Predictive maintenance, quality scoring, and supply chain AI. From the factory floor up.
We build the systems that predict failures, score quality, and optimize production.
Manufacturing runs on timing. Downtime costs thousands per minute, quality defects compound through the supply chain, and demand forecasting determines whether you're sitting on inventory or missing orders. We build the AI and data systems that make these decisions faster and more accurate.
Why AI in Manufacturing is different.
Manufacturing generates terabytes of sensor data every day. Vibration, temperature, pressure, current, speed. Unplanned downtime costs industrial manufacturers an estimated $50B annually. Most of that data sits in historians and SCADA systems, never analyzed, never acted on.
The plants running predictive maintenance see 25-30% reductions in maintenance costs and 50% fewer unplanned stops. The ones on calendar-based schedules are either over-servicing equipment that is fine or missing failures until the line goes down. Computer vision quality inspection catches defects at line speed that human inspectors miss entirely.
The challenge on the factory floor is not algorithms. It is integration. OPC-UA connections to legacy PLCs, data pipelines from noisy sensors, models that run at the edge with 50ms latency requirements, and operators who need to trust the output. That is where we work.
The Problems That Slow You Down
Unplanned downtime costs $10K+ per hour
Equipment fails without warning. Maintenance schedules are calendar-based, not condition-based. Spare parts aren't staged.
Quality inspection is manual and inconsistent
Human inspectors catch 80% of defects on a good day. Bad batches ship, returns spike, and the root cause takes weeks to find.
Supply chain visibility ends at your dock
You know what's in your warehouse. You don't know what's on the truck, at the port, or delayed by 3 weeks.
Production planning by experience, not data
Shift scheduling, batch sizing, and line changeovers based on tribal knowledge. When the veteran retires, the playbook leaves with them.
Systems for Manufacturing
Predictive Maintenance
Sensor data + ML models that predict equipment failure 2-4 weeks before it happens. Maintenance moves from reactive to planned.
View service →Computer Vision Quality
Camera-based inspection that catches defects human eyes miss, at line speed, every unit, 24/7.
View service →Supply Chain Analytics
Real-time dashboards across suppliers, logistics, and inventory. Demand forecasting that adjusts to actual signals.
View service →Production Optimization
AI-driven scheduling, batch optimization, and changeover planning that maximizes throughput and minimizes waste.
View service →These aren't pitch deck scenarios. Every use case maps to a system pattern we've built and deployed in production.
What We Build in Manufacturing
Predictive Maintenance for Production Assets
VP OperationsCalendar-based maintenance and operator intuition; 10-20% production time lost to unplanned downtime.
LSTM/Random Forest on vibration, temperature, current, runtime sensor data, producing RUL predictions feeding CMMS.
Real-Time Quality Inspection
Director of QualityManual sampling misses subtle defects; scrap rates of several percent, costly rework.
CNN on line-camera images + process data (temperatures, pressures, speeds) for real-time defect classification.
OEE Optimization & Micro-Stop Analysis
Plant ManagerOEE reports hide thousands of micro-stops; 5-15% latent capacity unused.
Unsupervised clustering on high-frequency machine event logs, identifying recurring patterns and root cause suggestions.
Process Parameter Optimization
Process Engineering ManagerOperators rely on tribal knowledge; sub-optimal recipes drive scrap, energy waste, shift variation.
Bayesian optimization on historical runs, recommending optimal parameter windows and alerting on drift.
GenAI for SOPs & Work Instructions
Director of Manufacturing EngineeringDocumentation scattered across legacy PDFs/Word; updates lag process changes by weeks.
LLM agent ingests existing SOPs, ERP/MES change logs, engineering notes, then generates/updates structured docs and pushes to MES/PLM.
Digital Work-Instruction Copilot
Plant ManagerOperators depend on static paper instructions; onboarding takes months, changeover mistakes cause downtime.
LLM+RAG indexes SOPs, manuals, incident reports, answering natural-language questions at station and surfacing contextual checklists.
AI Production Planning & Scheduling
Head of PlanningPlanners juggle complex constraints in spreadsheets; sub-optimal schedules lower OEE and increase overtime.
ML predicts run times and changeover impacts, constraint solver generates optimized schedules, LLM summaries for planners.
Building the system is half the job. Growing the business around it is the other half.
How We Grow Manufacturing Brands
AEO for Industrial Equipment
Marketing DirectorManufacturers miss 50% of "best CNC machine" to ChatGPT; zero-clicks cut quote requests 35%.
Schema specs/guides, case studies, Perplexity tracking to RFQ forms.
LinkedIn Ads for Plant Software
Demand Gen LeadLinkedIn CPL $90-140 for ops tools; under 10% demo SQL rate.
Plant manager targeting, webinars, HubSpot scoring, ROI nurture.
HubSpot Lead Scoring for Machinery Sales
VP Marketing Ops1.5K leads/quarter; 68% unqualified, long cycles.
HubSpot intent scoring, sequences (specs, demos), ERP sync.
How We Work With Manufacturing
Machine Learning Solutions
Prediction systems that run 24/7. Not proof-of-concepts gathering dust.
Learn more →Data Engineering & Analytics
Your data is scattered across 15 tools. We wire it into one system that answers questions.
Learn more →Intelligent Automation
The repetitive judgment calls your team makes 200 times a day? We build systems that handle them.
Learn more →Cloud & Infrastructure
Cloud architecture, migration, Kubernetes, monitoring: the foundation under everything else.
Learn more →Frequently asked questions
Most modern equipment already has sensors. We start with available data like vibration, temperature, and pressure, and add sensors only where gaps exist.
Typically 95-99% accuracy depending on defect type. We train on your specific products and defect categories, not generic models.
Yes. We integrate with OSIsoft PI, Ignition, Siemens, Rockwell, and custom SCADA systems via OPC-UA or REST APIs.
AI moves fast. Stay ahead.
No spam. One actionable email per week on AI systems and growth.
Every minute of downtime costs you. We build systems that prevent it.
Start with a plant-floor assessment. We'll identify the highest-impact predictive and quality opportunities.