Intelligence FeedЛента исследований
LABORATORY INSIGHTS & DATA ИССЛЕДОВАНИЯ И ПРАКТИКА
Engineering research and analysis from NeuraIQ. Evidence before claims. Инженерные исследования и разборы NeuraIQ. Сначала доказательства, затем выводы.
Research-based guide Руководство на основе исследования
Research-based guide · Snapshot 19 August 2026
AI development methodologies: a control map, not a ranking
Review five control layers, inspect the evidence and use a private-in-browser Risk Map to see which decisions remain unresolved.
Руководство на основе исследования · Срез 19 августа 2026
Методологии AI-разработки: карта контроля вместо рейтинга
Разберите пять слоёв контроля, проверьте доказательства и используйте локальную Карту рисков, чтобы увидеть, какие решения пока не зафиксированы.
How Search Behaviour Changed in 2025: The Data
Google's share of search dropped 6.1 points in six months. ChatGPT tripled its share. But AI traffic spends 67.7% more time on page than organic. The full data.
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ChatGPT vs Perplexity vs Grok: How AI Engines Choose Their Sources
61.3% of zero-click answers are generated by AI engines — but ChatGPT and Perplexity cite the same source only 11% of the time. Platform-specific optimisation is no longer optional.
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Automating Semantic Core Research with AI: A 10x Advantage
86% of SEO specialists have integrated AI into their keyword research. Intent accuracy up 20–30 points. ROI up to 520% in tech verticals. Here is the data.
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Zero-Click Search in 2025: The New Reality of SEO
Over 65% of searches now end without a click. Organic traffic from US search dropped from 2.3B to 1.8B visits in one year. Here is what is happening — and what to do about it.
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The Hidden Cost of Hallucinations in Enterprise Document Q&A
"Our system is 95% accurate." In most software contexts, 95% accuracy is excellent. In enterprise document Q&A for regulated industries, 95% accuracy means 1 in 20 answers is wrong — and someone is making a decision based on it. What Hallucinations Actually Cost The naive view of hallucination cost: an analyst notices the wrong answer, ignores it, searches manually. Time lost: 5 minutes. The reality is more complex and more expensive: Direct Costs * Verification overhead: If analysts don
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Why Most RAG Implementations Fail in Production
Retrieval-Augmented Generation works beautifully in demos. You chunk documents, embed them, store in a vector database, retrieve the top-k, feed to an LLM. The prototype impresses everyone. Then you hit production. And everything breaks. After building RAG systems for 8 enterprise clients — including a government regulator, two legal firms, and a financial services company — we've catalogued the failure modes. Here are the five that kill most implementations. Failure 1: Naive Chunking Fixed
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How We Built a 40-Knowledge-Base System for a Government Regulator
When a government regulatory authority approached us, they had a problem that sounds deceptively simple: 10,000+ documents, 40 separate knowledge bases, and analysts spending hours manually searching for precedents. The Challenge Regulatory documents don't come in clean formats. We faced scanned PDFs from the 1990s, handwritten annotations, tables embedded in images, and documents in multiple languages — all requiring identical treatment. * 10,000+ source documents, 1M+ pages total * 40 in
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