AI Overview optimization. Our team analyzes Google's patents, builds cutting-edge AI search tools, and speaks at industry conferences about the future of AI-powered search optimization.
Our Mission
To democratize AI search optimization by making cutting-edge research accessible to businesses of all sizes, helping them thrive in the age of AI-powered search.
"Our mission is to democratize AI search optimization by making cutting-edge research accessible to businesses of all sizes."
Our Core Values & Mission
We're guided by principles that ensure every client receives cutting-edge, research-backed AI search optimization strategies.
Our Mission
To democratize AI search optimization by making cutting-edge research accessible to businesses of all sizes, helping them thrive in the age of AI-powered search.
Our Vision
To be the leading authority in AI search optimization, pioneering new methodologies and tools that shape the future of how businesses connect with their audiences through AI-powered search.
Our Culture in Action
These values aren't just words on a wall—they guide every decision we make and every interaction we have.
Continuous Learning
Weekly research sessions and patent analysis
Client Partnership
Collaborative approach to every project
Results Driven
Measurable outcomes in every engagement
Meet Our Expert Team
Our team combines deep technical expertise with practical experience in AI search optimization, bringing together researchers, engineers, and strategists who are shaping the future of search.
Alex Smith
CEO & AI Search Strategist
Former Google Search engineer with 10+ years experience in search algorithms and AI systems. Leading the development of patent-backed relevance engineering methodologies.
Expertise
Key Achievements
- Led development of 5 proprietary AI search tools
- Analyzed 50+ Google AI search patents
- Speaker at 10+ industry conferences
Dr. Maria Johnson
Head of Research
PhD in Machine Learning, leading our proprietary research on AI search ranking factors. Published researcher in vector embeddings and dense retrieval systems.
Expertise
Key Achievements
- Published 15+ papers on vector search
- PhD in Machine Learning from Stanford
- Former researcher at OpenAI
David Chen
Technical Director
Expert in content optimization and technical SEO for AI-powered search systems. Specializes in passage-level engineering and query fan-out analysis.
Expertise
Key Achievements
- Optimized 1000+ pages for AI search
- Developed passage-level optimization framework
- Expert in Google's query fan-out systems
Sarah Kim
AI Analytics Lead
Data scientist focused on AI search performance metrics and reasoning chain analysis. Expert in building custom analytics platforms for AI visibility tracking.
Expertise
Key Achievements
- Built 3 custom analytics platforms
- Tracked AI search performance for 500+ clients
- Expert in reasoning chain analysis
Robert Park
Content Engineering Manager
Specialist in vector space optimization and semantic content engineering. Leads our content transformation initiatives for AI search compatibility.
Expertise
Key Achievements
- Transformed 2000+ pieces of content for AI search
- Developed semantic content engineering methodology
- Expert in vector space optimization
Our Journey in AI Search
From the early days of AI search to becoming industry leaders in relevance engineering, here's how we've evolved with the technology.
The Future of AI Search
We're just getting started. As AI search continues to evolve, we're committed to staying at the forefront of innovation, developing new tools and methodologies to help businesses succeed in the age of artificial intelligence.
Relevance Engineering Methodology
Our systematic approach is based on the four strategic pillars for AI search success, derived from extensive patent research and technical analysis.
Fit the Reasoning Target
Engineer semantically complete passages that win LLM pairwise ranking and support reasoning chains.
Be Fan-Out Compatible
Create entity-rich content aligned with synthetic query expansion and Knowledge Graph mapping.
Be Citation-Worthy
Develop factual, attributable content with high confidence extraction for AI citations.
Be Composition-Friendly
Structure content in modular, scannable formats optimized for synthesis and generation.
Technical Implementation Process
Our implementation follows a rigorous technical process based on understanding how AI Mode's multi-phase system actually works.
Vector Space Analysis
Analyze your content's semantic similarity and embedding alignment using cosine similarity calculations.
Query Fan-Out Mapping
Use our Qforia tool to identify synthetic query landscapes and reasoning chain opportunities.
Passage Engineering
Optimize content at the passage level for LLM pairwise ranking and citation likelihood.
Entity Graph Optimization
Align content with Knowledge Graph entities for improved fan-out compatibility.
Reasoning Chain Tracking
Monitor performance across probabilistic ranking systems and user embedding variations.
Continuous Optimization
Iterative refinement based on AI search performance metrics and emerging patent insights.
What Industry Leaders Say
Hear from companies who have successfully implemented our relevance engineering strategies.
"AI Mode Boost's patent-backed approach transformed our search visibility. We saw a 300% increase in AI Overview appearances within 60 days."
Jennifer Martinez
VP of Digital Marketing
TechFlow Solutions
"Their deep understanding of Google's AI search patents gave us a competitive edge. The technical expertise is unmatched in the industry."
Michael Chen
SEO Director
InnovateNow Corp
"Finally, an agency that understands the technical realities of AI search. Their relevance engineering methodology actually works."
Sarah Thompson
Chief Marketing Officer
DataDriven Inc
Ready to Pioneer AI Search Success?
Join the companies already benefiting from our patent-backed relevance engineering strategies. Let's discuss how we can transform your AI search visibility.