Plain-language
definitions.
The terms behind a brain for your business. Written the way your team actually talks, not the way the category does.
A brain for your business.
A connected map of everything your company knows. Documents, conversations, recordings, voice notes, integrations, all linked so the right answer finds your people instead of your people hunting for it.
AEO. Answer Engine Optimization.
Optimizing your content and structured data so AI engines like ChatGPT, Perplexity, Claude, and Gemini cite you accurately when users ask them topical questions.
Knowledge graph.
A connected map of the entities (people, projects, products, decisions) in your data and the relationships between them. Lets you answer questions that span multiple documents instead of retrieving just one.
Knowledge management.
Two categories. Two different jobs. Enterprise search makes your existing content findable across multiple tools. Knowledge management is the broader practice of capturing, organizing, and updating what your company knows.
LLM orchestration.
Routing different tasks through different foundation models (OpenAI, Anthropic, Google, and others) based on which one is best at each task, instead of picking a single model for everything.
RAG. Retrieval Augmented Generation.
A technique for making AI answers accurate by retrieving relevant content from a knowledge base and feeding it to the language model before it generates a response. Instead of guessing from training data, the model writes its answer based on documents you actually have.
GraphRAG.
A retrieval architecture that uses a knowledge graph (entities and the relationships between them) instead of, or in addition to, a flat vector store. Lets AI answer questions that require connecting multiple documents through the entities they share.
Tabular memory.
A retrieval architecture for structured data: rows and columns from spreadsheets, databases, and reports. Lets AI answer aggregate and analytical questions ("how many," "what's the average," "show me the top N") that don't make sense over unstructured text.
When to use each.
Three retrieval architectures, three different jobs. Vector RAG retrieves passages. GraphRAG traverses relationships. Tabular memory queries structured data. A production AI system uses all three, automatically routed by the question.
Grounding.
The practice of anchoring every AI-generated answer to a verifiable source in the underlying data. Instead of the model writing from its training data, every claim it makes traces back to a specific passage, row, or relationship in your actual content.
Source citation.
The practice of attaching a verifiable reference to every claim an AI answer makes, pointing back to the specific passage, row, or relationship in the underlying data the claim came from. Lets the user check the answer instead of trusting the model.
Hallucination.
When a language model generates content that sounds plausible but isn't true. The model isn't lying. It has no concept of truth. It's pattern-matching what an answer to your question would probably look like, and "probable" doesn't equal "accurate."
Audit trail. Chain of thought.
The visible record of every step an AI took to produce an answer: which sources it retrieved, which model it routed through, what intermediate reasoning it followed. Lets you debug, audit, and trust the output instead of treating the model as a black box.
Chain of reasoning.
The sequence of inferences an AI follows to arrive at an answer: "X is true because A, A is true because B and C, B comes from source D." Lets the user follow the logic step by step, not just see the final claim.
AI agents.
AI systems that can take actions, not just answer questions. They use tools, query systems, write to other systems, and chain multiple steps together to complete a task instead of just generating a response.
Tool use. Tool calling.
The capability of a language model to invoke external functions, APIs, or systems as part of generating an answer. Instead of just producing text, the model can search a database, call a service, run a calculation, or update a system, and use the result in its response.
Foundation model.
A large pre-trained AI model that serves as the underlying capability layer for AI applications. ChatGPT, Claude, Gemini, and Llama are foundation models. Application-layer products like Simply Asking use them as a substrate for reasoning, drafting, and language understanding. They don't replace them.
MCP. Model Context Protocol.
An open standard from Anthropic for connecting AI models to external tools and data sources. Lets any compliant AI assistant access any compliant tool without custom integration work. Think of it as USB for AI applications.
Tribal knowledge.
The knowledge a team relies on every day that lives only in someone's head. It was never written down, never documented, never indexed. When that person is on vacation, sick, or leaves the company, the knowledge leaves with them. Most acute in service businesses where one technician 'just knows' how to handle the call, multi-location franchises where playbooks never travel between locations, and hospitality teams where the brand standard depends on whoever's on shift.
Knowledge silos.
When knowledge inside a company is fragmented across teams, tools, and individuals with no connection between them. Sales has its information, support has its information, ops has its information. Each team makes decisions without seeing what the others already know.
Single source of truth.
A single, authoritative place where the current answer to any question lives. Eliminates the "we have five different versions of this and I don't know which is right" problem that every multi-team company eventually hits.
Knowledge gap detection.
Automatic identification of questions your team is asking that the knowledge base can't answer, and topics where the available information is thin, contradictory, or out of date. Tells you what's missing before someone gets burned by it.
Knows what it doesn't know.
An AI that recognizes when it doesn't have a confident answer, says so plainly, and points to what's missing. The opposite of confident hallucination. Honest about its limits, useful because of it.
Standard operating procedures (SOPs).
The documented step-by-step playbook for how a task gets done the same way every time, in every shift, at every location. The backbone of multi-location operations, franchise systems, and any business where consistency is the product.
Brand consistency.
The discipline of delivering the same brand experience across every location, every shift, every customer interaction. Most acute in multi-location and franchise businesses, where a single bad outpost shapes the whole brand's reputation.
New location onboarding.
The process of getting a new restaurant, retail store, franchise unit, or service branch fully operational (staffed, trained, branded, and consistent) before the doors open. Most multi-location operators ship binders and run two weeks of training, then watch knowledge transfer break down.
Field service knowledge.
The procedures, customer history, equipment specs, and judgment calls a field technician needs to do the job, on-site, often without a signal, often with their hands full. The pain point that defines whether your service business scales or stalls.