/ featured · this week
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Orchestration architecture describes the control layer that routes specialized agents through complex business workflows. Unlike single-agent systems that execute predefined scripts, orchestration...
Aug 14, 2026/ recent · long reads
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Handoffs are a discipline, not an accident. Specialized agents operate reliably in isolation, but handoffs fail in production when orchestration treats state ownership as implicit rather than...
Aug 3, 2026
Agentification maturity is not measured by agent count. Start with one high-value workflow, establish guardrails and governance early, and transition ownership to the function that runs it.
Jul 29, 2026/ selected notes
All dispatches →01 / AI Agents
An AI agent maturity model for a business team is a staged framework that ranks how much work a business can safely hand to agents, and where along that path it currently sits. Most models split the...
Jul 27, 2026
02 / AI Agents
Agent readiness is the business case for AI agents restated as a production question: can you afford the oversight, not can you afford the license. Most guides answer a different question. They walk...
Jul 27, 2026
03 / Workflows
Five words get sold as five products. They are one dial with a price on each notch, and the notch you can afford to buy is rarely the notch you can afford to supervise.
Jul 25, 2026
/ the feed · everything we've published
AI-native lead capture re-engineers conversion architecture for a zero-click world where large language models become the surface of first contact. This article maps the three-layer architecture...
Kurt Fischman · Oct 27, 2025
ChatGPT inclusion is not a badge or a ranking. It is whether the model surfaces your brand as a relevant answer when a user prompts it. This article breaks down the retrieval mechanics that...
Kurt Fischman · Oct 27, 2025
Answer coverage is the measurable share of prompt surface patterns in your domain where your brand appears in AI-generated answers with valid, schema-backed assets. Engineering answer coverage...
Kurt Fischman · Oct 27, 2025
AI search optimization (AISO) is the discipline of engineering content, entity signals, and structured data so that information is discoverable, retrievable, and cite-worthy by large language...
Kurt Fischman · Oct 27, 2025
Monetizing AI visibility is the practice of converting brand citations, mentions, and recommendations inside AI-generated answers into measurable business outcomes including pipeline, revenue, and...
Kurt Fischman · Oct 27, 2025
Sales attribution from LLMs is the discipline of tracing revenue outcomes back to AI-generated recommendations, citations, and brand mentions across platforms like ChatGPT, Gemini, Perplexity, and...
Kurt Fischman · Oct 27, 2025
Entity salience in AI search measures which real-world entities a document is genuinely about, not merely which terms it mentions. When salience is high for the right entities, retrieval models...
Kurt Fischman · Oct 27, 2025
An AI search optimization agency is a professional service firm built to engineer brand visibility across large language models like ChatGPT, Claude, Gemini, and Perplexity. Unlike traditional...
Kurt Fischman · Oct 27, 2025
authority-building-for-llm-credibility
Kurt Fischman · Oct 27, 2025
Trust signals in AI-driven rankings are the verifiable markers of credibility, authority, and factual consistency that large language models use to evaluate, rank, and retrieve content for...
Kurt Fischman · Oct 26, 2025
AI-era authority is the practice of making a brand discoverable, interpretable, and citation-worthy to large language models including ChatGPT, Claude, Gemini, and Perplexity. It replaces the...
Kurt Fischman · Oct 26, 2025
Measuring AI visibility is the discipline of tracking how often and where AI platforms cite your brand in generated answers across ChatGPT, Gemini, Claude, and Perplexity. It replaces click-based...
Kurt Fischman · Oct 25, 2025
Competitive intelligence in AI search is the practice of analyzing which brands get cited by large language models and reverse-engineering the semantic, structural, and entity signals that produce...
Kurt Fischman · Oct 25, 2025
Content roadmaps built for the AI search era start with retrieval fitness, not keyword volume. The traditional editorial calendar optimized for organic click-through is structurally misaligned...
Kurt Fischman · Oct 25, 2025
Wikipedia and Wikidata Q-nodes anchor entity disambiguation in large language models. When an LLM encounters an ambiguous brand name, it consults public knowledge graphs to resolve identity, and a...
Kurt Fischman · Oct 25, 2025
Multimodal LLM retrieval processes images, audio, and video alongside text. Embeddings convert visual and audio assets into vectors that retrieval systems use for similarity search, while metadata...
Kurt Fischman · Oct 25, 2025
Endpoints give LLM retrieval systems structured access to your brand data. A dedicated JSON-LD fact endpoint, properly hosted and versioned, turns opaque marketing content into machine-readable...
Kurt Fischman · Oct 25, 2025
In AI search, brand mentions across authoritative sources matter more than backlinks. LLMs do not follow hyperlinks to assign authority. They evaluate mention frequency, sentiment polarity, and...
Kurt Fischman · Oct 25, 2025
Public repositories on GitHub, Hugging Face, and Zenodo function as training-data pipelines that large language models ingest during pre-training and fine-tuning. This article details how to...
Kurt Fischman · Oct 25, 2025
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