Camera intake
Connect existing cameras, streams, or edge nodes with minimal site changes.
VisionFire
VisionFire, risk in sight
For hazchem, storage, charging, and small-premises sites, VisionFire connects recognition, alerting, response records, and evidence reports into a traceable loop.
Conditioned metricsTraceable alertsHuman reviewNo automated fire-control actuation
96.8%
Accuracy under sample conditions
<3%
Scene-tuned false-alarm target
5 steps
Alert workflow
Human confirmation
Response boundary
1Camera intake
2Fire-smoke recognition
3Alert delivery
4Response records
5Evidence reports
Product workflow
From risk detection to evidence retention, one traceable line
Combines video ingestion, object detection, semantic analysis, and multi-level alerting; metrics are presented with test conditions, while formal site conclusions depend on project records.
Fire-smoke recognition
Recognize suspected smoke, flame, and critical-zone anomalies with sample-condition notes.
Alert delivery
Send risk alerts to the console and duty channel instead of stopping at detection.
Response records
Record confirmation, escalation, response, false-alarm labels, and notifications for review.
Evidence reports
Archive screenshots, clips, handling records, and reports for demos, pilots, and acceptance preparation.
Platform capabilities
Put the core fire-duty jobs into one platform
The site uses real product screenshots and clearly separates decision support, response records, and delivery material boundaries.
Live cockpitDemo entry
Live demo and alert cockpit
Show risk state, camera context, alert status, and demo flow so buyers understand the loop first.
Traceable responseNotification log
Alert detail and response workflow
Preserve alert source, severity, notifications, and response actions for duty review.
Material archiveDelivery prep
Evidence and deployment materials
Prepare demo materials, deployment notes, pilot records, and review reports for pilot delivery.
Decision supportHuman review
AI-assisted judgment materials
Collect risk heatmaps, model outputs, and supporting notes for people to review, not to replace human response.
Scenario coverage
Built around real fire-duty scenarios
Start from three easy-to-understand scenarios, then refine with real samples and site false-positive sources.
Scenario
Hazchem and lithium sites
Specialized negative-sample training addresses steam, welding glare, and complex heat sources to reduce false alarms in high-risk plants.
Scene tuned
False-alarm control
Human review
Response boundary
Quarterly
Model review
Scenario
Charging and storage sites
Focuses on early thermal runaway warnings and headlight suppression for more reliable station-level monitoring.
Project review
Warning window
Real-time
Monitoring mode
API
Integration mode
Scenario
Small-premises scenario
For workshops, timber sites, and small shops where budgets are tight but risk remains real, with an emphasis on affordable deployment.
Standard template
Scenario package
Light deployment
Deployment mode
Policy friendly
Policy fit
Core technical parameters
Metrics are useful only with their conditions
Show sample conditions, site review, and human confirmation together so demo or training records do not become formal acceptance claims.
96.8%
Accuracy under sample conditions
Based on current training and validation records, not an unconditional guarantee for every site.
<3%
Scene-tuned false-alarm target
Requires review against camera angles, lighting, steam, welding glare, and other site factors.
5 steps
Alert workflow
Intake, recognition, delivery, response, and evidence reporting.
Human confirmation
Response boundary
The system assists judgment; authorized personnel make final response decisions.
VisionFire, risk in sight
| Metric | Value | Condition | Why it matters |
|---|---|---|---|
| False positives under complex light | Joint optimization with negative samples and semantic reasoning | Validated against steam, welding glare, and vehicle headlights | Better suited for continuous production environments. |
| Update capability | Quarterly model iteration | Based on field feedback and new samples | Prevents the system from stagnating after go-live. |
| Deployment fit | Private, edge, or API deployment | Select based on IT and security requirements | Works across enterprises, parks, and public-sector platforms. |
Metric
False positives under complex light
- Value
- Joint optimization with negative samples and semantic reasoning
- Condition
- Validated against steam, welding glare, and vehicle headlights
- Why it matters
- Better suited for continuous production environments.
Metric
Update capability
- Value
- Quarterly model iteration
- Condition
- Based on field feedback and new samples
- Why it matters
- Prevents the system from stagnating after go-live.
Metric
Deployment fit
- Value
- Private, edge, or API deployment
- Condition
- Select based on IT and security requirements
- Why it matters
- Works across enterprises, parks, and public-sector platforms.
Deployment options
Supports demos, pilots, and delivery expansion
The site explains deployment direction and delivery preparation without presenting local demos or screenshots as production facts.
Local demo environment
Used for solution walkthroughs, feature review, and internal validation, not as a production claim.
Customer pilot environment
Used for site or pre-release validation; conclusions must come from project records.
Private or edge deployment
Can connect to field devices, edge nodes, and customer networks based on security requirements.
Formal site acceptance, third-party acceptance, standards certification, and production conclusions depend on actual project records and acceptance materials.
Safety boundary
Assist duty teams, never replace statutory fire systems
This boundary is visible in the body copy so customers do not mistake AI alerts for automated fire-control actions.
Does not replace statutory fire systems
The system is for risk recognition, alerting, evidence retention, and response support.
No automated fire-control actuation
It does not connect to manual fire-control panels or automatically issue extinguishing, smoke exhaust, or broadcast commands.
Demo evidence is not formal acceptance
Formal site acceptance, third-party acceptance, and standards certification depend on project records and acceptance materials.