Shofo uses AI to automatically watch, chop up, and label millions of videos so AI researchers don't have to do it by hand.

Shofo's engineering team deploys advanced computer vision models — including object detection, activity recognition, scene segmentation, and temporal analysis — to automatically process millions of hours of raw video content into structured, labeled datasets. These pipelines ingest video from multiple social media platforms and public sources, apply frame-level and clip-level annotations (e.g., objects present, actions occurring, scene type, text overlays, audio transcription), and output richly tagged datasets via API. The system combines fully automated ML inference with human-in-the-loop quality assurance for edge cases, ensuring high label accuracy at massive scale. This infrastructure is the backbone of Shofo's value proposition: customers receive ready-to-use training data without building their own annotation pipelines, dramatically reducing time-to-model for AI labs and enterprises.

It's like hiring a million interns who can watch every video on the internet simultaneously, take perfect notes on everything they see, and file it all neatly before lunch.

Shofo lets you search millions of videos by typing what you're looking for in plain English — like Google, but for the inside of every video ever posted.

Shofo's product team has built a multimodal semantic search engine that allows users and API consumers to query the entire video library using natural language descriptions, keywords, or even image/audio inputs. Under the hood, the system encodes video frames, audio, transcripts, and metadata into a shared embedding space using transformer-based models (likely CLIP-family or proprietary multimodal encoders), enabling cross-modal retrieval — e.g., a text query like "person riding a bicycle on a beach at sunset" returns matching video clips ranked by semantic similarity. The search infrastructure leverages approximate nearest neighbor (ANN) indexing for real-time performance at scale. This capability is a direct evolution of the team's prior work on Correkt, their multimodal AI search engine, and represents a core product differentiator: no competitor offers semantic search across a video library of this scale with sub-second latency and cross-modal flexibility.

It's like having a librarian who has personally watched every video on the internet and can instantly find the exact 12-second clip you're thinking of based on a vague description.

Shofo uses AI to read and understand every social media post and profile, automatically tagging who's mentioned, what's being discussed, and how people feel about it — so companies can make smarter decisions faster.

Shofo's data team operates an intelligent extraction and enrichment pipeline that ingests raw data from major social media platforms (TikTok, LinkedIn, Instagram, X) and applies NLP models for named entity recognition (NER), sentiment analysis, topic classification, and relationship extraction. The pipeline transforms unstructured text, captions, bios, and comments into structured, queryable fields — e.g., extracting company names, job titles, product mentions, hashtag trends, and emotional tone from millions of posts and profiles. These enriched datasets are served via Shofo's API, enabling customers to build social listening dashboards, competitive intelligence tools, influencer analytics, and market research applications without building their own NLP infrastructure. The system is designed for continuous ingestion and near-real-time enrichment, with automated quality monitoring and drift detection to maintain accuracy as platform content evolves.

It's like having a team of analysts who read every single social media post in the world, highlight the important parts, and hand you a perfectly organized spreadsheet — updated every few minutes.