01
−28%
unplanned downtime
Predictive maintenance
Time-series ML on sensor data with explicit failure-mode modeling, integration with the maintenance management system, and the operational discipline plant maintenance teams actually use.
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AI & Machine Learning × Manufacturing
AI for Manufacturing.
Predictive maintenance, computer-vision quality control, production scheduling, and demand forecasting — engineered for OT/IT convergence with the operational embedding plant floors actually require.
- ISA/IEC 62443
- Aware
- FDA 21 CFR Part 11
- Supported
- GxP
- Co-piloted
- ISO 27001
- Aligned
The reality
Industrial AI fails when the model lives in the cloud and the maintenance team lives on the plant floor.
Industrial AI projects share a recurring failure mode: a model that worked in lab conditions degrades on the floor; an integration with the maintenance management system that was scoped as 'phase 2'; a predictive-maintenance alert that surfaces in a system the technician doesn't open; a quality-control vision deployment that requires a data scientist on call; and a digital-twin program that produced visualizations but never closed the loop with operations. The model isn't the problem; the operational embedding is.
Prosigns ships industrial AI with the operational embedding as primary scope. Senior engineers embedded with maintenance teams during model design, plant-floor data understood as primary scope rather than as an obstacle, integration with MES / EAM / SCADA designed first, and the operating discipline plant rhythms require. CORTEX builds the AI; FORGE handles MES integration; CITADEL handles ISA/IEC 62443 and GxP where applicable.
Where it ships
6 workloads, scoped to manufacturing.
Concrete applications where ai & machine learning unlocks measurable value inside manufacturing delivery constraints.
- 02
98.7%
defect detection rate
Computer-vision quality control
Defect detection, dimensional inspection, and process verification — edge inference where latency demands it, integrated with reject-handling and traceability systems.
- 03
Production scheduling AI
Optimization for throughput, changeover minimization, energy efficiency, and constraint satisfaction. Integration with MES and ERP for closed-loop scheduling.
- 04
Demand forecasting
Multi-horizon demand forecasting for finished goods, raw materials, and capacity. Integration with S&OP processes and the ERP that drives execution.
- 05
Energy and emissions optimization
Real-time energy optimization across manufacturing operations, with sustainability reporting integrated into the same data substrate analytics workloads use.
- 06
Workforce safety AI
Computer vision for PPE compliance, near-miss detection, and ergonomic risk monitoring — engineered against worker-privacy frame and the unionization realities plants actually face.
How we engage
4 phases, named in the SOW.
Each phase has a deliverable, an owner, and an acceptance criterion calibrated to manufacturing delivery.
- 01
Plant-floor reality assessment
Discovery on the plant floor. We walk lines, talk to operators and maintenance, audit OT inventory, and identify the integration surfaces that matter. AI use cases land against actual plant realities, not generic Industry 4.0 frameworks.
- 02
OT/IT convergence with explicit boundaries
Cloud-first AI layer with explicit OT integration boundaries. Network segmentation respecting ISA/IEC 62443 zones, edge inference where latency / sovereignty demands it, and identity that bridges OT and IT identities.
- 03
Model design with maintenance teams
Senior engineers embedded with plant operations and maintenance during model design. We don't build models plant teams won't use — we build alongside the people who'll operate them, with explicit integration into the workflows they already run.
- 04
Operating cadence
Plant-rhythm-aware operating cadence: quarterly model retraining, monthly maintenance-workflow integration health, and the IR plan that handles plant-floor incidents — not just data center ones.
Capabilities
What’s in scope.
- Predictive maintenance: time-series ML with maintenance-workflow integration
- Computer vision QC: defect detection, dimensional inspection, edge inference
- Production scheduling: throughput / changeover / energy optimization
- Demand forecasting integrated with S&OP and ERP execution
- Digital twin and simulation for capacity planning and operator training
- Edge inference: NVIDIA Jetson, AWS Wavelength, on-prem GPU
- OT/IT integration: OPC UA, MQTT, MES, SCADA, Historian
- ISA/IEC 62443-aware deployment with explicit zone and conduit design
Stack
Tools we use in production.
