Long-haul pipeline detection
Auto-recognition on inspection video, 90.8% recall.
Evidence notice: cases, figures and inferred project metadata are unverified scenario illustrations, not established client outcomes, credentials or return promises. Signed contracts define scope, fees, baselines, acceptance and data responsibilities; client business data stays off-chain.
1. Project Overview
- Customer type
- Top-tier Energy & utility groups
- Project duration
- 12–20 weeks
- Tech stack
- Multimodal backbone · Vision / OCR · Speech / ASR-TTS
- Deployment
- Hybrid cloud
2. Business Context & Pain Points
The customer is a Enterprise / Group player in Energy, with the core pain concentrated in "Ops & Production". Manual workflows and point tools could not scale or stay consistent, driving up response time, operating cost and compliance risk. This project delivered an end-to-end AI upgrade for that workflow.
3. Solution
Anchored on "AI + Energy", Global AI DAO integrated the following capability stack into a single solution:
- Multimodal backbone —— Wired into the critical business path across ingestion, inference and human-in-the-loop collaboration.
- Vision / OCR —— Wired into the critical business path across ingestion, inference and human-in-the-loop collaboration.
- Speech / ASR-TTS —— Wired into the critical business path across ingestion, inference and human-in-the-loop collaboration.
- Deployment: Hybrid cloud, balancing data compliance, performance SLA and operability.
4. Quantified Outcomes (Before vs After)
| Metric | Before | After | Delta |
|---|---|---|---|
| ticket response time | 100% | 45% | -55% |
Source metric: -55% ticket response time.
5. Specs & Fit
| Best for | Gas / pipelines / wind / solar / integrated energy |
| Core capabilities | Service / defect detection / O&M forecasting / data governance |
| Deployment | Cloud + edge + offline inference |
| Integrations | SCADA / GIS / ticketing |