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.
Loading…
Industry
Manufacturing.
Predictive maintenance, production scheduling, quality-control vision, and industrial IoT — engineered against the OT/IT convergence reality and the operational rhythms manufacturing runs on. Not a SaaS playbook with a manufacturing slide.
- ISA/IEC 62443
- Aware
- FDA 21 CFR Part 11
- Supported
- GxP
- Co-piloted
- ISO 27001
- Aligned
The landscape
Manufacturing engineering fails when IT pretends OT doesn’t exist — and OT pretends the cloud is a fad.
The familiar shape: an enterprise IT team rolling out cloud-first patterns that ignore the SCADA, MES, and PLC realities on the plant floor; a plant operations team running 15-year-old Windows installations because the OEM won't certify newer ones; a predictive-maintenance pilot that produced models nobody integrated with the maintenance workflow; a quality-control vision system that works in the lab and fails on the floor; and an MES integration scoped as phase 2 that's still phase 2 three years later.
Prosigns ships manufacturing engineering with OT/IT convergence as a first-class concern. We design with explicit awareness of plant-floor realities, MES / SCADA / Historian integration as primary scope, and the operating discipline plant rhythms require. ATLAS handles ERP / D365 implementations; CORTEX handles predictive maintenance and computer vision; FORGE handles MES integration and custom workflow apps; CITADEL handles ISA/IEC 62443 and GxP where applicable.
Where we ship
6 workloads, grounded in this sector.
Specific applications we’ve built and operated for manufacturing buyers. Every example below is grounded in a real shipped engagement.
- 02
98.7%
defect detection rate
Quality-control vision
Computer vision for defect detection, dimensional inspection, and process verification. Edge inference where latency demands it, integration with reject-handling and traceability systems.
- 03
Production scheduling AI
Optimization for throughput, changeover minimization, energy efficiency, and constraint satisfaction across multi-line plants. Integration with MES and ERP for closed-loop scheduling.
- 04
Industrial IoT platforms
Edge-to-cloud architectures with OPC UA / MQTT, time-series storage, anomaly detection, and the integration story that makes the data useful to plant operators.
- 05
MES and ERP integration
Microsoft Dynamics 365 F&O, SAP, Oracle, and custom MES integration. Bidirectional data flows, explicit interface contracts, and the operational discipline production environments require.
- 06
Digital twin and simulation
Process and asset digital twins for capacity planning, what-if simulation, and operator training — integrated with the same data substrate analytics and AI workloads use.
How we engage
4 phases, named in the SOW.
Each phase has a deliverable, an owner, and an acceptance criterion specific to manufacturing delivery.
- 01
Plant-floor reality assessment
Discovery on the plant floor, not in the boardroom. We walk lines, talk to operators and maintenance, audit OT inventory, and identify the integration surfaces that matter. Architecture decisions land against actual plant realities, not generic Industry 4.0 frameworks.
- 02
OT/IT convergence with explicit boundaries
Cloud-first IT layer with explicit OT integration boundaries. Network segmentation that respects ISA/IEC 62443 zones and conduits, identity that bridges OT and IT identities, and the audit-trail tooling regulated industries require.
- 03
Engineering with maintenance teams
Senior engineers embedded with plant operations and maintenance during design and rollout. We don't build models or systems plant teams won't use — we build alongside the people who'll operate them.
- 04
Operating discipline
Plant-rhythm-aware operating cadence. Quarterly model retraining against drift, monthly maintenance-workflow integration health, and the IR plan that handles plant-floor incidents — not just data center ones.
Practices in manufacturing
How each practice plays in this sector.
The capabilities below are scoped to the constraints manufacturing procurement actually enforces — compliance, audit, data residency, and vendor risk.
- 01CORTEX
AI & Machine Learning
Generative AI, agents, computer vision, predictive analytics, and MLOps — engineered for production.
Open the practiceIn Manufacturing
Predictive maintenance, computer-vision QC, production scheduling, and demand forecasting — with the OT/IT integration and maintenance-workflow embedding plant teams actually use.
- 02ATLAS
Enterprise Platforms
Implementation, customization, and managed support for Dynamics 365, Salesforce, Power BI, ServiceNow, Shopify Plus, and ERPNext.
Open the practiceIn Manufacturing
Dynamics 365 F&O for finance, supply chain, and EAM — with multi-plant rollout discipline and SOX-aligned financial close.
- 03FOUNDATION + SKYWAY
Platform & Cloud
Cloud architecture, DevOps, SRE, migrations, data engineering.
Open the practiceIn Manufacturing
Industrial IoT platforms with edge-to-cloud architecture, time-series storage, and the data-engineering discipline that makes plant data useful.
- 04FORGE
Custom Software
SaaS, enterprise applications, legacy modernization, integrations, and mobile.
Open the practiceIn Manufacturing
MES integration, custom workflow apps, and the operational tooling plant operations teams actually run.
Selected work
Quantified outcomes, not adjectives.
Common questions
Asked before the first call.
- 01
Can you work with our OT vendors and existing MES?
Yes — that's most of the engagement. We integrate with 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 rather than unmonitored screen-scrapers — and we partner with your OT team rather than working around them.
- 02
How do you handle 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 your existing OT security posture. We co-pilot with CITADEL on every regulated-industry engagement.
- 03
Do 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 also tell you when 'edge' is the wrong answer and the right one is cloud with QoS-aware connectivity.
- 04
What about FDA-regulated manufacturing (pharma, medical devices)?
21 CFR Part 11 and GxP frame is part of the discipline — 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 rather than acting as the IFU owner.
- 05
Can you help with sustainability / energy reporting?
Yes — energy monitoring, scope 1/2/3 emissions tracking, and sustainability reporting integrated with the same data substrate analytics workloads use. Most engagements treat sustainability as a data engineering problem first, then layer reporting and AI insight on top.
- 06
What does a manufacturing engagement cost?
Plant assessment: 4–6 weeks, $60K–$150K. Predictive maintenance program (1–3 plants): 6–10 months, $400K–$1.2M. Multi-plant ERP rollout: 9–18 months, $1.5M–$5M+. Industrial IoT platform: 6–12 months, $500K–$2M. Managed Services: $40K–$200K monthly retainer. Brackets published honestly.
Talk to us
Bring a manufacturing problem. We’ll bring a senior engineer.
A senior engineer plus the relevant department lead joins the first call. No discovery gauntlet, no junior reps.