Snicket Labs provides two SaaS tools—Match and Assure—that use deterministic video fingerprinting to eliminate duplicate and static footage and to verify broadcast content in near‑real time.
Funding
$4.1M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Media and entertainment organizations accumulate large video libraries that contain duplicate, near‑duplicate, and static footage, leading to excessive storage costs, slower workflows, and limited ability to monetize content.
Solution
Snicket Labs offers two SaaS products that address these challenges with high‑precision fingerprinting technology. Match analyzes video assets to identify exact duplicates, similar edits, and periods of unchanged content, allowing users to retain only the highest‑quality version and eliminate redundant footage, which can cut storage and operational expenses by up to 60 % and reduce AI processing costs and carbon impact. Assure connects playout streams to audience reaction by fingerprinting audio and video in near‑real time (≈2 seconds after playback), providing highly accurate detection of even minor changes between versions and enabling broadcasters to verify ad delivery, protect brand value, and make data‑driven scheduling decisions quickly. Both products are delivered via a web UI, REST API, and optional auto‑ingest pipelines, requiring no AI models—just deterministic mathematical fingerprinting.
Target Audience
Primary customers are broadcasters, media asset managers, and post‑production houses that need to optimise large video libraries and verify linear advertising delivery, as well as AI‑driven metadata enrichment services seeking to reduce input data volume.
Features
- Perceptual fingerprinting that works across formats, codecs, and resolutions to find exact and near‑duplicate video assets
- “Content thinning” that detects static periods in fixed‑rig recordings, isolating unique frames for storage reduction
- Integration with AI enrichment pipelines (Enrich) to send only unique frames, lowering processing costs and carbon footprint by up to 75 %
- Near‑real‑time playout verification (~2 seconds after broadcast) with audio‑visual fingerprinting capable of spotting minute differences such as a single digit change
- API, auto‑ingest (S3 bucket), and intuitive web UI for both Match and Assure, enabling easy integration into existing media workflows
- Fingerprinting‑only approach (no watermarking), preserving original content integrity while allowing pre‑air detection