Industrial-edge smart inspection
Shop-floor edge box runs the agent — works offline.
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 Process / discrete manufacturers
- Project duration
- 6–8 weeks
- Tech stack
- Multi-agent · Federated learning
- Deployment
- Edge / on-prem
2. Business Context & Pain Points
The customer is a Enterprise / Group player in Manufacturing, 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 "Local-First Agent Gateway (OpenClaw)", Global AI DAO integrated the following capability stack into a single solution:
- Multi-agent —— Wired into the critical business path across ingestion, inference and human-in-the-loop collaboration.
- Federated learning —— Wired into the critical business path across ingestion, inference and human-in-the-loop collaboration.
- Deployment: Edge / on-prem, balancing data compliance, performance SLA and operability.
4. Quantified Outcomes (Before vs After)
| Metric | Before | After | Delta |
|---|---|---|---|
| inspection coverage | 100% | 220% | +120% |
Source metric: +120% inspection coverage, anomalies caught 6h earlier.
5. Specs & Fit
| Deployment | PC / server / edge box |
| Min hardware | 16GB RAM, optional GPU acceleration |
| Data residency | 100% local, offline-capable |
| Channels | IM / email / API / in-house systems |