Turn video into
intelligent embeddings.
Mikshi converts video, audio, images, and text into rich vector embeddings — enabling semantic search, recommendations, classification, similarity detection, and intelligent AI workflows. Build smarter video applications powered by deep multimodal understanding.
Build AI features that understand video context.
Mikshi embeddings capture relationships between scenes, actions, speech, sounds, objects, and meaning — not just keywords or metadata. Move beyond metadata and build applications that truly understand video.
Semantic video search
Personalized recommendations
Similarity matching
Scene clustering
Smart categorization
Content moderation
Context-aware retrieval
AI-powered discovery
One embedding layer across every modality.
Mikshi generates embeddings from multiple forms of input — creating a unified representation of meaning and context. Search and connect information across modalities using a shared semantic understanding layer.
Discover related content automatically.
Mikshi understands contextual similarity between videos — even when scenes look visually different. Enable smarter recommendations and deeper content discovery.
Create AI classifiers using natural language.
Define concepts in plain language and instantly classify videos without traditional training pipelines. Reduce manual annotation and accelerate model development.
Designed for intelligent video products.
Mikshi embeddings enable advanced AI systems across industries and workflows.
Power discovery, recommendations, and archive exploration
Surface contextually-related content across vast libraries without manual curation.
Match ads to contextual moments and sentiment
Place ads only in brand-safe, contextually-aligned scenes — driven by understanding, not tags.
Identify behavioral anomalies and patterns
Cluster similar activity across cameras and time to surface recurring or rare events.
Recommend products and analyze interactions
Power video-driven recommendation and engagement analysis from in-store and online video.
Cluster plays, tactics, and athlete patterns
Group similar movements, sequences, and styles across seasons and athletes.
Recommend content by concept and engagement
Match learners with the videos most likely to advance their understanding and retention.
API-first embedding infrastructure.
Generate embeddings at scale using fast, developer-friendly APIs and SDKs.
- REST APIs
- SDK Support
- Batch generation
- Real-time inference
- Cloud & on-prem
- Vector processing
from mikshi import Client client = Client(api_key="msk_...") # Generate a video embedding embedding = client.embed.create( url="s3://archive/clip.mp4", modalities=["video", "audio", "speech"], ) # Compute similarity against a reference results = client.embed.search( vector=embedding.vector, index="library", top_k=10, )
Build smarter video intelligence systems.
Mikshi embeddings help applications understand video context, relationships, and meaning at scale.