Glossary
The GTM glossary for the AI era
46 terms. One worldview.
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The ten terms every B2B operator building in the AI era should own.
- 01Customer intelligenceCustomer intelligence is the structured, queryable layer of meaning a B2B company builds from every conversation, signal, and interaction it has with buyers and customers.
- 02Context BloatContext bloat is the degradation in model output that happens when too much raw or irrelevant data is stuffed into the context window, drowning the signal in noise.
- 03OntologyAn ontology is a structured map of the concepts, entities, and relationships in a domain, used to give a language model a consistent vocabulary and schema for reasoning about source data.
- 04Citation GraphA citation graph is the structure that traces every claim in a generated output back to the specific source material it came from, creating a verifiable audit trail.
- 05Agent-ready dataAgent-ready data is customer data structured, cited, and queryable by an AI agent without human translation.
- 06Agent-facing surfaceAn agent-facing surface is the part of a software product built for an AI agent to interact with, usually an MCP server with a clean tool schema.
- 07MCP Engine Optimization (MEO)MCP Engine Optimization (MEO) is the discipline of designing an MCP server so that AI agents choose it over alternatives when answering a query.
- 08Closed-loop content engineA closed-loop content engine is a system that uses customer signal to generate content and then feeds the resulting performance back into the next generation cycle.
- 09Vibes-based messagingVibes-based messaging is the common B2B practice of writing positioning and copy from intuition rather than real buyer data.
The full glossary
- AI InfrastructureAgent loopAn agent loop is the iterative cycle of observe, plan, act, and observe again that runs until the agent completes its task.
- AI InfrastructureAgent memoryAgent memory is what an agent retains across turns or sessions, split between short-term context and long-term external stores.
- The IntersectionAgent-facing surfaceAn agent-facing surface is the part of a software product built for an AI agent to interact with, usually an MCP server with a clean tool schema.
- The IntersectionAgent-ready dataAgent-ready data is customer data structured, cited, and queryable by an AI agent without human translation.
- The IntersectionAgentic marketingAgentic marketing is the practice of running marketing motions with AI agents that decide what to do next, not just how to do the current step.
- The IntersectionAI GTM EngineerAn AI GTM Engineer is an emerging hybrid role responsible for wiring AI agents and data infrastructure into a company's go-to-market motion.
- The IntersectionAI SDR / AI BDRAn AI SDR is an autonomous agent that runs outbound prospecting work traditionally handled by a sales development or business development representative.
- The IntersectionAI-native GTMAI-native GTM is a go-to-market operation built from the start to run with AI agents in the stack, as opposed to bolt-on AI layered onto a pre-AI process.
- The IntersectionBuyer archiveA buyer archive is the total set of first-party signals a company has accumulated from every buyer and customer interaction it has ever had.
- GTM FundamentalsBuyer intentBuyer intent is the signal that a prospect is actively researching or evaluating a solution, used by sales and marketing teams to prioritize outreach.
- GTM FundamentalsCategory designCategory design is the discipline of naming, shaping, and defending a new market category in which a company can become the default choice.
- AI InfrastructureCitation GraphA citation graph is the structure that traces every claim in a generated output back to the specific source material it came from, creating a verifiable audit trail.
- The IntersectionClosed-loop content engineA closed-loop content engine is a system that uses customer signal to generate content and then feeds the resulting performance back into the next generation cycle.
- The IntersectionContent velocityContent velocity is the rate at which a marketing team can ship grounded, defensible content from idea to publish.
- AI InfrastructureContext BloatContext bloat is the degradation in model output that happens when too much raw or irrelevant data is stuffed into the context window, drowning the signal in noise.
- AI InfrastructureContext EngineeringContext engineering is the discipline of deciding what a language model should know at inference time, including the source data, structure, and ordering of its working memory.
- AI InfrastructureContext WindowA context window is the maximum number of tokens a language model can process in a single request, covering both the input prompt and the generated output.
- GTM FundamentalsConversation intelligenceConversation intelligence is the category of software that records and analyzes sales calls for rep coaching, deal inspection, and pipeline review.
- The IntersectionCustomer intelligenceCustomer intelligence is the structured, queryable layer of meaning a B2B company builds from every conversation, signal, and interaction it has with buyers and customers.
