KI-Agentur AI glossary

An AI MVP is the first real, usable version of an AI solution with the smallest feature set that already creates value.

An AI prototype is an early, testable version of an AI idea that shows a narrow case on real data.

Artificial intelligence is the umbrella term for software that handles tasks we usually expect human judgement for: understanding language, spotting patterns, making suggestions, preparing decisions.

Deep learning is a subfield of machine learning that uses deep neural networks with many layers.

Generative AI is the part of artificial intelligence that produces new content rather than only classifying: text, images, code, audio or structured drafts.

Large language models, LLM for short, are AI models trained on huge amounts of text that understand and generate language.

Machine learning is an approach within artificial intelligence where a model learns patterns from data instead of receiving them as hand-written rules.

Multimodal AI processes several kinds of data in one model: text, image, sound, video or tables at once.

Narrow AI is specialised on a clearly bounded task, such as sorting text or recognising images.

Neural networks are computational models loosely inspired by the structure of the brain.

Vibe coding describes programming where a human describes in natural language what they want and an AI model produces the code.

AI adoption describes the degree to which an AI solution is actually used in daily work and has effect.

An AI agency is a provider that delivers AI initiatives from selection to running operation: workshop, prototype, implementation and development from one hand.

AI change management covers all measures that make sure an AI result is actually used in daily work.

AI consulting is the expert guidance for selecting, assessing and introducing AI in a company.

An AI hackathon is a compact, time-boxed format in which a team works on one or more AI use cases and builds a working prototype in a short time.

AI literacy is the ability of people and teams to assess AI sensibly, use it and recognise its limits.

AI readiness describes how well a company is prepared to use AI productively.

An AI roadmap is a time-ordered plan of a company's AI initiatives.

An AI strategy describes where and why a company uses AI and in which order.

AI transformation is the change through which AI becomes firmly anchored in a company's processes, roles and decisions.

An AI use case is a concretely described application in which AI solves a clearly bounded problem.

An AI workshop is a compact working format in which a use case is sharpened, planned and turned into a first prototype.

Build vs.

A proof of concept is a small, targeted test that shows whether an AI approach works in principle for a concrete case.

Agent orchestration is the coordination of several agents and tools into a reliable chain.

Agentic AI describes an approach where AI systems pursue goals on their own, plan actions, use tools and adapt their approach to feedback.

An AI agent is a software system that breaks a goal into steps, uses tools for them and judges intermediate results itself, instead of only answering single questions.

The context window is the maximum amount of text, measured in tokens, that a model can take into account at once for a request.

Embeddings are sequences of numbers that capture the meaning of a text, image or record so that similar things sit close together.

Fine-tuning is the deliberate retraining of a pretrained model with your own examples, so it handles a task more reliably or in a specific style.

A foundation model is a large model pretrained on broad data that serves as the base for many different applications.

Function calling is a language model's ability to trigger a predefined function with matching parameters instead of only producing text.

A hallucination is an output from a language model that sounds fluent and convincing but is factually wrong or entirely invented.

Inference is the process where a trained AI model turns an input into an answer.

LLMOps describes the practices and tools for reliably operating applications built on large language models.

The Model Context Protocol (MCP) is an open standard that connects an AI model to external tools and data sources.

A multi-agent system is a setup where several specialised AI agents work together to solve a task.

Open-source models are language models whose weights are openly available, so they can be downloaded, adapted and self-hosted.

Prompt engineering is the deliberate design of the instruction to a language model, so it solves a task reliably and in the desired format.

RAG (Retrieval Augmented Generation) connects a language model to a search over your own documents, so answers rest on checked sources instead of the model's memory alone.

Tokens are the small building blocks a language model breaks text into, usually word parts or short character sequences.

A vector database is a store that holds embeddings and searches them for closeness in an instant, meaning by content similarity rather than by exact values.

An AI answering machine takes calls when no one is reachable, captures the request in the conversation and passes it on in a structured way.

An AI assistant is software that understands requests in natural language, uses knowledge and tools for them, and completes a task for the user.

An AI phone assistant answers incoming calls, understands the request and handles reception: booking appointments, giving information, routing to the right person.

A chatbot is a text-based dialogue program that answers user questions in one channel, usually as a widget on a website or in a messenger.

A company brain is the central knowledge layer of an organisation, prepared for access by AI.

Conversational AI is the umbrella term for technology that holds dialogues in natural language with people, by text or voice.

A copilot is an AI assistant that sits directly inside a work tool and accompanies a person at their task.

