00:00
Create the project
Two plain sentences describing what to look for in warehouse footage. Nothing technical, nothing structured.
The world comprehension layer between reality and reasoning.
EndlessAI's continuous situational awareness engine reviews every video frame, understands what is happening, preserves context over time, and determines what matters and how to act on it to accomplish real-world tasks.
Input
Live streams, archives, UGC, audio, sensors, operational data
Time to value
First actionable output in minutes, from an empty project
Reasoning
Model-agnostic: cloud frontier models or local open models
Output
Evidence-backed alerts, answers, reports, and automations
The Architecture
World Comprehension
On-Demand
World comprehension means maintaining a continuous understanding of cause, effect, and context in a real-world environment over time — all of which can change and evolve in a fraction of a second. EndlessAI delivers that understanding. Off-the-shelf models provide the reasoning support. Together, they see what is happening, understand why it matters, and turn expert insights into policy-guided decisions.
01 Input
Reality
observe
- Live cameras — RTSP, HLS, Edge Bridge
- Archived video and file libraries
- User-generated content
- Audio and sensors
- Operational and business data
02 EndlessAI
Continuous comprehension
understand
Continuous visual understanding, every frame
People, objects, actions, relationships
Customer rules and policies
Evidence linking and relevance
Temporal context and memory
Change, sequence, and causality
Persistent world state
Model selection, escalation, cost control
This is the layer that does not exist anywhere else: turning continuous visual reality into a persistent understanding that off-the-shelf models can reliably reason over.
03 Reasoning
Off-the-shelf models
judge
- Interpret
- Apply policy
- Explain
- Decide next action
Claude, ChatGPT, Gemini, or open models on your own hardware. We don't train the reasoner — we make it competent on the visual world, and it improves as the models do.
04 Output
Action
act
- Alerts with evidence
- Answers across one video or thousands
- Incident and compliance reports
- Workflow triggers and automations
- APIs, webhooks, SDKs, MCP
- Human review queues
Platform Capabilities
From observation to
operational intelligence
Out of the box
01
Build
Use the platform — or your own coding agents — to build any video-centric application for your organization, described in natural language.
02
Connect
Bring in any video or media from cameras, cloud storage, files, streams, and devices. Live or archived.
03 · The critical layer
Analyze
What actually happens as it unfolds — sequence, cause and effect, severity, and what the footage does not prove. Ask anything in plain language, and every answer names the video, the timestamp, and the frame it came from. This is the work that turns a detection into a decision.
04
Act
Automate alerts, tasks, reports, and workflows, and integrate into the systems your operation already runs on. Evidence and context persist across time, cameras, and projects — and the system improves as it runs.
ROI in minutes, not months.
The Warehouse Safety project is real, and this is the timeline:
00:02
Upload the footage
Five fixed-camera incident clips — 7 minutes 22 seconds of unedited warehouse video.
00:04
Analysis begins
Every frame of every clip, automatically. No configuration step, no model selection, no waiting on anyone.
00:10
Actionable output
Timestamped cause-and-effect reconstructions, severity assessments, and prevention actions — ready to act on.
What this project required
Ten Minutes
- A short description written in plain English
- Five video files, dragged in
- Nothing else
The prompts were deliberately simple. The platform still returned cause-and-effect incident reports detailed enough to act on — because the comprehension layer, not the operator, does the hard part.
How it went
Three steps. That's it.
01
Describe the job
Describe the environment, the problem, the policies that apply, what good and bad look like, and what evidence you need. In your own words. EndlessAI converts that into a working application — standing analysis, alert conditions, tracking behaviour — and you refine it in conversation, not in code.
- Two minutes
- Natural language
- No CV pipeline
- Refine anytime
02
Bring the reality in
Upload files, sync a cloud bucket, or connect a live camera. RTSP and HLS streams are watched continuously; Edge Bridge reaches cameras on a local network without exposing them.
- Upload
- S3 • Drive
- RTSP • HLS
- Edge Bridge
03
Get decision-grade output
Not a label and a confidence score — an evolving presentation of reality as it unfolds. Timeline, cause and effect, severity, prevention actions, and an explicit account of what the footage cannot establish on its own.
- Timestamped
- Cause & Effect
- States its limits
- Evidence frames
Likely safety issues visible in the clip
Based only on what is visible in vid1.mp4, the likely hazards and violations include:
- Unsafe proximity of powered industrial truck to pallet racking
- Rack impact or near-impact risk during turning
- Operation in a narrow aisle with heavily loaded high-bay storage
- Severe struck-by hazard from falling materials
- Potential rack overloading, rack weakness, or prior structural compromise
Cause-and-effect interpretation
A careful, evidence-based interpretation is:
- The forklift turns close to the left-side rack.
- The rack area adjacent to the forklift begins to deform/fail.
- That local failure triggers a rapid progressive collapse of loaded rack sections.
- Stored goods and rack components fall into the aisle.
- The forklift moves away while the collapse continues, with the operator remaining exposed to falling debris.
Alerts that show their work.
Describe a condition in plain English. The platform compiles it into a rule — before → after → notify — and watches for it. Live matches arrive as suspect and are promoted to confirmed only when a second tier verifies them against the evidence. Two false alarms is one too many.
Outcomes, not analytics
Video search finds a clip. EndlessAI determines cause-and-effect and delivers actionable analysis.
Warehouse safety is one configuration of one platform. The same comprehension layer, described differently, produces a different application — with no technology rebuilt underneath it.
Retail & POS
Stop an incorrect transaction
Logistics
Prevent a picking error
Industrial
Escalate a safety hazard
Platforms & UGC
Apply a moderation policy
Insurance & Legal
Document visual evidence
Operations
Initiate a workflow
Workforce
Improve human performance
Anything you can describe
Situations no one predefined
One successful use case unlocks many more.
Adjacent workflows · more cameras · more facilities · additional policies · new business units · entirely new use cases — and the economics improve as you expand.
Built for scale, security, and ROI
Enterprise-grade infrastructure.
Massive scale
Thousands of cameras and millions of hours, with edge + cloud orchestration and adaptive compute.
Cost efficient
Watch inexpensively and continuously. Invoke deep analysis and premium models only when something warrants it.
Enterprise grade
Role-based access, SSO, encryption, audit logs, data residency, and compliance controls.
Open & extensible
APIs, webhooks, SDKs, and MCP for integration with your own systems and agents.
Sovereign capable
Latency-sensitive workloads run on local hardware — reducing bandwidth, and keeping footage where policy requires it.
Access & Alerts
Works everywhere you work.
Personal channels are two-way and work across every project — upload from your phone, ask a question, and get the answer and the alert in the same thread. Delivery channels post one-way into an inbox or a team channel.
Two-way — Ask, upload, get alerts
Slack
Ask, upload, and get alerts in your workspace.
Send video from your phone, get alerts back.
Microsoft Teams
Ask, upload, and get alerts in your Teams channel.
Discord
Ask and get alerts in your server.
One-way delivery — Alerts and reports
Alerts to your inbox.
SMS
A text with a link to the evidence.
Telegram
Alerts and reports pushed to a Telegram chat.
Or work directly in the web app, over the API, or through MCP.