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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Structured extraction
Pulling specific fields from messy inputs like emails, PDFs, forms, or transcripts and turning them into clean data your systems can use.
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.
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.
Integration
The connection between the AI system and the tools your team already uses, like CRMs, phone systems, calendars, spreadsheets, or ticketing software.
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.
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.
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.
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.
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.