Reference

IT & AI glossary

Plain-English definitions for the terms that come up across our services — no jargon, no assumed background.

AI & Machine Learning

Agentic AI: AI systems that can autonomously plan and carry out multi-step tasks toward a goal, rather than just responding to a single prompt.
LLM (Large Language Model): An AI model trained on large volumes of text that can understand and generate human language, powering tools like chatbots and content generation.
Machine Learning: A way of building software that learns patterns from data, rather than following explicitly programmed rules for every case.
Predictive Model: A machine learning model trained to forecast an outcome, like sales or customer churn, based on historical data.
Chatbot / Conversational AI: Software that simulates conversation with users, often built on an LLM, to answer questions or complete tasks.
MLOps: The practices and infrastructure used to deploy, monitor, and maintain machine learning models once they're running in production.

API: A defined way for two pieces of software to communicate, letting one system use another system's data or functionality.
SaaS (Software as a Service): Software hosted centrally and delivered over the internet, usually by subscription, rather than installed locally on a device.
Microservices: An architecture where an application is built as a set of small, independently deployable services instead of one large program.
Tech Stack: The combination of programming languages, frameworks, and tools used to build a piece of software.
MVP (Minimum Viable Product): The smallest version of a product that's functional enough to release and learn from real users.
Legacy System: An older piece of software or infrastructure still in active use, often difficult to maintain or connect to modern tools.

Cloud Migration: Moving an application, its data, or infrastructure from on-premise servers, or one cloud provider, onto a (different) cloud platform.
CI/CD: Continuous Integration/Continuous Deployment — automated pipelines that test and release code changes frequently and reliably, instead of manual, infrequent releases.
DevOps: A set of practices combining software development and IT operations to ship and maintain software faster and more reliably.
Auto-scaling: Cloud infrastructure automatically adding or removing computing resources based on real-time demand.

ERP (Enterprise Resource Planning): A system that connects core business processes — finance, inventory, HR, operations — into one shared platform.
CRM (Customer Relationship Management): A system for tracking and managing a company's interactions with current and potential customers.
ERP vs. CRM: ERP manages internal operations like inventory, finance, and supply chain. CRM manages external relationships like sales and support. Many businesses use both, connected together.
Role-Based Access Control (RBAC): A system for restricting who can see or do what within software, based on a person's role rather than configuring access individually.

SEO (Search Engine Optimization): The practice of improving a website so it ranks higher and gets found more easily in search engine results.
Programmatic Marketing: Automated, real-time buying and targeting of digital ads across platforms, rather than manually managed campaigns.
Conversion Rate: The percentage of visitors to a website who complete a desired action, like making a purchase or filling out a form.

Regression Testing: Re-testing an application after a code change to confirm existing features still work correctly.
Load Testing: Testing how an application performs under expected or peak levels of user traffic.
Cross-Browser Testing: Verifying that a website or app works correctly across different web browsers and devices.