AI-driven anomaly detection for multi-site energy grids.
Energytech teams cannot achieve real-time oversight of distributed grid assets because IoT platforms lack multi-site deployment support.
VoltSight uses AI to detect anomalies in real-time across distributed grid assets, alerting teams before issues escalate. It supports multi-site deployments by normalizing data from various IoT sources. Teams gain predictive insights without rebuilding their infrastructure.
Energytech teams managing distributed enterprise grid assets across multiple sites
Out-of-box AI models tuned for grid-specific anomalies like voltage spikes.
professional
ML-based detection of outliers in metrics across sites.
Prioritized notifications with root cause suggestions.
Federate data from all sites for global anomaly views.
Log and analyze past incidents.
Easy connect to IoT platforms.
Forecast potential failures.
Channel notifications.
User-trained models.
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| name | text | No |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| org_id | uuid | No |
| name | text | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| site_id | uuid | No |
| score | float | No |
| resolved | bool | No |
| timestamp | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| site_id | uuid | No |
| metric_type | text | No |
| threshold | float | No |
Relationships:
/api/sitesList sites
/api/anomaliesFetch recent anomalies
/api/anomalies/:id/resolveMark as resolved
/api/webhook/dataIngest IoT data for AI
/api/baselinesUpdate thresholds
50 anomalies/mo
500 anomalies/mo
Unlimited
| Month | Users | Conversion | MRR | ARR |
|---|---|---|---|---|
| Month 1 | 80 | 4% | $96 | $1,152 |
| Month 6 | 700 | 7% | $1,470 | $17,640 |
AI-powered multi-site detection for energy assets.
Target 'IoT energy' keywords on LinkedIn, offer free AI audits. Share anomaly detection demo on Energy Central forums. DM attendees from DER conferences.
Visual IoT
No built-in AI for grids
Grid-tuned AI
Anonymized anomaly data moat for improving ML models.
AI maturity + rising DER failures due to decentralization.
ML false positives
User feedback loops
Data privacy regs
SOC2 compliance path
Success: 80% validate AI need
Success: 20 waitlist
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