The intelligence layer between raw video and the reasoning models that act on it.
01
Expert-level, full-frame video comprehension.
02
Sees and reasons like a human, scales like software.
03
Zero-code natural language user interface.
04
In production today.
05
Works with all major LLMs (OpenAI, Anthropic, Google).
06
End-to-end proprietary with deep technical moat.
07
Works as an API that builds and powers APIs across verticals.
Fundamentally different.
Other approaches don't actually see the video. EndlessAI does.
How EndlessAI compares across the video intelligence stack.
The landscape
Computer Vision
CV/ML, CNNs
VLMs
Twelve Labs, Moment Labs
Multimodal LLMs
ChatGPT, Gemini
World Models
AMI, World Labs
Visual Intelligence
Video input
Raw video
Sampled frames
Sampled frames
Synthetic / latent
Full frame
Comprehension
Frame-level, predetermined detection
Motion, detection, limited context
Interpolated guesses
Abstract prediction
Full context
Time to market
In production
In production
In production
Years of R&D
In production
Model dependency
—
Proprietary
Walled garden
New architectures
Open
Cost at scale
Low
Moderate
Moderate / high
Billions in training
Low
Human-level accuracy
No reasoning
Limited
Limited
TBD
Yes
Computer Vision
CV/ML, CNNs
Video input
Raw video
Comprehension
Frame-level, predetermined detection
Time to market
In production
Model dependency
—
Cost at scale
Low
Human-level accuracy
No reasoning
VLMs
Twelve Labs, Moment Labs
Video input
Sampled frames
Comprehension
Motion, detection, limited context
Time to market
In production
Model dependency
Proprietary
Cost at scale
Moderate
Human-level accuracy
Limited
Multimodal LLMs
ChatGPT, Gemini
Video input
Sampled frames
Comprehension
Interpolated guesses
Time to market
In production
Model dependency
Walled garden
Cost at scale
Moderate / high
Human-level accuracy
Limited
World Models
AMI, World Labs
Video input
Synthetic / latent
Comprehension
Abstract prediction
Time to market
Years of R&D
Model dependency
New architectures
Cost at scale
Billions in training
Human-level accuracy
TBD
Visual Intelligence
Video input
Full frame
Comprehension
Full context
Time to market
In production
Model dependency
Open
Cost at scale
Low
Human-level accuracy
Yes
Legacy approach
Computer vision & CNNs
Used for decades to detect specific objects or pre-defined events — primarily for surveillance, industrial, and logistics automation. They flag only what they've been programmed to recognize and cannot reason.
Detect → classify → done. No reasoning.
Narrow approach
Video language models
VLMs extract audio, text, and metadata to approximate video content. They produce broad textual summaries — useful for media archiving and search — but too imprecise to extract actionable intelligence for most real-world applications.
● 1 sampled15 dropped
Audio"…stepping out of the vehicle now…"
OCRPARKING · LOT B · 14:22
Metaduration:00:48 · fps:30 · h264
Summary"A person walks across a parking lot."
Extract → summarize. Misses what happens between frames.
Standard Approach
Down Sampling.
This is the approach used by leading frontier models, cloud moderation APIs, and most emerging video analysis tools; sample a subset of frames, pass them to an image-capable model, and ask it to interpret what happened.
Sampling a small fraction of frames and asking the model to reason about a continuous, temporal medium is like reading every twentieth page of a novel and asking for a plot summary. The LLM will produce a confident, well-structured answer — but it will be guessing about everything it did not see.
What breaks when you only look at some of the frames.
01
Missed events
Key activity that occurs between sampled frames goes undetected — a brief flash of explicit content, a split-second gesture, or an escalation across a narrow temporal window.
● eventframe timeline
94%of frames discarded
02
Hallucination
LLMs optimized to produce coherent outputs from incomplete inputs will infer events that did not occur, fabricate details to fill gaps, and present fabrications with the same confidence as grounded observations.
Confidence92%
Accuracy41%
Same tone · different truth
37%fabricated details at high confidence
03
Lost context
The most consequential challenges — accidents, theft, policy violations, malevolent intent — are defined by behavioral patterns over time. Frame-sampling destroys the temporal continuity needed to identify them.
● behavior● samples
1saverage gap between samples
Video content moderation is a human process. AtlasTrust automates it.
UGC videos uploaded daily
Hours/min on YouTube
Teen harmful exposure on TikTok
Brand trust lost
Existing automation doesn't comprehend video.
Hive, AWS Rekognition, Google Cloud Video speed triage through frame sampling — but don't understand video.
The status quo is unsustainable.
Even the best hybrid AI/human-in-the-loop pipelines don't scale in relationship to UGC growth.
Sources: Fortune Business Insights, IAB (2025), TikTok
