Статьи о создании высокоточных AI ассистентов, Retrieval Augmented Generation, оценке и продакшн‑операциях.
Sep 15, 2025
Deep comparison of AI chat assistants and traditional live chat for digital / marketing agencies: cost, scalability, lead quality, client value, and operational metrics.
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Comprehensive blueprint for automating customer support with AI while safeguarding quality, compliance, and customer trust.
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Blueprint for planning, implementing, securing, evaluating and continuously improving a production-grade AI website assistant grounded only in your own content.
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Engineering a high-signal focused crawler: discovery, prioritization, trap avoidance, freshness, observability.
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How a B2B SaaS vendor tripled on-site engagement, doubled qualified lead rate, and cut support response time using a grounded AI assistant.
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Designing high-precision chunking for website RAG: fixed, semantic, hierarchical, adaptive strategies and evaluation.
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Feature-by-feature analysis of CrawlBot AI versus Intercom and Drift AI assistants: retrieval quality, enterprise controls, analytics depth, and TCO.
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From prototype to production: deployment models, embedding patterns, performance, observability, iteration strategy.
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Rigorous, reproducible evaluation framework for retrieval‑grounded AI assistants: test sets, metrics, automation, governance.
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A practical, end‑to‑end playbook for reducing hallucinations in retrieval‑augmented (RAG) AI assistants: data quality, retrieval precision, prompting, validation, and continuous improvement.
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Engineering resilient hybrid retrieval: blending vector, lexical, metadata & temporal signals for website RAG.
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Operational KPI framework for AI assistants: retrieval, generation, resolution, business impact & diagnostic metrics.
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Practical, production-focused blueprint for Retrieval‑Augmented Generation (RAG) tailored to dynamic website content—public and authenticated.
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Security & compliance architecture patterns for multi‑tenant AI assistants: isolation, privacy, governance, resilience.
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A practical framework for embedding security, privacy, and threat modeling into AI chat assistant platforms from day zero.
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Tactical breakdown of how grounded AI assistants increase on‑page engagement, session depth, qualified lead rate, and content discoverability.
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Engineering a focused crawl and transforming raw HTML into reliable, structured, high‑signal knowledge assets for AI assistants.
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A practical framework for judging AI chat vendors across retrieval quality, security, latency, governance, and roadmap resilience.
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A practical overview of converting your existing website content into a high-accuracy AI chat assistant using retrieval-augmented generation.
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