Inbound automation gathers all incoming requests across phone, chat, email and form and handles them with AI: take, understand, triage and where possible answer directly.

Outbound qualification is the active outreach to contacts with AI to find out whether and how they fit the offer.

Speech recognition, in English speech-to-text or STT, converts spoken language into written text.

Text-to-speech, TTS for short, converts written text into spoken language.

A voice agent is an AI system that leads a spoken conversation on its own and completes tasks in the process.

A voicebot is a voice-controlled dialogue system that guides callers through fixed flows and queries simple requests or routes them.

AI automation combines classic flow control with AI models that understand unstructured input and prepare decisions.

An API integration connects two systems through their official programming interface, so they exchange data and trigger actions in a structured way.

Exception handling is the planned treatment of cases that deviate from the expected flow, that is errors, exceptions and incomplete data.

Hyperautomation describes an approach where an organisation automates many flows deliberately and with several techniques, instead of solving only single tasks.

Intelligent document processing, IDP for short, reads documents and understands their content, so fields and meaning can be processed in a structured way.

Make is a hosted automation platform where flows take shape as visual scenarios built from connected modules.

n8n is an automation platform that lets you assemble flows visually from nodes that connect systems and pass data on.

Process automation means mapping a whole business process with its rules, roles and exceptions technically, so it runs reliably and traceably.

RPA stands for robotic process automation and describes software robots that mimic human clicks and inputs in existing interfaces.

A webhook is a message a system sends automatically to a set address the moment a certain event occurs.

Workflow automation connects individual work steps across system boundaries into one continuous flow that runs without manual hand-off.

Zapier is a hosted automation service that connects apps through simple rules, usually in the pattern when this happens, do that.

An AI audit is the systematic review of an AI system for quality, fairness, data protection and compliance with applicable rules.

AI compliance means that an AI system follows the applicable rules and can prove it.

AI governance is the set of rules an organisation uses to deploy AI responsibly.

The AI literacy obligation from Article 4 of the EU AI Act requires providers and deployers to ensure a sufficient level of AI literacy among the people who operate or use AI systems.

AI risk classes are the levels by which the EU AI Act sorts applications according to their degree of danger.

Bias in AI is a systematic skew in a model's results that treats certain groups or cases unfairly.

Data protection and AI describes the responsible handling of personal data in AI systems.

Data sovereignty is an organisation's control over its own data: who accesses it, where it sits, how it is used and whether it can be reclaimed.

The EU AI Act is the European Union regulation for governing AI systems.

EU hosting means that data and applications run on servers within the European Union, under the law applicable here.

Explainable AI covers methods that make it traceable how an AI system reaches a result.

Guardrails are rules and checks that constrain what an AI may output or do.

Human in the loop describes a workflow in which a person checks, approves or corrects AI results before they take effect.

Prompt injection is an attack in which crafted text overrides or redirects the instructions of an AI system.

A data pipeline is a chain of automated steps that moves data from source to target: ingest, check, transform, combine and store.

Data quality describes how well data suits a given purpose.

Document extraction is the automatic reading of structured fields from unstructured documents such as invoices, contracts, delivery notes or forms.

A knowledge base is an ordered collection of knowledge that an AI assistant can draw on reliably.

A knowledge graph is a data structure that represents things as nodes and their relationships as edges.

Retrieval is the step where a system finds the passages relevant to a query from a knowledge source and passes them to the model.

Synthetic data is artificially generated datasets that resemble real data in structure and distribution without depicting actual individual cases.

Unstructured data is information without a fixed schema, such as emails, PDF documents, images, contracts or meeting notes.

AI implementation is the step where a validated AI use case moves into real systems, data flows and responsibilities.

AI monitoring is the ongoing observation of an AI solution in operation.

AI operation is the ongoing phase in which a deployed AI solution works reliably, stays monitored and remains maintained.

An AI pilot is the limited use of an AI solution in real operation, bounded in time and scope, to check with clear criteria whether it holds.

AI ROI describes the ratio between the benefit of an AI solution and its total costs.

A baseline is the measured starting value of a process before an AI solution changes it.

Business process management (BPM) is the structured design, steering and improvement of business processes across their entire lifecycle.

Process mining reconstructs the actual flow of a process from the event data of the systems involved.

Scaling AI is the step from one proven use case to many, without quality, cost or operation running out of control.

Total cost of ownership (AI) is the sum of all costs of an AI system over its lifetime, not just the build.