Academic papers and technical research exploring AI infrastructure patterns, emerging technologies, and novel approaches to enterprise AI systems.
A novel synthesis of MAP-Elites quality-diversity optimization, Red Queen adversarial co-evolution, and Ralph Wiggum persistent iteration for automated PCB layout optimization using Large Language Models as first-class optimization operators without neural network training.
Production-deployed autonomous agent platform implementing goal-directed execution with self-reflection across 44 integrated microservices. Key innovations include a 10-phase autonomous execution loop, a Living Library service catalog with 6-factor performance scoring, and checkpoint-based recovery.
A comprehensive technical analysis examining AI and knowledge graph technologies for voting rights litigation, presenting an integrated platform architecture with 20 use cases mapped to active cases across 81 active democracy litigation matters.
An academic research paper exploring the integration of high-performance computing infrastructure, AI-driven electronic design automation, and geospatial intelligence systems. This paper analyzes deployment strategies across global HPC data centers, marketplace plugin ecosystems, and progressive knowledge systems for enterprise AI platforms.
How air-gapped, self-hosted AI systems enable intelligence agencies and militaries to leverage frontier AI models without exposing sensitive data to adversaries. Examines five critical vulnerabilities of foreign AI dependence.
How air-gapped, self-hosted AI systems enable intelligence agencies and militaries to leverage frontier AI models without exposing sensitive data to adversaries. Examines five critical vulnerabilities of foreign AI dependence.
As AI models require vast training data, enterprises face critical decisions about data residency, cross-border transfers, and vendor lock-in. Self-hosted solutions provide control without sacrificing capability.
As AI models require vast training data, enterprises face critical decisions about data residency, cross-border transfers, and vendor lock-in. Self-hosted solutions provide control without sacrificing capability.
A production-ready framework implementing query enhancement, adaptive routing, RAG Triad evaluation, and self-correction loops as a wrapper service over existing RAG infrastructure. Based on published research including HyDE, Self-RAG, and RAGAS, with projected 30-50% retrieval quality improvements.
A production-ready framework implementing query enhancement, adaptive routing, RAG Triad evaluation, and self-correction loops as a wrapper service over existing RAG infrastructure. Based on published research including HyDE, Self-RAG, and RAGAS, with projected 30-50% retrieval quality improvements.
Three-tier document processing cascade combining rule-based extraction, ML-based detection, and LLM refinement achieves 97.9% accuracy on complex financial tables, outperforming single-model approaches by 12.4%.
AI-powered geospatial intelligence operating system combining H3 hexagonal spatial indexing, spatial-temporal knowledge graphs, and multi-service orchestration achieving 14x faster spatial queries, 119% accuracy improvements, and 80-90% cost reductions versus traditional GIS
Smart document processing with 3-tier cascade architecture.
Multi-agent AI systems for R&D velocity improvement.
Hexagonal spatial AI for real-time geospatial intelligence.
Solving knowledge fragmentation with triple-layer systems.
Multi-agent AI system revolutionizing research workflows through orchestrated collaboration, achieving 68% time savings and 3x research velocity improvement
First composable AI-native CRM platform achieving 80% code reuse, 86% cost reduction, and breakthrough capabilities including multi-agent orchestration with 320+ LLM models and triple-layer knowledge architecture
Formal framework for multi-agent AI systems supporting competitive (best-of-N), collaborative (ensemble), and hybrid modes. Achieves 31% accuracy improvement through agent consensus on complex reasoning tasks.
Formal framework for multi-agent AI systems supporting competitive (best-of-N), collaborative (ensemble), and hybrid modes. Achieves 31% accuracy improvement through agent consensus on complex reasoning tasks.
Technical architecture for integrating Uber's H3 hexagonal indexing system with large language models, enabling natural language geospatial queries with 47ms average response time and sub-meter precision.
Novel triple-layer RAG architecture combining vector embeddings, knowledge graphs, and episodic memory achieving 23.7% accuracy improvement over baseline RAG and 15.2% over state-of-the-art on multi-hop reasoning tasks.
AI-powered geospatial intelligence operating system combining H3 hexagonal spatial indexing, spatial-temporal knowledge graphs, and multi-service orchestration achieving 14x faster spatial queries, 119% accuracy improvements, and 80-90% cost reductions versus traditional GIS
Triple-layer architecture combining semantic search, graph reasoning, and episodic memory achieving sub-100ms latency across 10M+ documents with 94.2% retrieval accuracy
Research on autonomous multi-domain threat hunting using multi-agent systems, achieving 99.7% faster detection (45 seconds vs 4.2 hours), 94% false positive reduction (6% final rate), and 82% threat prediction accuracy
Get started with our AI infrastructure platform and transform your enterprise operations.
