SMAC – Engineering Field Reporting and Root Cause Analysis App
A field engineering app for offline inspections, AI-assisted technical reports and structured root cause analysis.
- Platforms
- iOS, Android
- Store category
- Productivity
The project
SMAC is an engineering fieldwork platform for technical professionals in power generation and oil and gas. With ReportImpulse, engineers capture inspections, activities, measurements, findings, photos and recommendations in one structured workflow, even offline on site, then generate client-ready reports with AI-assisted summaries. With RootLens, they investigate failures through structured root cause analysis: 5 Whys, evidence, contributing causes, corrective actions and lessons learned. Reports and analyses are kept as engineering knowledge for future jobs, on iOS and Android.
The challenge
- Field engineers took notes on paper or in scattered files and rebuilt them into reports back at the office.
- Sites often have no connection, so cloud-only tools fail where the work happens.
- Failure investigations often ended at the repair, without a recorded root cause or corrective action.
What we built
- Structured field reports for inspections, maintenance, troubleshooting and shutdowns, captured offline.
- AI-assisted executive summaries and wording help that turn field notes into professional text.
- Export to PDF with sections the engineer can show or hide, branded for the client.
- A root cause analysis module with 5 Whys, severity and risk classification, evidence and corrective actions.
Screens
Key features
ReportImpulse reporting
Activities, measurements, findings, photos and recommendations in one structured report.
Offline field capture
Engineers keep recording on site without a network connection.
AI-assisted summaries
An executive summary and key defect observations drafted from the captured data.
Client-ready PDF export
Engineers choose which sections appear and export a professional report.
RootLens root cause analysis
Structured 5 Whys analysis with evidence tracking and contributing causes.
Corrective actions
Actions and lessons learned recorded against each failure, with AI suggestions.
