Ship predictive maintenance AI without building an internal team
Atomic Build is a forward-deployed product and engineering team that embeds inside your plant operations and ships AI systems that forecast equipment failures, optimize maintenance schedules, and cut unplanned downtime in weeks.
Unplanned downtime is killing your margins. Your sensors have the data. You don't have the team.
Equipment failures still surprise your ops team because sensor data lives in silos and maintenance decisions rely on technician hunches and spreadsheet schedules. Hiring a full ML team costs $1.5M+/year before you ship your first model. Vendors promise predictive maintenance dashboards that never actually connect to your OPC servers.
- Reactive maintenance schedules
- You fix equipment after it breaks, not before. Unplanned downtime costs $10k–100k per hour depending on your production line.
- Siloed sensor and operational data
- Real-time sensor feeds, historical maintenance logs, production schedules and spare parts inventory live in disconnected systems. Nobody can see the full picture.
- No embedded AI in your product
- Your OEMs can't offer predictive maintenance as a product feature because you don't have the in-house capability to build and deploy it at scale.
- False positives burn technician trust
- Off-the-shelf predictive tools cry wolf so often your team ignores the alerts. Maintenance decisions go back to gut feeling.
- Seasonal and production-specific patterns go unmapped
- Your plant's unique equipment combos, environmental conditions and production rhythms aren't captured in generic vendor models.
- Hard to justify AI investment without proof
- You know predictive maintenance could save 20–30% of downtime, but you have no baseline business case to sell it internally.
Predictive maintenance AI lives inside your operations, not inside a platform vendor's SaaS box.
We don't believe in generic ML models or 18-month transformation programs. We embed a small product and engineering team directly into your plant, calibrate models against your actual sensor and maintenance data in real time, and ship a production system that your ops team and OEM customers can rely on.
- Calibrate to your equipment, not to industry averages
- Every plant has different equipment combos, environmental conditions and failure modes. We build models trained on your data, validated against your maintenance history, tuned for your specific production lines.
- Ship embedded AI, not a dashboard
- We integrate directly with your SCADA, PLC and ERP systems so predictions flow into your existing maintenance workflows — not a separate tab your technicians have to remember to check.
- Forward-deployed by default
- Our engineers live onsite for the build duration, running hypothesis tests against live equipment, talking to your maintenance leads, and iterating on model performance in real time.
- Human judgment, enhanced
- Technicians stay in control. We surface predictions with confidence scores and reasoning so they can make smarter decisions faster — never automation that removes human accountability.
- Measure impact, not model accuracy
- We optimize for downtime prevented, spare parts ordered at the right time, and maintenance labor saved — not for AUC-ROC scores.
From equipment baseline to production AI in six weeks
We start with an Opportunity Sprint to score your critical equipment and highest-risk failure modes. Then we deploy a forward-deployed team to build, test and ship the AI system into your plant operations.
- Opportunity Sprint · Week 1
- We walk your plant with maintenance leads and ops managers, map critical equipment, review historical failure logs and sensor data, and score which AI workflows will prevent the most downtime and cost.
- Data preparation and model scope · Week 2
- We connect to your SCADA, PLC and maintenance systems to extract sensor history and failure records. We design the model architecture, define prediction targets and success metrics, and map data flows from equipment through to your maintenance scheduling system.
- Forward-deployed model build and integration · Weeks 3–6
- Atomic Build engineers sit in your plant, build and iterate the predictive model against live equipment data, integrate predictions into your CMMS or maintenance dispatch system, and stress-test the full workflow with your technicians.
- Monitor, validate impact, and scale · Week 7+
- We measure the system against your baseline downtime and cost metrics, iterate the model as new equipment failure patterns emerge, and queue the next critical failure mode to predict.
Relevant services
Most engagements combine three or four of these. Start with what hurts most.
Five predictive maintenance workflows we've deployed
Each build starts from a critical equipment asset or failure mode inside your plant. These are the patterns that move plant availability the fastest.
- Bearing and motor failure prediction
Real-time vibration and temperature data feeds an AI model that forecasts bearing wear and motor failure 2–4 weeks in advance, auto-triggering spare parts orders and maintenance scheduling.
- Hydraulic system degradation detector
Monitors pressure, flow and contamination across hydraulic lines to predict seal failures and fluid degradation before catastrophic loss.
- Critical spare parts demand forecast
AI model predicts which high-lead-time parts you'll need 8–12 weeks out based on equipment condition and seasonal production demand so you never stock out.
- Maintenance window optimizer
Scores when to schedule maintenance so you minimize disruption to production while addressing equipment risk — balancing technician availability, material staging and production calendars.
When to talk to us
Some patterns we hear on the first call. If two or more of these are true, the conversation is worth having.
- You fix equipment after it breaks, not before. Unplanned downtime costs $10k–100k per hour depending on your production line.
- Real-time sensor feeds, historical maintenance logs, production schedules and spare parts inventory live in disconnected systems. Nobody can see the full picture.
- Your OEMs can't offer predictive maintenance as a product feature because you don't have the in-house capability to build and deploy it at scale.
- Off-the-shelf predictive tools cry wolf so often your team ignores the alerts. Maintenance decisions go back to gut feeling.
- Your plant's unique equipment combos, environmental conditions and production rhythms aren't captured in generic vendor models.
- You know predictive maintenance could save 20–30% of downtime, but you have no baseline business case to sell it internally.
Book a discovery call
Decide what is worth building first.
We start with an Opportunity Sprint to score your critical equipment and highest-risk failure modes. Then we deploy a forward-deployed team to build, test and ship the AI system into your plant operations.