AI Glossary

20 operational AI terms explained in plain English. No jargon walls. No PhD required.

3Rs (Repetitive, Rules-based, Resource-intensive)

The three characteristics that identify tasks suitable for AI automation. If a task scores high on all three, it is a strong candidate. If it scores low on any, be cautious. How to Choose AI Tools.

AEO (Answer Engine Optimisation)

Structuring website content and data so that AI engines (ChatGPT, Claude, Perplexity, Google AI Overviews) can accurately retrieve and cite your business information. The next evolution of SEO. What is AEO?.

AI (Artificial Intelligence)

Technology that enables machines to perform tasks that normally require human intelligence, such as understanding language, recognising patterns, and making decisions. In a business context, AI typically automates repetitive, rules-based work.

API (Application Programming Interface)

A way for different software systems to communicate with each other. APIs allow AI tools to connect with your existing business systems, CRMs, claims platforms, and email without replacing them.

Automation

Using technology to perform tasks with minimal human intervention. Unlike AI, basic automation follows fixed rules (if X happens, do Y). AI-powered automation can handle more complex, variable tasks.

Data Pipeline

The infrastructure that moves data from its source (emails, forms, systems) through processing stages to its destination. Clean, reliable data pipelines are a prerequisite for AI systems that work consistently.

Edge Case

An unusual or unexpected scenario that falls outside the normal process flow. AI systems need clear handling for edge cases, typically routing them to human review rather than attempting to process them automatically.

Grounded AI

AI systems that generate responses based on verified, specific source material rather than general training data. Ensures accuracy, traceability, and trust. Every claim can be traced back to its source document. How AI Decides Who to Recommend.

Hallucination

When an AI system generates information that sounds plausible but is factually incorrect. Common in generic LLMs. Grounded AI architectures (like RAG) are specifically designed to prevent this. AI for Motor Claims.

Human-in-the-Loop

An AI system design where humans review, approve, or override AI decisions at defined checkpoints. Essential for maintaining quality, catching errors, and building trust during early adoption. Our position on ethical AI.

Large Language Model (LLM)

An AI system trained on vast amounts of text data that can understand and generate human-like language. ChatGPT, Claude, and Gemini are examples. Used in customer service, document drafting, and data extraction. How to Rank in ChatGPT.

Machine Learning (ML)

A subset of AI where systems learn from data and improve over time without being explicitly programmed for every scenario. Used in fraud detection, claims categorisation, and predictive analytics.

MVP (Minimum Viable Product)

The simplest version of a product that delivers core value. In AI projects, starting with an MVP means solving one clear problem before expanding. This reduces risk and accelerates learning.

NLP (Natural Language Processing)

AI technology that enables computers to understand, interpret, and generate human language. Used in email triage, document analysis, chatbots, and customer service automation.

Process Mapping

Documenting every step in a business workflow: inputs, decisions, outputs, handoffs, and exceptions. Essential preparation before any AI implementation. If you cannot map the process, you cannot automate it. The 5 Pillars of AI.

Prompt Engineering

The practice of crafting specific instructions (prompts) to get the best output from an AI system. Good prompts include context, constraints, and clear success criteria.

RAG (Retrieval-Augmented Generation)

An AI architecture that retrieves verified source material before generating a response. This grounds the output in real data rather than training data, reducing hallucinations. CreditHire-Assist uses RAG to reference UK case law accurately. What is an AI Audit?.

ROI (Return on Investment)

The measurable financial return from an investment. For AI projects, ROI is calculated by comparing the cost of the current manual process against the cost after automation, including time saved, errors reduced, and capacity freed. AI Consulting Cost UK.

RPA (Robotic Process Automation)

Software that mimics human actions within digital systems, such as copying data between applications, filling forms, or processing transactions. Follows fixed rules and works best on structured, repetitive tasks.

SOS Framework

Optimus Consulting's engagement methodology: Stabilise (fix the process and the data first), Optimise (tighten the workflow so it runs cleanly), Scale (apply automation and AI where it earns its place). Work proceeds in this order. The SOS Framework.