The language of modern operations.
A quick-reference guide to the AI, staffing, and outsourcing terms you'll hear us use — so every conversation starts on the same page.
A
AI Agent
A software system that perceives its environment, reasons about a goal, and takes actions — often using tools like web search, code execution, or APIs — to complete tasks with minimal human supervision.
AI Hallucination
When an AI model generates output that sounds plausible but is factually wrong or entirely made up, typically because the model is pattern-matching rather than retrieving verified facts.
Agentic AI
AI systems that can plan, use tools, and take multi-step action toward a goal with little ongoing human input.
Artificial Intelligence (AI)
Software built to perform tasks that normally require human intelligence, such as learning, reasoning, and judgment.
Attention Mechanism
The component inside transformer models that lets the network weigh how relevant each part of an input is to every other part, enabling it to understand context across long sequences.
Automation
Using software or tooling to carry out a task with little or no manual effort.
C
Chain of Thought (CoT)
A prompting technique where a model is guided to reason step-by-step before giving a final answer, significantly improving accuracy on complex problems.
Cloud Computing
Accessing compute, storage, and software over the internet instead of running it on local hardware.
Context Window
The maximum amount of text (measured in tokens) a language model can read and reason over in a single interaction — everything outside this window is invisible to the model.
D
Data Mining
Digging through large datasets to surface useful patterns and relationships.
Data Security
The controls and practices used to keep data confidential, accurate, and available only to the right people.
Digital Transformation
Rethinking how a business operates by weaving digital tools and workflows into every part of it.
Diffusion Model
A class of generative AI model that learns to create data (images, audio, video) by learning to reverse a gradual noise process — the basis for tools like Stable Diffusion and Sora.
E
Embedding
A numerical representation of text, images, or other data as a vector of numbers that captures semantic meaning, allowing AI models to find similar items by comparing distances in vector space.
F
Fine-tuning
Further training a pre-trained foundation model on a smaller, task-specific dataset so it performs better on a particular domain or style without training from scratch.
Foundation Model
A large AI model trained on broad data at massive scale that can be adapted for many downstream tasks — examples include GPT-4, Claude, and Gemini.
Function Calling
A capability that lets a language model trigger structured external tools or APIs — like querying a database or running a calculation — and incorporate their results into its response.
G
GDPR (General Data Protection Regulation)
The EU's legal framework governing how personal data belonging to EU residents can be collected and used.
Generative AI (GenAI)
AI that produces new content — text, images, code, audio, or video — by learning patterns from training data rather than just classifying or predicting.
GPU (Graphics Processing Unit)
Specialized hardware originally designed for graphics that excels at the massively parallel matrix math underlying AI model training and inference.
Grounding
Connecting an AI model's responses to verified, real-world information — such as retrieved documents or live data — to reduce hallucinations and improve factual accuracy.
Guardrails
Rules, filters, or secondary models placed around an AI system to prevent it from producing harmful, off-topic, or policy-violating outputs.
H
Human-in-the-Loop (HITL)
An approach where people review, correct, or approve AI output to keep it accurate and reliable.
I
Inference
Running a trained AI model on new inputs to produce outputs — as distinct from training, which is the process of teaching the model in the first place.
K
KPI (Key Performance Indicator)
A specific, trackable metric used to gauge progress toward a business goal.
L
LLM (Large Language Model)
A neural network trained on vast amounts of text data that can generate, summarize, translate, and reason about language. GPT-4, Claude, and Llama are examples.
LLMOps
The practices, tools, and infrastructure for deploying, monitoring, versioning, and continuously improving large language models in production.
M
Machine Learning (ML)
A branch of AI where systems learn from data to improve at a task over time without being explicitly programmed with rules.
Managed Pod
A KDCI staffing model where a dedicated team comes with its own embedded leadership and QA, plugged directly into your company.
Managed Services
Handing over ongoing responsibility for a process or system to a provider who proactively maintains it.
MLOps (Machine Learning Operations)
The discipline of streamlining the end-to-end ML lifecycle — from data prep and model training through deployment, monitoring, and retraining — using DevOps-style practices.
Model Drift
The gradual degradation in a deployed model's accuracy as the real-world data it sees starts to differ from the data it was trained on.
Multimodal AI
AI systems that can understand and generate content across multiple types of data — such as text, images, audio, and video — within the same model.
N
Natural Language Processing (NLP)
The field of AI focused on enabling machines to understand, interpret, and generate human language in a useful way.
Neural Network
A computational architecture loosely inspired by the brain, made of layers of interconnected nodes that transform inputs into outputs through learned weights.
O
Orchestration
Coordinating multiple AI models, tools, and data sources into a coherent pipeline or agent workflow to complete complex tasks.
Overfitting
When a model learns the training data so precisely — including its noise and quirks — that it performs poorly on new, unseen data.
P
Prompt Engineering
The practice of designing and refining the instructions given to an AI model to reliably produce the desired output.
Q
QA (Quality Assurance)
A proactive set of checks built into a process to prevent defects and errors before they reach the customer.
QC (Quality Control)
Reviewing finished work or output to confirm it meets a defined quality standard.
R
RAG (Retrieval-Augmented Generation)
An architecture that pairs a language model with a retrieval system — the model first fetches relevant documents from a knowledge base, then generates a response grounded in that content.
Reasoning Model
A language model variant that spends extra compute "thinking" before responding — producing an internal chain of thought that improves performance on hard logical, mathematical, and coding problems.
Remote Work
Working from outside a traditional office, often from home or another location of choice.
RLHF (Reinforcement Learning from Human Feedback)
A training technique where human raters score model outputs, and those scores are used to steer the model toward more helpful, safe, and accurate responses.
RPA (Robotic Process Automation)
Software "bots" that mimic repetitive human actions across systems to speed up routine work.
S
Scalability
The ability of a team, system, or process to grow (or shrink) smoothly as demand changes.
Semantic Search
Search that understands the meaning and intent behind a query — rather than just matching keywords — typically using embeddings to find conceptually similar results.
SLA (Service Level Agreement)
A documented commitment between provider and client covering expected quality, availability, and responsibilities.
SOP (Standard Operating Procedure)
A written, step-by-step guide for carrying out a routine task consistently.
Staff Augmentation
Adding outside specialists to temporarily extend your team's capacity or skill set.
Supervised Learning
A machine learning approach where a model is trained on labelled examples — input/output pairs — so it learns to predict the correct output for new inputs.
T
Token
The basic unit of text a language model processes — roughly a word or word fragment. Model costs, context windows, and speed are all measured in tokens.
Transfer Learning
Reusing a model trained on one task as the starting point for a different but related task, dramatically reducing the data and compute needed.
Transformer
The neural network architecture introduced in the 2017 "Attention Is All You Need" paper that underlies virtually all modern large language models.
V
Vector Database
A database purpose-built to store and query embeddings, enabling fast similarity search over millions of items — a core component of RAG systems.
W
Workflow Automation
Designing and running processes so tasks, data, and approvals move automatically between people and systems based on set rules.
Z
Zero Trust Security
A security approach that verifies every user and device continuously, regardless of whether they are inside or outside the network.
Zero-shot Learning
Prompting or tasking an AI model to perform something it was never explicitly trained on, relying purely on general knowledge learned during pre-training.
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