The Challenge
Moment Track builds factory automation systems that manufacturers depend on for real-time production tracking. When the factory floor is scanning QR codes hundreds of times an hour, the software behind it cannot lag — and it certainly cannot go down.
By the time Moment Track came to OmniMinds, their infrastructure was working against them. Everything ran on a single-server setup that had been outgrown long ago:
- Daily lag in QR scanning. The core product experience degraded every day under production load, and operators on the floor felt it.
- 12% downtime during shift changes. The exact moments when scanning volume spiked — shift handovers — were the moments the system buckled. Downtime during a shift change is not an IT metric; it is lost production data and frustrated factory customers.
- $28K per month wasted on over-provisioned infrastructure. To compensate for instability, the team had over-bought capacity that sat idle most of the day. They were paying enterprise prices for single-server reliability.
The engineering team knew the architecture was the problem. What they needed was a partner who could re-platform a live, revenue-carrying system without pausing the business — and do it in months, not quarters.
Our Solution
OmniMinds deployed a 5-person pod of AWS-certified engineers and completed the re-platform in 3 months, with zero disruption to Moment Track’s production customers.
Cloud-native foundation on AWS EKS. We moved the platform onto Amazon EKS with high availability across multiple availability zones. The single point of failure disappeared: if a node or an entire AZ degrades, workloads reschedule automatically and scanning continues. All infrastructure was codified in Terraform, so every environment is reproducible and auditable.
Real-time data pipelines. QR scan events now flow through Kafka pipelines into OpenSearch, giving Moment Track real-time analytics on production data that previously arrived in batches. Shift-change spikes — the old failure mode — became a routine autoscaling event.
IoT and predictive maintenance. We integrated factory IoT sensor data into the same pipeline and layered AI-powered predictive maintenance on top, so equipment issues surface before they cause downtime rather than after.
Computer vision at scale. Moment Track’s defect-detection applications were Dockerized and deployed on GPU-optimized node groups, sized to the workload instead of the worst case. This is what allowed the camera fleet to grow 5× without an architecture conversation.
Throughout the build, our engineers used AI agents for the mechanical work — Terraform module scaffolding, pipeline configuration, migration validation — which is a large part of how a 5-person team ships a full re-platform in 3 months.
The Results
Six-plus months into production, the numbers hold:
- 100% uptime, sustained for over 6 months. The 12% shift-change downtime is gone. Not reduced — gone.
- 35% cost reduction, $132K saved. Right-sized, autoscaling infrastructure eliminated the $28K/month over-provisioning tax. Moment Track now pays for the capacity it uses.
- 40% faster defect detection. GPU-optimized nodes and streaming pipelines mean vision workloads process at line speed, catching defects earlier in the production run.
- Camera fleet scaled from 10 to 50 — seamlessly. The growth that would have broken the old architecture became a non-event. New cameras join the fleet; the platform absorbs them.
The strategic result sits underneath the metrics: Moment Track’s infrastructure went from being the reason they couldn’t sign bigger customers to being a selling point when they do.
Why It Worked
Moment Track did not buy hours from OmniMinds. They bought outcomes: uptime, cost reduction, and headroom for growth — and the engagement was scoped and measured against exactly those.
The AI-augmented pod model is why 5 people delivered what typically takes a much larger team. Senior AWS-certified engineers — the same caliber of architects who have built systems at IBM, TCS, and DXC — made the design decisions that matter: multi-AZ topology, Kafka partitioning, GPU node sizing. AI agents accelerated everything repeatable around those decisions. The client pays for the outcome; the leverage is ours to engineer.
It also worked because Cloud, DevOps, and IoT were not three separate vendors. One pod owned the EKS platform, the Terraform pipelines, and the sensor integration end to end, so there were no seams for problems to hide in.
Jared Egget called the team “a diamond in the rough.” We will take that — along with the 100% uptime record that backs it up.
“This is a diamond in the rough. If you ever get a chance to work with them, you should do so.”