Running a local LLM at home or in a small lab is no longer just for experts. With tools like Ollama, llama.cpp, vLLM, and Open WebUI, you can create a powerful, private, and customizable AI setup. But the real challenge isn’t just getting a model running, it’s building a stack that’s flexible, secure, and easy […]
The ultimate guide to building a local LLM homelab (2025) Read More »
Most banks and insurers are no longer experimenting with AI. They already have chatbots, OCR pipelines, risk models, and fraud classifiers in production. Yet operational costs remain high, cycle times remain slow, and human bottlenecks still dominate critical workflows. Traditional AI systems are good at individual tasks. Financial services, however, are dominated by multi-step, cross-system,
Top Agentic AI Use Cases in Banking & Insurance Read More »
Scaling RAG pipelines from a prototype to a production system handling thousands of queries per second (QPS) reveals a harsh reality: default configurations rarely meet sub-second service level agreements (SLAs). Achieving consistent low latency at scale requires a fundamental shift in perspective. Speed is not merely a function of a faster vector database. Instead, latency
How to optimize RAG for sub-second latency? Read More »
In this blog, we break down why standard chunking fails for structured data and how to design table-preserving chunking strategies using modern RAG best practices. Each approach comes with implementation guidance, use cases, and architecture fit. Why chunking fails on tabular data? Tables aren’t text — they are relational knowledge graphs compressed into rows and
Why chunking fails on Tables in RAG, & 4 proven strategies to fix it Read More »
If you’ve ever built or evaluated multi-agent LLM systems, you’ve hit the same bottleneck:agents collaborate by dumping text back and forth. This works, but comes with structural problems: LatentMAS proposes a fundamentally different inter-agent communication model:skip the token channel completely and operate directly in latent space. Below, we break down its architecture, performance characteristics, practical
LatentMAS explained: A new architecture for faster multi-agent AI systems Read More »
Prompt data types matter more than most developers realize. It started with a simple anomaly. We were building a complex multi-turn conversational agent for a client. The logic was sound, the model was the latest GPT-4, and the context retrieval was optimized. Yet, the agent felt… sluggish. Worse, it was hallucinating during complex reasoning tasks, and
How prompt data types are costing 40% in AI performance? Read More »
November 13, 2025 OpenAI has officially launched GPT-5.1, introducing meaningful improvements in reasoning depth, responsiveness, and conversational flow. Rather than a raw capability jump, GPT-5.1 feels like an upgrade to how users experience the model: Faster where needed More deliberate when required and more controllable overall. This release marks another step toward AI systems that
GPT-5.1: Discover What’s New in OpenAI’s Latest AI Model Read More »
OpenAI’s IndQA benchmark shifts the conversation from translation to native, culture-aware reasoning. Here’s what it means for AI product teams, startups, and enterprises, Published by InteligenAI — 12 November 2025 In today’s age of large-language models (LLMs) and generative AI, one major gap persists: language and cultural depth. Most benchmarks, systems and deployments assume English
Why the launch of IndQA by OpenAI matters for global AI — and why India is key Read More »
November 8, 2025 Open source projects for engineers are transforming development workflows. Discover three open-source projects (opencode, DeepCode, Llama-Factory) that bring AI into developer workflows: inside the terminal, from paper-to-code, and from model-to-deployment. Modern engineering teams want AI that fits their workflow. Whether you’re a backend engineer, a research scientist, or a DevOps lead, developer-native
3 open-source projects every engineer should try in 2025 Read More »
Overview Zero-shot (open-vocabulary) object detection lets models find and localize objects they were not explicitly trained on — using language prompts instead of thousands of class-specific annotations. This changes how enterprises approach vision projects: faster prototyping, less labeling, and new opportunities for real-time automation. Top 5 zero-shot object detection models: 1. OWL-ViT What it is:
Top 5 zero-shot object detection models in 2025 Read More »