AI Glossary

An AI agent is an autonomous software entity that perceives its environment through sensors or data inputs, reasons about the best course of action, and executes actions to achieve defined objectives. Agents can use tools, call APIs, and orchestrate workflows.

AI governance encompasses the policies, processes, and controls that organizations put in place to ensure AI is used responsibly, ethically, and in compliance with regulations. It covers security, cost control, audit trails, and ROI measurement.

AI-native design means building systems, architectures, and business processes with AI as a first-class citizen from the start—rather than bolting AI onto legacy systems. Thinkia specializes in AI-native consulting and platform development.

An API is a set of protocols and tools that allows different software systems to communicate. For AI, APIs enable applications to call LLMs (OpenAI, Claude, etc.), retrieve embeddings, and integrate AI capabilities into existing workflows.

Agentic AI refers to systems that operate with a degree of autonomy, perceiving their environment, making decisions, and executing multi-step actions to achieve goals without constant human intervention. Thinkia Synapse is an agentic platform for enterprise AI.

Automation refers to the execution of tasks without human intervention. When combined with AI, automation can handle complex decision-making, not just rule-based workflows. Hyperautomation extends this across entire business processes.

Autonomous systems operate with minimal or no human oversight, making decisions and taking actions in real time based on their environment. Examples include self-driving vehicles, automated warehouses, and AI-powered contact centers.

BERT is a Transformer-based model pre-trained on large text corpora. It learns bidirectional context and is used for tasks like semantic search, question answering, and classification. It paved the way for modern LLMs.

Bias in AI refers to systematic errors or distortions in training data or model behavior that lead to unfair, discriminatory, or inaccurate outcomes. Responsible AI practices include bias detection and mitigation.

Big data refers to datasets too large or complex for traditional processing. It is the foundational resource for training machine learning models and powering data-driven AI applications in enterprises.

A bot is software that automates conversational or repetitive tasks. Chatbots handle text; voicebots handle speech. Modern bots often use LLMs for natural, context-aware dialogue.

CRM systems manage customer data and interactions. AI enhances CRM with predictive analytics, lead scoring, automated follow-ups, and personalized experiences—enabling smarter customer engagement.

Customer Experience (CX) encompasses all touchpoints between a customer and a brand. AI transforms CX through personalized recommendations, automated support, and intelligent routing—delivering consistency and efficiency at scale.

A chatbot is an AI-driven assistant that converses with users via text. It can be rule-based (intent + responses) or powered by LLMs for open-ended, natural conversation. Used in customer service, sales, and internal tools.

Cloud AI refers to AI capabilities offered as managed cloud services—e.g., Azure OpenAI, AWS Bedrock, Google Vertex AI. Enterprises use them to avoid building and maintaining their own infrastructure.

Computer vision is the field of AI that allows machines to understand and interpret visual information—images and video. Applications include object detection, facial recognition, quality control, and medical imaging.

The context window is the maximum amount of input text (in tokens) that an LLM can process in one request. Larger windows allow longer documents and conversations but increase cost and latency.

Conversational AI enables machines to engage in natural, context-aware dialogue with humans. It powers Digital Humans, contact center automation, and virtual assistants across channels.

A copilot is an AI assistant embedded in an application or workflow—e.g., GitHub Copilot for code, Microsoft Copilot for Office. It augments human work with suggestions, automation, and natural language interaction.

A corpus is a structured collection of documents—often used for training models or as the knowledge base in RAG systems. Quality and structure of the corpus directly affect retrieval and generation quality.

A data lake is a centralized repository that stores structured and unstructured data in its raw form. It serves as the source for analytics, machine learning, and AI applications.

A data pipeline is an automated workflow that ingests, transforms, and moves data from sources (databases, files, APIs) to models or applications. Critical for keeping AI systems fed with fresh, clean data.

Deep learning uses neural networks with many layers to learn hierarchical representations of data. It underpins computer vision, NLP, and most state-of-the-art AI models today.

A Digital Human is a lifelike 3D avatar powered by conversational AI. It embodies a brand or service, engaging users in natural dialogue. Thinkia Digital Humans deliver hyper-personalized, memorable experiences.

Domain adaptation fine-tunes or augments a general-purpose model so it performs well in a specialized domain—e.g., legal documents, medical records. RAG is often used for domain-specific grounding.

Downtime is when systems are unavailable. Predictive AI can forecast failures before they occur, enabling proactive maintenance and zero-downtime operations—a key use case for industrial and critical systems.

The EU AI Act is Europe's regulatory framework for AI. It classifies systems by risk level (unacceptable, high, limited, minimal) and imposes requirements for transparency, human oversight, and documentation. Thinkia helps organizations achieve compliance.

An embedding is a vector (list of numbers) that represents the meaning of text, an image, or audio. Similar meanings produce similar vectors, enabling semantic search, clustering, and retrieval in RAG systems.

