Agent
An AI-driven workflow that can take steps toward a goal instead of answering one message. In practice, that might mean searching, deciding, updating a system, and asking for help when it gets stuck.
Glossary
Practical AI terms, translated into normal business language.
This page is for buyer-search terms and plain-English definitions. If you want the sales-cycle questions instead, start with the FAQ.
Agentic workflow
A business process where an AI agent handles a chain of steps with rules, tools, and handoffs. Good agentic workflows still define what the human approves.
AI call QA
Using AI to review calls for compliance, coaching, or quality issues so managers can focus on exceptions instead of manually sampling everything.
AI phone agent
An AI system that answers inbound calls, handles common questions, qualifies leads, books appointments, and hands off to a human when needed. Useful when missed calls turn into lost revenue.
AI readiness
Whether a business has a workflow that is painful enough, frequent enough, and clear enough to justify an AI project. It does not mean having perfect data or a giant strategy deck.
AI ROI
Return on investment from an AI project. Usually measured in time saved, leads captured, errors reduced, faster response times, or revenue that stopped leaking out of a broken process.
AI workflow automation
Using AI inside a business process to move work forward: classify an email, extract details from a form, draft a reply, update a CRM, route a task, or flag exceptions.
API
An application programming interface. It is the approved way software systems read from or write to each other. AI projects often depend on APIs for CRMs, calendars, accounting tools, and ticketing systems.
Context window
The amount of text, data, and instructions a model can consider at once. Bigger context helps, but it does not replace retrieval, permissions, or clean workflow design.
Embeddings
Number representations of text, images, or other data that help systems compare meaning. Embeddings are often used for semantic search and retrieval.
Evaluation
A repeatable test for whether an AI system is doing the job. Evaluations can check accuracy, tone, citation quality, extraction fields, routing decisions, or whether the system asks for human review.
Guardrails
Rules and checks that limit what an AI system can do. Useful guardrails include approval steps, allowed tools, blocked topics, validation checks, and audit logs.
Hallucination
When an AI model states something confidently that is wrong, unsupported, or invented. Good systems reduce this with retrieval, validation, constraints, and human review.
Human-in-the-loop
A setup where AI does the first pass and a person reviews, approves, or handles edge cases. This matters when the cost of a wrong answer is high.
Integration
The connection between the AI system and the tools your team already uses, like CRMs, phone systems, calendars, spreadsheets, or ticketing software.
Knowledge base
The collection of SOPs, docs, notes, tickets, FAQs, and tribal knowledge that people need to do their jobs. AI gets more useful when it can search and cite this material.
LLM
Large language model. The core model that can read, write, summarize, classify, extract, and reason over text. It is powerful, but by itself it is not a finished business workflow.
Model
The AI system that produces predictions or responses. In business use, model choice matters less than the data, tools, review steps, and measurement around it.
Multimodal AI
AI that can work across more than one kind of input, such as text, images, audio, video, PDFs, or screenshots. This matters for calls, documents, forms, and field-service evidence.
OpenClaw
The private AI assistant setup Indy AI Consulting uses when a business wants internal search, secure context, reusable workflows, and better control over how AI is used across the team.
Orchestration
The layer that coordinates models, tools, prompts, permissions, memory, retries, and human approvals. Orchestration is what turns one AI response into a working process.
Private AI assistant
An internal assistant built around your company documents, processes, and tools instead of a generic public chat window. The point is control, context, and usefulness on real work.
Prompt engineering
Giving the model clear instructions, examples, constraints, and output formats so it behaves more predictably. Useful, but not a substitute for good workflow design.
Prompt injection
A security risk where outside text tries to override the system rules. AI systems that read emails, websites, tickets, or documents need defenses against this.
RAG
Retrieval-augmented generation. The model looks up relevant company information first, then uses that context when answering. This is how an assistant can answer from your docs instead of guessing from memory.
Semantic search
Search by meaning instead of exact keyword match. It can find related policies, tickets, notes, or product details even when the wording is different.
Structured extraction
Pulling specific fields from messy inputs like emails, PDFs, forms, or transcripts and turning them into clean data your systems can use.
Token
A chunk of text a model reads or writes. Tokens affect cost, speed, and how much information can fit into a request.
Tool calling
A model using approved tools or APIs during a workflow, like looking up a customer, creating a ticket, booking a job, or sending a follow-up.
Vector search
A way to find related documents or passages by meaning instead of exact keyword matches. Often used under the hood in RAG systems.
Use this stuff
Definitions do not save money. Workflows do.
If you already know where the leak is, skip the jargon and bring the workflow. If not, the FAQ answers the common starting questions.