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
- Document AutomationInsightAug 20
State of OCR technology: Is it dead or a solved problem?
Optical Character Recognition (OCR) is one of the earliest areas of artificial intelligence research. Today, OCR technology is relatively mature and no longer called AI, which is a good example of Pulitzer Prize winner Douglas Hofstadter’s quote: AI is whatever hasn’t been done yet. In our OCR benchmark, DeltOCR, a large language model, correctly reads…
- LLMInsightAug 19
ChatGPT for Customer Service: Top 10 Use Cases
ChatGPT has moved from novelty to infrastructure in customer service. Companies are using it to cut response times, handle volume their teams can’t absorb, and reduce the cost of routine interactions. But results vary sharply depending on how it’s implemented. OpenAI launched GPT-5.6, a materially more capable model that is better at instruction-following, reasoning across…
- AIInsightAug 17
800+ Leading AI Benchmarks
We curated a list with over 800 AI benchmarks for LLMs, GPUs, cloud GPUs, AI agents, tabular AI, and cybersecurity that are not yet saturated. Note that most of the May–June peak corresponds to the period during which we carried out our research. Benchmarks that update continuously are dated to the last time we verified…
- LLMBenchmarkAug 16
Intelligence Density of 71 LLMs for Smarter & Denser Models
We tracked 71 LLMs released between February 2023 and May 2026 and collected 10 public benchmarks to measure intelligence density. We divided the capability score by the resource the model consumes (active parameters, training compute, and inference price). To calculate intelligence density, we executed the following steps: See methodology for the scoring approach, and per-resource…
- RAGBenchmarkAug 14
Multimodal Embedding Models: Apple vs Meta vs OpenAI
Multimodal embedding models excel at identifying objects but struggle with relationships. Current models struggle to distinguish “phone on a map” from “map on a phone.” We benchmarked 7 leading models across MS-COCO and Winoground to measure this specific limitation. To ensure a fair comparison, we evaluated every model under identical conditions using NVIDIA A40 hardware…
- AI FoundationsOpen World EvaluationAug 12
Top 10 AI Infrastructure Companies & Applications
Many organizations invest heavily in AI, yet most projects fail to scale. 10-20% of AI proofs of concept progress to full deployment. A key reason is that existing systems are not equipped to support the demands of large datasets, real-time processing, or complex machine learning models. As AI becomes more central to business strategy, infrastructure…
- Sentiment AnalysisOpen World EvaluationAug 10
Top 10 Open Source Sentiment Analysis Tools
Sentiment analysis has gained worldwide momentum as one of the text analytics applications. Businesses that have not implemented sentiment analysis may feel an urge to find out the best tools and use cases for benefiting from this technology. Explore the top open source sentiment analysis tools and no-code solutions for businesses looking to pilot sentiment…
- AI FoundationsInsightAug 6
Compare AI Revenues Across the Stack
The AI market expanded rapidly across all four layers (data, compute, models, and applications). For example, NVIDIA’s data center revenue increased from $47.5B to $115.2B in a single fiscal year (FY2024 to FY2025, ending January 2024 and January 2025). We tracked revenue data from over 80 AI companies. Explore how revenues shifted across compute, data,…