System Core V1.0 Active

Forensic Video Intelligence at Scale.

DeepMatch AI deploys state-of-the-art neural architecture to identify, index, and match complex visual patterns with sub-second latency. A clinical approach to video copyright enforcement.

DeepMatch AI — Neural network visualization with forensic data panels
0 Supported Platforms
99.9% Match Accuracy
< 50ms Query Latency
512-dim Vector Embeddings

Operational Pipeline

A deterministic, four-stage architecture ensuring absolute data integrity from raw ingest to final analytical match.

input
STAGE 01

Data Ingestion

Multi-platform video intake via yt-dlp + OpenCV. Supports 1000+ platforms, IPTV streams (HLS/RTSP/RTMP/UDP), local files, and direct URLs.

hub
STAGE 02

AI Embedding

CLIP ViT-B-32 encodes each frame into a 512-dimensional vector — an immutable digital fingerprint resilient to all transformations.

account_tree
schema
STAGE 03

Vector Indexing

FAISS IndexFlatIP with Cosine Similarity for optimized spatial querying across millions of vectors in milliseconds.

radar
STAGE 04

Forensic Matching

Probabilistic nearest neighbor matching yielding confidence-scored forensic intelligence reports with SHA-256 evidence hashing.

> MATCH_FOUND: VIO-20260427-001
> CONFIDENCE: 99.98%
> EVIDENCE_HASH: SHA-256
terminal Live Terminal

Experience DeepMatch AI

Enter the original video URL and suspected infringing video URL for AI analysis.

Ready
DeepMatch AI v1.0 — aideepmatch.com
System ready. Enter URLs and click "Start Scan".