- The IntersectionCustomer world modelCustomer world model is an older term for the customer intelligence layer. It refers to the living, structured, queryable representation of everything a B2B company knows about its buyers.
- The IntersectionDefensible AI contentDefensible AI content is AI-generated content where every claim traces back to a source that survives a legal, brand, and buyer review.
- GTM FundamentalsDemand generationDemand generation is the marketing motion aimed at buyers who are not actively searching yet, through paid, content, events, and brand.
- The IntersectionFirst-party buyer signalFirst-party buyer signal is any signal from a conversation, transaction, or interaction that a company owns directly, as opposed to third-party intent data purchased from a vendor.
- The IntersectionGrounded AI contentGrounded AI content is AI-generated text anchored in proprietary source material with traceable citations back to the original evidence.
- The IntersectionGTM agentA GTM agent is an AI agent that takes action on behalf of a go-to-market team, including AI SDRs, AI marketers, and AI customer success managers.
- AI InfrastructureHallucinationA hallucination is output from a language model that looks plausible and fluent but is factually incorrect, unsupported by source material, or fabricated entirely.
- AI InfrastructureHuman in the loopHuman in the loop is a design pattern where an agent drafts or proposes work but a human approves before anything ships.
- The IntersectionICP-message fitICP-message fit is the degree to which a company's messaging matches the language, pains, and priorities of its ideal customer profile.
- GTM FundamentalsIdeal Customer Profile (ICP)An Ideal Customer Profile (ICP) is the defined segment of companies where a product is a structural fit, worth the cost of acquisition, and likely to expand.
- AI InfrastructureMCP Engine Optimization (MEO)MCP Engine Optimization (MEO) is the discipline of designing an MCP server so that AI agents choose it over alternatives when answering a query.
- GTM FundamentalsMessagingMessaging is the tactical expression of positioning, the specific sentences a company uses to describe its product across the website, sales deck, and outbound email.
- AI InfrastructureModel Context Protocol (MCP)Model Context Protocol (MCP) is an open protocol for connecting language models to external tools, data sources, and capabilities through a standard wire format.
- AI InfrastructureOntologyAn ontology is a structured map of the concepts, entities, and relationships in a domain, used to give a language model a consistent vocabulary and schema for reasoning about source data.
- GTM FundamentalsPersonaA persona is a composite sketch of one buyer role within an ICP, describing their goals, constraints, pain points, and decision criteria.
- GTM FundamentalsPositioningPositioning is the strategic choice about who a product is for, what category it competes in, and why a specific buyer should pick it over the alternatives.
- GTM FundamentalsProduct marketingProduct marketing is the function that owns positioning, messaging, launches, and sales enablement for a product.
- AI InfrastructurePrompt engineeringPrompt engineering is the practice of phrasing requests to a language model to get better outputs.
- AI InfrastructureRetrieval Augmented Generation (RAG)Retrieval Augmented Generation (RAG) is a pattern where a system retrieves relevant documents from an external source, injects them into the model's prompt, and has the model answer from the retrieved material rather than from parametric memory.
- GTM FundamentalsRevenue intelligenceRevenue intelligence is the category of software that combines CRM, call, and email signals to forecast pipeline and diagnose deal health.
- GTM FundamentalsSales enablementSales enablement is the practice of giving reps the content, training, and data they need to close deals.
- AI InfrastructureStructured outputStructured output is the practice of forcing a language model to return data in a predictable schema, usually JSON.
- AI InfrastructureTool callingTool calling is the capability that lets a language model invoke a function, API, or MCP server to fetch data or take action.
- The IntersectionVibes-based messagingVibes-based messaging is the common B2B practice of writing positioning and copy from intuition rather than real buyer data.
- The IntersectionVoice matchingVoice matching is the practice of generating AI content that sounds like a specific author or team, capturing structural patterns rather than surface tics.
- GTM FundamentalsVoice of customerVoice of customer is the verbatim language buyers and users use to describe their problems, goals, and reactions to a product.
- GTM FundamentalsWin/loss analysisWin/loss analysis is the practice of investigating closed deals (won, lost, no decision) to extract patterns that improve future deals.
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