About the client
A fast growing Industrial IoT company building predictive maintenance for heavy equipment. The goal was clear: reduce downtime and improve technician trust in alerts across many sites.
The business challenge
- Time to market: A full product would take more than a year without the right team.
- Data volume: Up to six hundred sensors per site sending data every twenty minutes.
- Scale: Multi tenant needs and different machine types across plants.
Cloud data pipeline
Resilient ingestion of ultrasonic and vibration streams with edge buffering, so predictive models receive complete signals even during connectivity gaps.
Tenant isolated SaaS
Per tenant MySQL on AWS RDS with templated schema cloning. Keeps data separate, supports compliance, and enables quick onboarding.
Technician apps
React for web and React Native for mobile. Teams view asset health, receive alerts, and log inspections. Overrides help refine alerts and build trust.
Install assist
QR guided setup reduces errors and shortens the time from install to useful predictive insights.
Built for scale and reliability
Java Spring Boot seventeen, AWS EC2 and S3, and RDS with MySQL eight. Cold storage lowers cost while keeping recent data responsive for analytics.
Technology stack
| Layer | Technologies |
|---|---|
| Frontend | React for web, React Native for mobile |
| Backend | Java Spring Boot seventeen |
| Database | MySQL eight on AWS RDS |
| Cloud | AWS EC2, S3, IAM, CloudWatch |
| Data | Vibration and ultrasonic sensors |
Business outcomes
| Metric | Impact |
|---|---|
| Time to market | Launched forty percent faster |
| Sensor deployment | Sixty percent faster with QR guided setup |
| Maintenance efficiency | Higher fault detection precision and fewer false positives |
| Support effort | Thirty percent lower due to integrated workflows |
| Operational scale | Six hundred plus sensors per site across many clients |
Conclusion
SequoiaAT turned a strong idea into a working platform that plants trust. The system delivers clear alerts, helps teams act sooner, and supports growth across many factories.
Where we help in the U.S.
We support industrial teams in Detroit, Chicago, Houston, Pittsburgh, Atlanta, San Jose, Los Angeles, Boston, Milwaukee, and Phoenix.
Explore our capabilities:
Frequently asked questions
- What is predictive maintenance
- It uses sensor data with analytics to detect early signs of failure so teams can service machines before breakdowns.
- How is trust built into alerts
- Field teams can review signals, add notes, and override alerts. This feedback improves future predictions.
- Can this work across many plants
- Yes. The platform is tenant isolated with templates for quick onboarding and consistent dashboards.