Media Analysis

Automatically analyze images and video material at scale. VisionaryAI uses advanced computer-vision models to detect objects, environments and visual patterns, making it possible to structure and understand large media archives. The system can identify visual elements, generate contextual descriptions and produce structured metadata that makes large collections of images and video searchable and analyzable.

Coaching Analysis

Analyze conversations, interviews and coaching sessions using AI-driven language understanding. VisionaryAI identifies themes, insights, emotional signals and conversational dynamics within dialogue. This enables coaches, researchers and organizations to better understand communication patterns and extract meaningful insights from complex conversations.

Text Analysis

Analyze large volumes of textual data such as documents, reports and email conversations. VisionaryAI identifies patterns, recurring themes, contradictions and key insights across multiple documents. This makes it possible to investigate complex information sets and quickly uncover relationships, trends and critical findings hidden within large document collections.

VisionaryAI Suite Settings

LLM Configuration

The LLM settings control which language model is used for analyzing text, transcriptions and generating structured summaries within VisionaryAI. Users can select model providers, configure model IDs and connect to either local or external AI inference engines. The system is designed to work seamlessly with environments such as LM Studio and other OpenAI-compatible APIs. These settings allow users to control the maximum context length, model capabilities and analysis behavior for different AI tasks. This architecture enables VisionaryAI to run entirely locally, ensuring that sensitive data never leaves the user's environment.

Image Analysis Configuration

The image analysis settings control how VisionaryAI processes and interprets visual data. The system integrates modern computer-vision models such as YOLO, BLIP and CLIP to detect objects, generate visual descriptions and produce contextual metadata. Users can configure:

• object classes to detect
• detection confidence thresholds
• maximum objects per image
• which AI models should be used for analysis These controls allow the analysis pipeline to be adapted for different workflows including media archiving, digital asset management and AI training pipelines.

Semantic Memory

Semantic memory enables VisionaryAI to analyze images based on conceptual similarity rather than only object detection. Using CLIP embeddings, the system can understand visual content on a higher semantic level. This makes it possible to identify visually related images even if they do not contain identical objects. The system can also reference a curated image library to generate more contextual tags and improve classification accuracy over time. By continuously storing analysis results, VisionaryAI gradually builds an intelligent semantic knowledge base of previously analyzed material.

Prompt Configuration

Prompt settings determine how AI models are instructed to analyze different types of media. VisionaryAI uses specialized prompt templates tailored for various analysis tasks such as video interpretation, image description and audio transcription. By selecting different presets, users can control the level of detail in the analysis and the structure of generated reports. This enables fast adaptation of the AI workflow for different domains including research, media analysis, investigations and archiving.

Audio Analysis Configuration

The audio analysis settings control how speech is transcribed and interpreted. VisionaryAI uses Whisper speech-recognition models to convert spoken language into text with high accuracy. Users can configure model size, transcription modes and automatic language detection. The system also supports advanced features such as:

• voice activity detection
• speaker identification (diarization)
• word-level timestamp alignment This makes VisionaryAI especially useful for analyzing interviews, meetings, podcasts and recorded conversations.

Video Analysis Configuration

The video analysis settings control how VisionaryAI processes and analyzes video material. The system can automatically extract keyframes and analyze them using computer-vision models to detect objects, environments and visual events. At the same time, the audio track can be transcribed and analyzed, enabling a fully multimodal analysis pipeline. The final result is a structured report that combines visual analysis, speech analysis and text interpretation within a single system.

Text Analysis Configuration

Text analysis settings determine how VisionaryAI processes and interprets documents and written material. The system can analyze individual documents or large document collections simultaneously to detect patterns, recurring themes and potential anomalies. Users can select different analysis modes depending on the objective, such as deep analysis, summarization or structured report generation. This functionality is particularly valuable when investigating reports, analyzing email conversations or reviewing extensive document archives.

Report Export (PDF / HTML)

Generate structured reports directly from AI analyses and export them to professional formats such as PDF or HTML for documentation, investigation or presentation.

Hardcoded Subtitles

Automatically generate subtitles from speech and embed them directly into video files, enabling accessible and searchable video content.

VTAG Metadata Editor

Create, edit and organize metadata connected to media files using the VTAG format. This allows VisionaryAI to store structured AI-generated insights such as tags, descriptions, transcription data and analytical results alongside the original media files.