Manufacturing

Manufacturing runs on SOPs, supplier documents, and quality records. AI can process all three.

Supplier certifications that expire without notice. Quality reports that take an engineer two hours to compile from SPC data. Safety incidents that get logged but never analyzed for patterns. Shift reports that a plant manager builds manually every morning from five different systems.

We build AI systems that handle the document processing and reporting layer of your operations, structured output from unstructured data, anomaly detection from process telemetry, and reports your teams actually use without manual assembly.

How we work

Tell us about your operations workflow.

What we build.

Five specific AI workflows for manufacturing operations.

Supplier qualification document processing

Extract and structure data from supplier certifications, insurance certificates, quality audit reports, and PPAP documents. Flags expiring certifications and routes exceptions for procurement review.

Quality control report generation

AI reads inspection data from CMM outputs, SPC charts, and nonconformance records, generating structured QC reports and corrective action summaries without a quality engineer transcribing data.

Preventive maintenance prediction

Analyze equipment sensor data, maintenance history, and failure records to predict which assets are at elevated risk of downtime. Output integrates with your CMMS work order system.

Safety incident report analysis

Classify and analyze safety incident reports (near misses, recordable incidents, first aid cases) to surface recurring causal patterns, high-risk work areas, and leading indicators before OSHA recordable counts rise.

Production KPI reporting

Pull OEE, scrap rate, cycle time, and throughput data from your MES or ERP and generate readable shift reports and management summaries, at shift end, daily, or weekly, without a plant manager building the deck manually.

Who we build for.

Discrete manufacturers with complex supplier qualification requirements
Process manufacturers with SPC-driven quality control programs
Plant operations teams looking to reduce unplanned downtime
EHS managers managing high incident report volumes
Contract manufacturers building operational reporting infrastructure

Who this isn't for.

Facilities without digital process data
Predictive maintenance and quality AI require data from sensors, PLCs, or MES systems. If your shop floor produces no digital data, the first project is instrumentation, not AI.
Companies expecting AI to replace process engineers
AI generates reports and surfaces anomalies. Root cause analysis, process improvement decisions, and engineering judgment remain with your team. We build tools that make those engineers faster, not unnecessary.
Startups without an operational manufacturing footprint
Our manufacturing AI builds are for companies with production operations in place and data to work from. Pre-production or R&D-stage companies do not yet have the workflow volume to justify the investment.

Frequently asked questions.

Can this integrate with SAP, Oracle, or other ERP systems?: Yes. We have integrated AI reporting and document processing systems with SAP S/4HANA, SAP ECC, Oracle EBS, Oracle Cloud, and Infor via their APIs and data export capabilities. Most ERP integrations use REST APIs or SFTP-based data pipelines. The integration approach and timeline depend on which ERP modules contain the relevant data. We scope that on the discovery call.
How do you handle ITAR and export control compliance?: For defense and aerospace manufacturers with ITAR obligations, we design systems with access controls that restrict data to US-persons only, use US-based cloud infrastructure, and avoid routing controlled technical data through systems with foreign national access. We document the data flow architecture so your export control team can review before deployment.
Can this run on-premise rather than in the cloud?: Yes, with some limitations. LLM inference can run on-premise using self-hosted open-weight models, though performance is lower than frontier cloud models at equivalent cost. For plants with air-gapped OT networks, we design hybrid architectures where process data stays on-premise and only aggregated, non-sensitive outputs reach cloud systems. We scope this specifically for your network topology.
How do you handle OT/IT network separation requirements?: We do not put AI systems directly on OT networks. Our architecture reads data from historian systems, MES exports, or data diodes at the IT/OT boundary, not from PLCs or SCADA systems directly. If your plant already has an IT-side data historian (OSIsoft PI, Ignition), that is typically the integration point. If not, we help you scope the historian layer as a prerequisite.

Ready to automate your operations reporting?

Tell us which document processing or reporting workflow consumes the most engineering or operations time. We will reply within one business day with a rough scope and price range.

Manufacturing runs on SOPs, supplier documents, and quality records. AI can process all three.

What we build.

Five specific AI workflows for manufacturing operations.

Supplier qualification document processing

Extract and structure data from supplier certifications, insurance certificates, quality audit reports, and PPAP documents. Flags expiring certifications and routes exceptions for procurement review.

Quality control report generation

AI reads inspection data from CMM outputs, SPC charts, and nonconformance records, generating structured QC reports and corrective action summaries without a quality engineer transcribing data.

Preventive maintenance prediction

Analyze equipment sensor data, maintenance history, and failure records to predict which assets are at elevated risk of downtime. Output integrates with your CMMS work order system.

Safety incident report analysis

Production KPI reporting

Who we build for.

Discrete manufacturers with complex supplier qualification requirements

Process manufacturers with SPC-driven quality control programs

Plant operations teams looking to reduce unplanned downtime

EHS managers managing high incident report volumes

Contract manufacturers building operational reporting infrastructure

Who this isn't for.

Facilities without digital process data

Predictive maintenance and quality AI require data from sensors, PLCs, or MES systems. If your shop floor produces no digital data, the first project is instrumentation, not AI.

Companies expecting AI to replace process engineers

AI generates reports and surfaces anomalies. Root cause analysis, process improvement decisions, and engineering judgment remain with your team. We build tools that make those engineers faster, not unnecessary.

Startups without an operational manufacturing footprint

Our manufacturing AI builds are for companies with production operations in place and data to work from. Pre-production or R&D-stage companies do not yet have the workflow volume to justify the investment.

Frequently asked questions.

Can this integrate with SAP, Oracle, or other ERP systems?

Yes. We have integrated AI reporting and document processing systems with SAP S/4HANA, SAP ECC, Oracle EBS, Oracle Cloud, and Infor via their APIs and data export capabilities. Most ERP integrations use REST APIs or SFTP-based data pipelines. The integration approach and timeline depend on which ERP modules contain the relevant data. We scope that on the discovery call.

How do you handle ITAR and export control compliance?

For defense and aerospace manufacturers with ITAR obligations, we design systems with access controls that restrict data to US-persons only, use US-based cloud infrastructure, and avoid routing controlled technical data through systems with foreign national access. We document the data flow architecture so your export control team can review before deployment.

Can this run on-premise rather than in the cloud?

Yes, with some limitations. LLM inference can run on-premise using self-hosted open-weight models, though performance is lower than frontier cloud models at equivalent cost. For plants with air-gapped OT networks, we design hybrid architectures where process data stays on-premise and only aggregated, non-sensitive outputs reach cloud systems. We scope this specifically for your network topology.

How do you handle OT/IT network separation requirements?

We do not put AI systems directly on OT networks. Our architecture reads data from historian systems, MES exports, or data diodes at the IT/OT boundary, not from PLCs or SCADA systems directly. If your plant already has an IT-side data historian (OSIsoft PI, Ignition), that is typically the integration point. If not, we help you scope the historian layer as a prerequisite.

Ready to automate your operations reporting?

Tell us which document processing or reporting workflow consumes the most engineering or operations time. We will reply within one business day with a rough scope and price range.