AI Glossary

AI Enablement is the process of preparing businesses to productively use AI. This includes training, strategy, tool selection and implementation. The goal is to establish AI as an integral part of business processes rather than an experiment.

An API (Application Programming Interface) is an interface through which two software systems communicate. In AI context: APIs send data to LLMs and receive results. Example: your ERP sends customer data to Claude, Claude generates a quote, the ERP saves it.

Claude is an LLM by Anthropic optimized for safety and reliability. Claude is particularly suited for enterprise applications: document analysis, code generation, customer service and process automation. w2 Labs uses Claude as the primary AI model for implementations.

Digital Maturity describes how far a company has progressed in its digital transformation. Assessment categories: website quality, social media presence, tools in use, process digitization and GDPR compliance. A score of 1-10 shows where the company stands and where the biggest levers are.

Docker is a platform that runs applications in isolated containers. Each container includes everything the application needs. Relevant for SMEs: Docker enables running AI tools like n8n, Supabase or custom chatbots on your own server. This saves cloud costs and increases data control.

The General Data Protection Regulation governs the handling of personal data in the EU. For AI projects this means: customer data must not be sent to US servers without explicit consent. GDPR-compliant AI relies on European servers, self-hosted models or contractually secured providers.

ERP Integration connects an Enterprise Resource Planning system (e.g. Weclapp, SAP, Microsoft Dynamics) with AI tools and automations. Typical use cases: automatic quote generation from ERP data, inventory monitoring with alerts, customer data synchronization between systems.

An AI Audit is a structured analysis of a company's existing processes to identify automation potential. The result is a report with specific measures, priorities and estimated savings in euros per year.

AI Automation uses artificial intelligence to execute recurring business processes without manual intervention. Common applications: email classification, quote generation, data entry, social media posting. Modern tools like n8n enable connecting different systems through workflows.

An AI Chatbot uses a Large Language Model to answer customer queries in natural language. Unlike rule-based chatbots, it understands context and can respond flexibly. Use cases: customer service on websites, internal helpdesk, FAQ automation. Can be implemented GDPR-compliant via self-hosted models.

AI Readiness describes how well prepared a company is for AI adoption. Factors include: data quality, IT infrastructure, digital literacy of employees and management buy-in. An AI Readiness Score of 1-10 provides a quick assessment.

An AI Strategy defines how a company uses AI to achieve business goals. It answers: Which processes do we automate first? Which tools do we use? How do we train the team? How do we measure success? A good AI strategy starts with quick wins and scales gradually.

A Large Language Model is an AI model trained on massive amounts of text that can understand and generate natural language. Well-known examples: Claude (Anthropic), GPT (OpenAI), Gemini (Google). LLMs are used in SMEs for text generation, classification, summarization and data extraction.

The German Mittelstand comprises companies with 10 to 500 employees. They form the backbone of the German economy. For AI projects, the Mittelstand is particularly interesting: enough process volume for automation, but often still manual workflows. Typical savings potential through AI is 15-40% of process costs.

n8n (pronounced: n-eight-n) is an open-source workflow automation platform. It connects different apps and services through visual workflows. Compared to Zapier or Make, n8n offers more flexibility, can be self-hosted (GDPR compliant) and has no limit on the number of workflows.

Prompt Engineering is the art of crafting instructions for AI models to reliably produce desired results. In a business context this means: creating system prompts for recurring tasks that work consistently and error-free. Good prompt engineering can improve AI output quality by 50-80%.

RAG is a technique where an LLM retrieves relevant documents from a knowledge base before generating an answer. This allows the AI to access company-specific information without needing to train it into the model. Ideal for internal chatbots, document search and knowledge management.

RPA uses software robots that mimic human actions in digital systems: clicking, copying, pasting, filling forms. Unlike AI automation, RPA follows fixed rules. In practice, RPA and AI are often combined: RPA handles the routine, AI makes the decisions.

Supabase is an open-source alternative to Firebase. It provides a PostgreSQL database, authentication, real-time subscriptions and file storage. Supabase can be hosted in the EU, making it GDPR compliant. w2 Labs uses Supabase as a backend solution for client projects.

A Vector Database stores text as mathematical vectors (embeddings). This enables semantic search: not by exact words but by meaning. Searching for 'vacation request' also finds 'time-off' or 'absence notification'. Supabase offers pgvector as an integrated solution.

Workflow Automation automates work processes across multiple systems. Example: A contact form is submitted, data goes to CRM, a confirmation email is sent and sales gets notified. Tools like n8n or Make connect these steps without programming.