- Foundation models
- Anthropic ClaudeOpenAI GPTAWS BedrockAzure OpenAI
- Computer vision
- PyTorchYOLODetectron2ONNXNVIDIA Triton
- ML platform
- SageMakerDatabricksMLflowWeights & Biases
- Edge inference
- NVIDIA JetsonAWS WavelengthAzure Stack EdgeCustom GPU
- OT integration
- OPC UAMQTTAspenOSIsoft PIRockwell FactoryTalk
- Observability
- DatadogOpenTelemetryGrafanaPrometheus
Compliance overlay
Frameworks we engineer to.
Every manufacturing engagement carries the evidence collection that procurement and audit teams expect on day one.
- 01
ISA/IEC 62443
Network zones and conduits documented in the architecture, identity model that respects OT/IT boundaries, change management that doesn't blow up plant safety, and audit logging integrated with existing OT security posture.
- 02
FDA 21 CFR Part 11 (regulated manufacturing)
For pharma and medical-device manufacturing: electronic records, electronic signatures, validation evidence aligned to GAMP 5, and the audit-trail tooling regulated manufacturing requires. We co-pilot with the customer's regulatory and quality functions.
- 03
GxP (cGMP)
Validation lifecycle, IQ / OQ / PQ documentation, change management aligned to GxP, and the data-integrity discipline (ALCOA+) regulated manufacturing operates under.
- 04
Worker privacy and labor law
Worker-facing AI (PPE compliance, ergonomic monitoring, productivity analytics) engineered against worker-privacy frames, union agreements, and the explicit consent / opt-out patterns labor relations require.
Selected work
Quantified outcomes, not adjectives.
Where this fits
Open the parent practice or industry hub.
Common questions
Asked before the first call.
- 01
Can you ship at the edge?
Yes — when latency, sovereignty, or connectivity demands it. NVIDIA Jetson, AWS Wavelength, Azure Stack Edge, and on-prem GPU deployments are all in our portfolio. We tell you when 'edge' is the wrong answer and the right one is cloud with QoS-aware connectivity.
- 02
How do you integrate with our existing maintenance management system?
As primary scope, not phase 2. Major CMMS / EAM platforms (SAP PM, Maximo, Infor EAM, Fiix, eMaint) are in our active engagement portfolio. We design integrations through documented interface contracts, with explicit handling of work-order lifecycle and the technician workflow patterns the system actually supports.
- 03
Do you work with our OT vendors and existing SCADA?
Yes — Rockwell, Siemens, Honeywell, GE Digital, AVEVA, and the major MES vendors through their published interfaces. Where the integration surface is custom, we build sanctioned interfaces with the OT team rather than working around them.
- 04
How do you handle 21 CFR Part 11 / GxP for regulated manufacturing?
21 CFR Part 11 and GxP frame is part of the discipline — electronic records, electronic signatures, validation evidence aligned to GAMP 5, IQ / OQ / PQ documentation, and the audit-trail tooling regulated manufacturing requires. We co-pilot with the customer's regulatory and quality functions.
- 05
What about worker-facing AI?
Engineered against worker-privacy frame from architecture. Computer-vision PPE compliance, ergonomic monitoring, and near-miss detection deployed with explicit worker-consent and union-agreement patterns. We tell you when an AI use case is fundamentally bias-fragile or labor-relations-sensitive.
- 06
What does a manufacturing AI engagement cost?
Discovery: 4–6 weeks, $60K–$150K. Predictive maintenance program (1–3 plants): 6–10 months, $400K–$1.2M. Computer-vision QC across multiple lines: 5–9 months, $400K–$1M. Production scheduling AI: 6–12 months, $500K–$1.5M. Multi-plant programs: $1.5M–$5M+. Managed Services: $40K–$200K monthly retainer.
Talk to us
Bring a ai for manufacturing problem. We’ll bring a senior engineer.
A senior engineer plus the CORTEX department lead joins the first call — both with prior manufacturing delivery experience.