diff --git a/docs/01-Planning/01-Technical-preparation-for-docs.md b/docs/01-Planning/01-Technical-preparation-for-docs.md
index a89861b..3d06e81 100644
--- a/docs/01-Planning/01-Technical-preparation-for-docs.md
+++ b/docs/01-Planning/01-Technical-preparation-for-docs.md
@@ -1,4 +1,4 @@
-# Technical preparation for adding documentation
+Vassal: Technical preparation for adding documentation
 
 Before documenting all my research for the planning phase of the app there are several technical preparations to arrange:
 
@@ -7,7 +7,7 @@ Before documenting all my research for the planning phase of the app there are s
 * After preparing the container I will connect it to my self hosted Git(ea) repository to ensure a git flow from the very beginning.
 * To ensure SSL which is recommended for code-server I will use a tunnel with my Pangolin instance and Cloudflare as DNS resolver. This is a temporary solution later it will be changed to a self signed cert with Caddy. 
 
-## Code-Server docker container deployment
+# Code-Server docker container deployment
 
 I use Portainer to setup my docker-compose yaml:
 
diff --git a/docs/01-Planning/02-Product-and-technical-vision.md b/docs/01-Planning/02-Product-and-technical-vision.md
index 7d4c964..b7a5bd0 100644
--- a/docs/01-Planning/02-Product-and-technical-vision.md
+++ b/docs/01-Planning/02-Product-and-technical-vision.md
@@ -1,18 +1,18 @@
-# Project: Vassal
+Vassal: Product and technical vision
 
-## Product vision
+# Product vision
 Vassal is an AI-powered bridge designed to connect physical reality to digital management systems. It functions as an intelligent intermediary that performs image analysis and semantic search, enriching raw data before "serving" it to a primary application via REST API.
 
-## Technical requirements & stack
+# Technical requirements & stack
 
-### Frontend
+## Frontend
 - Framework: Next.js (React)
 
 - Styling: Tailwind CSS + SCSS
 
 - Core Task: Provide a streamlined interface for capturing or uploading photos (single or multi-item) and a dedicated UI for reviewing AI-generated metadata.
 
-### Backend
+## Backend
 - Framework: Spring Boot
 
 - AI Orchestration: Spring AI
@@ -21,20 +21,20 @@ Vassal is an AI-powered bridge designed to connect physical reality to digital m
 
 - Core Task: Managing the lifecycle of an item from initial photo upload to final API export.
 
-### Database
+## Database
 - System: PostgreSQL + pgvector (initial iteration)
 
 - Role: Acts as the vector store for intelligent search and the engine for the image processing
 
-## Functionality
-### Intelligent search
+# Functionality
+## Intelligent search
 Vassal does not rely on exact word matching. Because it stores data in a vector-capable database, users can search via Natural Language Prompts.
 
 Example: Searching for "tool to tighten a bolt" will return a "Wrench," even if the word "bolt" or "tighten" isn't in the item's title.
 
 Process: The search prompt is converted into a vector by the AI, and Postgres performs a "Cosine Similarity" check to find the closest matches in the staging or history tables.
 
-### Image processing workflow (Start to Finish)
+## Image processing workflow (Start to Finish)
 The image processing is the heart of Vassal’s "bridge" functionality. It ensures data quality before it reaches your primary app.
 
 - Ingestion: When a photo is uploaded, the raw image and its initial metadata are saved into a temporary staging table in Postgres.
@@ -49,7 +49,7 @@ The image processing is the heart of Vassal’s "bridge" functionality. It ensur
 
 - Cleanup: Upon successful export, the staged record is either archived or removed from the local database, keeping the bridge lean.
 
-### Deployment
+## Deployment
 Vassal is deployed via Docker Compose for high portability.
 
 - Container 1: Next.js (Web UI)