RAG
A method that lets an AI answer using internal documents or a knowledge base instead of relying only on its general training.
Example: An HR assistant that answers from the company's internal procedures.
B2B AI glossary: understand the terms before choosing a provider
Simple definitions for reading AIPartnerLens profiles, categories and guides without needing to speak like a technical expert.
The useful habit is not memorizing every acronym. Ask what the provider actually delivers, how your teams will use the solution, which data is required and who maintains the system after launch.
AI agent
A system that can chain several actions together to help complete a task.
An AI agent can, for example, read a request, retrieve information, apply a rule and propose an answer. It should remain controlled when its actions can affect business operations or customers.
Example: An agent that analyzes a support request and prepares a response for an adviser.
No-code
An approach for creating automations or tools with little or no code, often through visual platforms.
Example: Connect a form, spreadsheet and automated email using a visual tool.
Low-code
An approach that combines visual tools and lightweight development to build applications or automations faster.
Example: Build an internal application with visual blocks and a few custom rules.
POC
A short proof-of-concept used to test whether an AI idea is feasible before investing in a complete project.
Example: Test an assistant on a small document set before a broader deployment.
Workflow
A sequence of automated or semi-automated steps used to process a business task.
Example: Receive a request, assess it, create a task and notify the right team.
AI automation
The use of AI to perform or assist repetitive tasks within a business process.
AI automation uses models or AI agents to accelerate tasks such as request triage, response generation, lead qualification, document analysis or updates to internal tools. Sensitive decisions should be governed by rules, controls and human supervision.
Example: An SMB can automate the initial triage of support tickets with AI while keeping a person responsible for validating important replies.
Omnichannel
The ability to manage several channels such as email, chat, phone or social media within one customer-experience approach.
AI governance
The rules, responsibilities and controls that govern how AI is used within a company.
Example: Define who validates outputs, which data is permitted and how errors are handled.
AI integrator
A provider that connects AI solutions to a company's existing tools, data and processes.
Example: Connect an AI assistant to the CRM, helpdesk and an internal document repository.
Production deployment
Moving an AI prototype or test into real use by teams, with security, reliability and maintenance in place.
AI orchestration
Coordinating several tools, models or AI steps so they work together within one process.
Example: One tool reads a document, a model summarizes it, then a rule sends the result to the right team.
AI customer support
Using AI to help a support team handle customer requests faster or more consistently.
Example: An assistant proposes a reply while a person validates it before it is sent to the customer.
Lead generation
Activities intended to identify or qualify new commercial prospects.
Example: A chatbot qualifies an inbound request before passing it to the sales team.
How to use it
Use the glossary during a comparison
Identify the term used by the provider.
Ask which concrete use case it refers to.
Check the available evidence and system limitations.
Clarify budget, maintenance and responsibilities after launch.
Frequently asked questions about AI terminology
Why does AIPartnerLens still use technical terms?
Some terms are used by providers themselves. The glossary translates them into buyer-friendly language so you can ask better questions.
Do I need to master these terms before contacting a provider?
No. The goal is mainly to understand the actual scope of an offer, the evidence to request and the risks to verify.
Does a technical term guarantee a stronger offer?
No. A term such as RAG, AI agent or no-code still needs to be connected to a use case, budget, evidence and maintenance model.