Best document AI approach in 2026: OCR, VLMs, or Agentic Systems?

For the last decade, the objective of document processing was simple: Digitization. The goal was to transform physical paper into digital characters. Today, that objective is obsolete. The new imperative is Intelligent understanding. It is no longer enough to extract a string of text; systems must now interpret that text as structured, actionable data. They […]

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The ultimate guide to building a local LLM homelab (2025)

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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

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Top Agentic AI Use Cases in Banking & Insurance

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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,

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How to optimize RAG for sub-second latency?

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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

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Why chunking fails on Tables in RAG, & 4 proven strategies to fix it

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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

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LatentMAS explained: A new architecture for faster multi-agent AI systems

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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

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Hive to improve accuracy in AI solutions

How prompt data types are costing 40% in AI performance?

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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

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