Enterprise AI refers to AI solutions designed for large organizations—with governance, security, scalability, and seamless integration with existing ERP, CRM, and data systems. Thinkia specializes in enterprise AI strategy and implementation.

Ethical AI is developed and deployed according to principles such as transparency, fairness, accountability, and privacy. It aligns with regulations like the EU AI Act and builds trust with stakeholders.

Evaluation assesses how well an AI model performs—on accuracy, relevance, safety, bias, and other metrics. Robust evaluation is essential before deploying models to production.

Event-driven architecture uses events (messages, signals) to trigger and coordinate system behavior. It supports real-time, reactive AI applications and scalable data pipelines.

Explainability is the capability to understand and explain how an AI model arrives at its outputs. It is critical for trust, debugging, and regulatory compliance. See XAI (Explainable AI).

Extraction (or information extraction) pulls structured data from unstructured text—entities, relationships, dates, etc. LLMs excel at this and are used for document processing and data enrichment.

Few-shot learning trains or adapts a model using very few labeled examples. LLMs excel at this via in-context learning—showing a few examples in the prompt without retraining.

Fine-tuning continues training a pre-trained model (e.g., GPT, Llama) on domain-specific data. It adapts the model to new tasks or vocabularies. Alternative to prompt engineering when more control is needed.

Foundation models are large models pre-trained on vast amounts of data. They can be adapted (via fine-tuning or prompting) to many downstream tasks. Examples: GPT-4, Claude, Llama.

Fusion integrates data or representations from multiple sources—e.g., text and images in multimodal AI, or hybrid retrieval in RAG. It improves accuracy and richness of AI outputs.

GPT is an architecture for autoregressive language models. Models are pre-trained on huge text corpora and can generate coherent text. ChatGPT, GPT-4, and many alternatives are based on this paradigm.

Generative AI creates new content—text, images, code, audio—instead of merely classifying or predicting. It powers ChatGPT, image generators, code assistants, and many enterprise applications.

Grounding ensures AI responses are based on verifiable sources—documents, databases, or APIs—rather than model memory. RAG is a primary technique for grounding; it reduces hallucinations.

Hallucination occurs when an LLM produces plausible-sounding but false or fabricated information. RAG, grounding, and confidence scoring help reduce hallucinations in production systems.

Headless architecture decouples content management from front-end presentation. Content is delivered via APIs, enabling omnichannel experiences and easier integration with AI and personalization.

Human-in-the-loop (HITL) ensures humans review, approve, or correct AI outputs before they have impact. Critical for high-stakes decisions and compliance with regulations like the EU AI Act.

Hyperautomation automates entire business processes by combining RPA, AI, workflow orchestration, and integration. It goes beyond single-task automation to transform end-to-end operations.

Inference is when a trained model is used to produce outputs—predictions, classifications, or generated content. It happens in production; optimizing inference (latency, cost) is key for scalability.

Integration connects AI capabilities with existing enterprise systems—ERP, CRM, data warehouses, APIs. Seamless integration is essential for AI to deliver value in real workflows.

Intent is the user's goal or intention extracted from their input—e.g., 'book a flight' or 'request a refund.' Intent recognition drives routing and response selection in chatbots and voicebots.

The Internet of Things (IoT) refers to connected devices and sensors that collect and transmit data. IoT data feeds predictive models, anomaly detection, and autonomous systems in manufacturing, logistics, and smart buildings.

Jailbreaking refers to techniques used to bypass safety guardrails in AI models—to elicit harmful, biased, or restricted content. Robust AI governance includes monitoring and hardening against such attacks.

A knowledge base is a structured repository of organizational knowledge—documents, FAQs, policies. It powers RAG systems and AI assistants that answer questions from company data. Thinkia Knowledge Core is an example.

A knowledge graph represents information as a network of entities and their relationships. It enhances retrieval and reasoning in RAG by capturing structure and semantics beyond plain text.

Knowledge retrieval finds and returns the most relevant documents or passages for a user query. It uses semantic search (embeddings) and sometimes hybrid approaches; it is the 'R' in RAG.

Large Language Models (LLMs) are neural networks trained on enormous text corpora. They generate coherent text, answer questions, and perform many NLP tasks. Examples: GPT-4, Claude, Llama, Mistral.

MLOps (Machine Learning Operations) applies DevOps practices to ML—CI/CD for models, monitoring, versioning, and rollback. Essential for reliable, scalable AI in production.

Machine learning (ML) enables systems to learn patterns from data without explicit programming. It includes supervised, unsupervised, and reinforcement learning. ML is the foundation of modern AI.

Metadata describes other data—authors, dates, categories, tags. It enables filtering, faceted search, and better organization in RAG and knowledge management systems.

Multimodal AI handles multiple data types—text, images, audio, video—often in the same model. It enables richer applications like image understanding, video analysis, and cross-modal search.

