Glossary

Terms from our articles, briefly explained

RAG, IDP, MCP, agentic workflows: the recurring terms in our posts, each with a definition, practical context, and links to relevant articles and solutions.

I

IDP

Also known as: Intelligent Document Processing

Intelligent Document Processing - automated processing of documents with AI: classification, extraction of structured fields, and handover to downstream systems.

Intelligent Document Processing (IDP) refers to the automated handling of documents with the help of AI. Unlike plain OCR, it goes beyond reading text from a scan: the content is understood, classified, and turned into structured fields.

Not to be confused with IdP (Identity Provider)

The acronym IDP shows up in two very different worlds. Here we mean document processing. Identity Providers (usually written as IdP) are systems used for authentication and single sign-on.

Typical steps

Classification: which type of document is this (invoice, contract, official notice, letter)?
Extraction: which fields should be read out (amounts, dates, counterparties, reference numbers)?
Validation: plausibility checks, four-eyes review, and fallbacks for low-confidence results.
Handover: delivering the results to a DMS, ERP, line-of-business application, or workflow engine.

What sets a good solution apart

A reliable IDP setup stands or falls with data quality and handling of edge cases. A clean intake channel, good training data, clear stop criteria, and a human review step for uncertain cases matter more than the choice of model.

M

MCP

Also known as: Model Context Protocol

Model Context Protocol - an open protocol that lets language models and coding agents access external tools, data sources, and actions in a standardized way.

The Model Context Protocol (MCP) is an open standard that describes how a language model or agent can talk to external tools and data sources in a structured way. Instead of coding every integration by hand, an MCP server advertises its capabilities, and a client - Claude or your own agent - can use them right away.

What MCP is aiming at

Language models only become useful at work when they can read your own data and trigger actions: look into a DMS, search a knowledge base, book a slot in a calendar. MCP provides a standardized interface for that, independent of the specific model.

What an MCP server offers

Tools: clearly described actions the agent can invoke.
Resources: data the agent is allowed to read, such as documents or records.
Prompts: prepared building blocks the agent can use as templates.

Why it matters in practice

For mid-sized companies, MCP reduces the integration effort between AI systems and existing applications. An MCP server in front of an existing API is often shorter and more stable than a hand-built adapter per AI tool.

R

RAG

Also known as: Retrieval-Augmented Generation

Retrieval-Augmented Generation - an approach where a language model answers based on retrieved documents rather than relying only on its training data.

Retrieval-Augmented Generation (RAG) augments a language model's answer with content from a dedicated document source. Instead of relying only on the knowledge baked into the model, the system first retrieves a small set of relevant passages from an index (retrieval), then has the model generate an answer grounded in those passages (generation).

Why it matters

In a business setting the relevant knowledge usually does not live inside the model - it lives in case files, contracts, wikis, and email threads. RAG combines both: the language understanding of the model with the concrete, current source material from the company. This reduces the risk of fabricated answers and makes statements traceable.

Typical building blocks

Ingest pipeline: extracting and preparing documents, often including OCR and cleanup.
Chunking: splitting documents into passages that can be indexed individually.
Embedding index: vector representation of the chunks in a vector database.
Retrieval step: semantic search for relevant chunks, often with re-ranking.
Grounded generation: the model produces an answer and points to the supporting passages.

Common pitfalls

A pilot on sample data is far easier than running RAG in production. Permissions, versioning, OCR quality, and traceable citations decide whether a RAG system is actually accepted in day-to-day work.

IDP