How your teams get defensible answers from every piece of content.

Multimodal AI refers to AI systems that can process and analyze multiple types of data, video, audio, text, and images, simultaneously. Unlike AI that handles only one format, multimodal AI reads all content types together and can return results that draw from different formats in a single response. VIDIZMO Metis is a multimodal AI platform: it analyzes video frame by frame with computer vision, transcribes audio in 82 languages, extracts text from documents, and indexes everything into one searchable system.

Multimodal AI runs different AI models on different content types in a coordinated pipeline. Computer vision models analyze video frames; speech recognition models transcribe audio; OCR and NLP extract meaning from documents. A unified indexing layer connects all outputs so a single query retrieves relevant results from any format. In VIDIZMO Metis, this entire pipeline runs inside your own infrastructure, no content is sent to external servers for processing.

A self-hosted AI platform runs entirely within your own infrastructure, on your servers, in your private cloud, or in your on-premises data center, rather than sending data to a vendor's cloud environment. With self-hosted AI, your data never leaves your network, AI models run locally, and you control access, security, and governance. VIDIZMO Metis is designed as a self-hosted AI platform for organizations where data residency and sovereignty are non-negotiable, law enforcement, healthcare, federal government, and financial services.

Cloud AI sends your data to a vendor's servers for processing, your content leaves your network. Self-hosted AI processes data inside your own environment using models you control. For law enforcement, healthcare, and government organizations, self-hosted AI is required or strongly preferred because it satisfies data sovereignty requirements, meets CJIS Security Policy mandates, and eliminates the risk of sensitive data being processed in third-party cloud environments. In VIDIZMO Metis, AI models run locally via Ollama or VLLM, nothing is sent externally unless your policy specifically permits it.

An enterprise AI analysis platform architecture has several layers: an ingestion layer for multiple content formats; processing layers that run specialized AI models (computer vision for video, speech recognition for audio, OCR for documents); a unified indexing layer that makes all outputs searchable; a query layer that accepts natural language and returns sourced answers; and an agent layer that automates repeatable workflows. In VIDIZMO Metis, all of these layers run inside your network, every response includes citations to the exact source document, page, or video timestamp.

Most AI platforms process the audio track of video, transcribing what was said. VIDIZMO Metis runs computer vision on every frame of the video simultaneously, detecting faces, objects, vehicles, license plates, weapons, and physical activities with precise timestamps. The audio is also transcribed in 82 languages with speaker identification. Both outputs are indexed together. A search for a vehicle description returns results from visual frame analysis and from transcripts, in a single query.

Yes. VIDIZMO Metis is designed for on-premises deployment with CJIS compliance as the default architecture. All AI processing happens inside your network via self-hosted models, no criminal justice information is transmitted externally. The platform includes audit logging, role-based access controls, AES-128 encryption at rest and in transit, and FIPS 140-2 validated cryptographic modules, all CJIS Security Policy requirements. On-premises, Azure Government, and AWS GovCloud deployment options are all supported.

VIDIZMO Metis supports both cloud AI models and self-hosted AI models in the same platform. Cloud: OpenAI GPT series, Anthropic Claude, Google Gemini. Self-hosted: Ollama and VLLM, running any compatible open-source model inside your network. Administrators configure which model runs for which workflow, self-hosted for sensitive data, cloud models for non-sensitive analysis where policy permits. Organizations with strict data residency requirements can run entirely on self-hosted models.