Natural Language Processing (NLP) enables machines to understand and generate human language. It covers translation, summarization, sentiment analysis, chatbots, and many LLM applications.

Omnichannel delivers a consistent, unified customer experience across web, mobile, voice, chat, and in-person. AI enables personalization and context continuity across channels.

Orchestration coordinates multiple AI services, agents, and models to execute complex workflows. Thinkia Synapse is an orchestration platform that unifies governance, routing, and execution.

Overfitting occurs when a model learns training data too closely, including noise, and performs poorly on new data. Regularization, validation, and more data help prevent it.

Parameters are the numerical values a model learns during training. LLMs have billions of parameters, which encode knowledge and capabilities. Model size (e.g., 7B, 70B) refers to parameter count.

A pipeline is a sequence of processing steps—e.g., data ingestion → preprocessing → model inference → post-processing. AI systems are built as pipelines for reliability and scalability.

An AI platform centralizes models, agents, workflows, and governance in one place. Thinkia Synapse is an enterprise AI platform for orchestration, security, and cost control.

Predictive analytics uses historical data and ML to forecast future outcomes—demand, churn, equipment failure, sales. It enables proactive decision-making and automation.

Proactive AI anticipates needs and takes action without explicit user requests—e.g., alerting, recommendations, automated workflows. It shifts AI from reactive to anticipatory.

Production is the live environment where an AI system serves real users. It requires monitoring, scaling, security, and compliance—beyond what development or staging environments need.

A prompt is the text (and sometimes images) given to an LLM as input. It instructs or contextualizes the model's response. Prompt engineering optimizes prompts for quality and consistency.

Prompt engineering is the practice of crafting prompts to elicit better, more reliable outputs from LLMs. It includes techniques like few-shot examples, chain-of-thought, and structured output formatting.

RAG augments LLM generation with retrieved documents. Given a query, the system fetches relevant passages from a knowledge base, then passes them to the LLM as context. This grounds answers and reduces hallucinations.

ROI (Return on Investment) measures the financial return from an investment. For AI projects, it's critical to demonstrate measurable value—cost savings, revenue growth, efficiency gains—to secure and sustain investment.

RPA automates rule-based, repetitive tasks—e.g., data entry, form filling. AI augments RPA with judgment and adaptivity, leading to hyperautomation across complex processes.

Reinforcement learning (RL) trains agents through reward signals. The agent takes actions, receives feedback (reward/penalty), and learns to maximize cumulative reward. Used in robotics, gaming, and optimization.

Responsible AI is developed and deployed with consideration for ethics, legal compliance, and social impact. It includes fairness, transparency, accountability, and privacy.

Retrieval is the step in RAG (and search systems) that finds the most relevant documents or text passages for a user query. It typically uses embeddings and vector similarity search.

Semantic search finds content by meaning rather than keyword matching. It uses embeddings to compare query and documents by conceptual similarity, improving recall and relevance.

Sentiment analysis classifies the emotional tone of text—positive, negative, neutral, or more granular emotions. Used in customer feedback, social listening, and brand monitoring.

Thinkia Synapse is the unified agentic platform for enterprise AI. It orchestrates agents, models, and workflows with centralized governance, security, and cost control. Your central nervous system for AI.

A token is the basic unit of text that a model processes. In English, roughly 4 characters or 0.75 words per token. Token count drives cost and context window limits for LLM APIs.

Training is the process of teaching a model from data. Pre-training learns general language; fine-tuning adapts to specific tasks or domains. Training requires compute, data, and expertise.

Transfer learning applies knowledge from one task or domain to another. Pre-trained LLMs transfer to new tasks via prompts or fine-tuning, reducing the need for large task-specific datasets.

A use case is a specific business scenario where AI is applied—e.g., customer service automation, document summarization, predictive maintenance. Identifying and prioritizing use cases is key to AI strategy.

A vector is a list of numbers that represents data—e.g., text as an embedding. Similar content yields similar vectors; this enables semantic search and retrieval in RAG systems.

A vector database stores embeddings and supports fast similarity search. Given a query embedding, it returns the most similar stored vectors. Essential for RAG and semantic search at scale.

A voicebot is an AI assistant that interacts via speech—both understanding and generating voice. Used in call centers, IVR, and hands-free applications.

A workflow is a sequence of steps—often automated—that accomplishes a business process. AI workflows can include retrieval, generation, API calls, and human review. Synapse orchestrates complex AI workflows.

Explainable AI (XAI) provides interpretable explanations for model outputs. It builds trust, supports debugging, and meets regulatory requirements for transparency in high-stakes decisions.

Zero downtime means systems run without unplanned interruptions. Predictive AI can forecast failures and enable proactive maintenance, supporting zero-downtime operations in critical environments.

Zero-shot learning means a model can perform a task without being shown examples during training or in the prompt. LLMs often exhibit zero-shot capabilities for many NLP tasks.