Artificial Intelligence
Explore practical insights, research, and benchmarks on artificial intelligence, including generative AI, large language models, RAG, governance frameworks, MLOps practices, and AI hardware. Gain an understanding of key tools, implementation strategies, and enterprise use cases shaping the AI landscape.
Explore Artificial Intelligence
Vector Database Sizing and Selection Calculator
The practical question behind a self-hosted vector database for RAG is which engine fits a given server, and which one the workload rules out. The calculator below answers both, from our benchmark of seven self-hosted vector databases run at matched recall on identical embeddings. Five checkboxes at the top of the calculator name five common…
Enterprise AI Companies: Landscape Breakdown in 2026
Artificial intelligence is revolutionizing every industry with various use cases. Demand for AI products grows as more companies shift their legacy systems to digital products to survive in the competitive business landscape. However, the AI vendor landscape is crowded, and most executives or decision-makers have limited knowledge of the AI landscape. Check out our comprehensive…
Top 20 Supply Chain AI Tools with Examples
From demand forecasting and inventory optimization to last-mile delivery and supplier negotiations, AI enables supply chain companies to process complex data, respond to disruptions more quickly, and make more informed decisions across global networks. Discover the top 20 supply chain AI tools and learn how they utilize AI to address real-world challenges and enhance performance…
Top 12 SEO AI Use Cases with Case Studies
As algorithms change and consumer expectations rise, it has become more challenging to compete for accessibility in search results. Conventional SEO techniques, which depend on manual research and minor updates, frequently fall behind these developments. AI-powered SEO tools address this challenge by automating complex tasks and aligning content more precisely with user intent. Explore the…
AI Hallucination Detection Tools: W&B Weave & Comet
We benchmarked three hallucination detection tools: Weights & Biases (W&B) Weave HallucinationFree Scorer, Arize Phoenix HallucinationEvaluator, and Comet Opik Hallucination Metric, across 100 test cases. Each tool was evaluated on accuracy, precision, recall, and latency. We tested 100 responses (50 correct, 50 hallucinated) from factual Q&A scenarios against their source context. See the benchmark methodology.…
Best RAG Tools, Frameworks, and Libraries
RAG improves LLM responses by grounding them in external data instead of just what the model memorized in training. We benchmarked the components a RAG system is built from and gathered the results in one place, with a practical guide to choosing each part of the stack. See our benchmark results for each RAG component,…
Vector Database Benchmark: 7 Open-Source Engines for RAG
We benchmarked seven open-source, self-hosted vector databases as the retrieval layer of a RAG pipeline, each run one at a time on identical bge-m3 embeddings and real medical and technical queries, so the database index was the sole variable. The workload spanned MedRAG-50k, TechQA-28k, and a 2.25M-vector corpus across eight dimensions, from accuracy and retrieval…
GPU Concurrency Benchmark: H100 vs H200 vs B200 vs MI300X
I have spent the last 20 years focusing on system-level computational performance optimization. We benchmarked the latest NVIDIA GPUs, including the NVIDIA’s H100, H200, and B200, and AMD’s MI300X, for concurrency scaling analysis. Using the vLLM framework with the gpt-oss-20b model, we tested how these GPUs handle concurrent requests, from 1 to 512. By measuring…
Text-to-SQL: Comparison of LLM Accuracy
I have relied on SQL for data analysis for 18 years, beginning in my days as a consultant. Translating natural-language questions into SQL makes data more accessible, allowing anyone, even those without technical skills, to work directly with databases. We used our text-to-SQL benchmark methodology on 35+ large language models (LLMs) to assess their performance…
Top 20+ Agentic RAG Frameworks
Agentic RAG enhances traditional RAG by boosting LLM performance and enabling greater specialization. We conducted a benchmark to assess its performance on routing between multiple databases and generating queries. Explore agentic RAG frameworks and libraries, key differences from standard RAG, benefits, and challenges to unlock their full potential. We used our agentic RAG benchmark methodology…