Energy & Industrial

AI, Vision, and Data Systems for Software and AI -First Industrial Outcomes.

We build pragmatic solutions across engineering data, inspection imagery, and enterprise systems focused on decision support, data quality, QA/QC intelligence, and document-centric workflows.

Manufacturing QA/QC
From Reacting to Quality Problems to Preventing Them.

A quality intelligence system for a high-strength steel tubular manufacturer that flags drifting heat-treat batches in process, cutting scrap and rework 30-40% and recovering $400-600K a year in margin.

PREDICTIVE MAINTENANCE
The Plant That Stopped Failing by Surprise.

Machine-health intelligence gives a high-volume precision parts maker weeks of warning before a failure, protecting uptime and every delivery promise.

Energy and Industrials
Hidden Capacity, Found: 8 to 12 Points of OEE Unlocked.

Live OEE with automated downtime capture lifted a multi-shift fabrication plant's OEE by 8 to 12 points, growing output with zero new machines.

The Intelligence Layer for Industrial Operations

We build practical AI, vision, and data solutions on top of engineering documents, operational data, inspection imagery, and enterprise systems.

Digitize Engineering Knowledge

Extract tags, symbols, BOMs, revisions, relationships from P&IDs, CAD exports, PDFs, scans, & redlines.

Scale Quality With Computer Vision

Detect defects, labels, markings, completeness, & inspection evidence patterns in manufacturing imagery.

Turn Operational Data Into Intelligence

Integrate historian, SCADA, MES, ERP, EAM, quality, lab, and sensor streams into governed data products.

Transform Documents Into Workflows

Extract tags, symbols, BOMs, revisions, relationships from P&IDs, CAD exports, PDFs, scans, & redlines.

Energy & Industrial Solutions

The Work Worth Automating  Across Operations

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Datasheet Parsing & Equipment Attribute Extraction

Extract structured attributes from vendor datasheets, spec sheets, manuals, and catalogs. Normalize units, ratings, materials, dimensions, operating limits, and certification fields into a searchable model.

Improve Operational Efficiency
Quality & Engineering

Structured asset intelligence

Event Context & Root-Cause Explorer

Correlate alarms, process tags, quality events, maintenance history, shift logs, and documents around a downtime or deviation event. Teams get a timeline and candidate drivers for investigation.

Improve Operational Efficiency
Operations

Faster RCA preparation

Supplier Quality & Process Data Fusion

Link supplier lots, certificates, inbound inspection, production outcomes, and claims. Identify supplier or material patterns that correlate with scrap, rework, yield loss, or customer complaints.

Improve Quality & Reliability
Supply Chain & Procurement

Closed-loop supplier view

Energy Consumption Analytics

Analyze energy usage by asset, line, batch, shift, and operating mode. Detect abnormal consumption, benchmark peers, and model cost-to-serve without modifying physical controls.

Reduce Costs
Operations

5–12% efficiency opportunity

OEE, Yield & Downtime Intelligence

Create a cross-source view of OEE, yield loss, stoppages, downtime reasons, and quality events. Tie losses back to asset, line, product, material, and process conditions.

Improve Operational Efficiency
Operations

Loss tree, not static reports

Batch & Process Anomaly Detection

Learn normal process behavior across batches, shifts, product grades, and operating modes. Surface early anomalies with context so operations and engineering teams can investigate the drivers.

Improve Quality & Reliability
Operations

Earlier deviation signals

Asset Health Risk Scoring

Combine sensor trends, alarm history, maintenance history, operating envelopes, and inspection evidence to score asset risk. The output is decision support for planners, not automatic field dispatch.

Improve Quality & Reliability
Operations

Prioritized reliability view

Sensor Telemetry Integration & Data Quality

Ingest sensor and time-series streams with context, units, asset hierarchy, calibration metadata, and data-quality checks. Detect gaps, stuck values, drift, and missing tags before analytics break.

Modernize Data & Technology
IT & Data

Cleaner operational signals

Defect Taxonomy & Vision Feedback Loop

Build the data flywheel behind CV QA/QC: labeled defect libraries, reviewer feedback, model performance dashboards, and retraining workflows mapped to product lines and manufacturing cells.

Improve Quality & Reliability
Quality & Engineering

Model improves with use

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platform

An Industrial Intelligence Foundation, Pre-Built for Speed

Four layers, one delivery model. We connect industrial data sources, normalize operational context, apply AI safely to drawings, documents, images, and time-series data, then expose the results through dashboards, copilots, APIs, and workflow apps.

Four Building Blocks of a Trusted Financial Platform

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Process and mechanical knowledge becomes computable.

We extract symbols, tags, relationships, revision tables, BOMs, tolerances, and notes so drawings become data products that can be searched, reconciled, and audited.

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Computer vision supports inspectors, notreplaces accountability.

Models flag defects and evidence patterns. Reviewers keep final judgment,while the system tracks reviewer feedback and creates traceable QA records.

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Sensor and operations data gets context,lineage, and quality.

We connect time-series streams to asset hierarchy, process context, maintenance history, quality records, and ERP data so analytics are trusted enough to use.

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Unstructured industrial documents become searchable workflows.

Datasheets, invoices, CoAs, manuals, contracts, and specs are parsed into structured attributes, obligations, exceptions, and answerable knowledge bases.

Build Industrial Systems That Drive  Real-World Outcomes.

Transform engineering data, operational workflows, and industrial intelligence into measurable business impact.

ENGAGEMENTS

No Hype. Just Industrial Software Systems That Make Existing Data Useful