Quadruple Automation Services

Dedicated DBA & AI Teams

Data Analytics & AI
for Manufacturing

Two specialist teams. One outcome — your factory running smarter. Our Database Engineering team builds the data foundations, and our AI & Analytics team turns that data into predictions, decisions and profit.

94%
Prediction Accuracy
−60%
Unplanned Downtime
−80%
Reporting Time
Live Analytics Dashboard
LIVE
84.2%
OEE
97.8%
Quality
88.5%
Perf.
Production Output — Last 7 Days
M T W T F S S
AI Predictive Alert
Press #3 bearing temp rising — failure predicted in 18–22 hrs. Maintenance notified.
Dedicated DBA Team
Dedicated AI Team
SQL Server • PostgreSQL • Oracle
Python • scikit-learn • TensorFlow
Power BI • Grafana
Time-Series Historian
Real-time OEE
Predictive Maintenance
Our Structure

Two Dedicated Teams — One Unified Platform

Most analytics vendors are one-dimensional. At QAS, we built two specialist practices that work in concert — the data engineering foundation and the AI intelligence layer on top.

Team 1

Database Engineering (DBA)

Our DBA team is the backbone of every data solution we build. They design and manage the data architecture that every dashboard, report and AI model depends on. Without solid data foundations, analytics fails — our DBAs make sure that never happens.

Schema Design & Optimization
SQL Server / PostgreSQL / Oracle
Data Warehouse & Data Lake
ETL / ELT Pipelines
Query Performance Tuning
Database Security & Backup
Replication & High Availability
Time-Series & Historian DB
Master Data Management
Cloud DB (Azure / AWS RDS)
Why it matters
Clean, normalised, performant data is the non-negotiable foundation. Our DBAs ensure every byte that enters the system is structured, indexed and accessible at speed — whether you need a single KPI or 10 million rows of historical analysis.
Team 2

AI & Machine Learning Engineering

A fully separate AI engineering practice, focused exclusively on manufacturing intelligence. Our AI team builds models trained on your specific production data — not generic templates. Predictive maintenance, quality forecasting, anomaly detection and closed-loop optimization, all purpose-built for your processes.

Predictive Maintenance (ML)
Quality Prediction Models
OEE Anomaly Detection
Demand Forecasting
Computer Vision (Defect)
NLP for Maintenance Logs
Reinforcement Learning
Closed-loop Optimization
Model Monitoring & Retraining
Explainable AI (XAI)
Why it matters
Generic AI doesn’t understand your press cycle, your cure curve or your batch variance. Our AI team trains models on your data, in your context, and deploys them in your existing infrastructure — models that operators trust and act on.
How the Two Teams Work Together
The DBA team designs the data architecture, builds pipelines, manages the historian and ensures data quality. The AI team consumes that clean, structured data to train and serve models. Neither team compromises the other. The result is a production-grade analytics platform with solid engineering under every insight.
Analytics Capabilities

What We Build for Your Factory

DBA Team

Real-Time OEE Dashboards

Live OEE, Availability, Performance and Quality KPIs per machine, line and plant. Built on your historian and MES data. Drill down from plant to shift to individual machine in one click.

AI Team

Predictive Maintenance

ML models trained on vibration, temperature, current and pressure signals. Predict bearing failures, motor degradation and seal wear 24–72 hours before they cause unplanned downtime.

AI Team

Quality Analytics & SPC

Statistical Process Control charts, Cpk/Ppk tracking, defect Pareto analysis and quality prediction models. Catch drift before it becomes scrap.

DBA Team

Data Warehouse & Lakehouse

Centralised manufacturing data warehouse or lake architecture. SAP, MES, SCADA, historian and IIoT data unified in one queryable platform — designed and managed by our DBA team.

DBA Team

Shift & Production Reporting

Automated shift reports, daily production summaries and management packs generated without human intervention. Right data, right person, right time — every time.

AI Team

Demand & Yield Forecasting

Time-series forecasting models for production demand, material yield and energy consumption. Plan better, waste less and hit customer delivery targets more consistently.

AI Team

Computer Vision & Defect Detection

Camera-based automated visual inspection using deep learning. Detects surface defects, assembly errors and label mismatches at line speed — no human fatigue, no missed rejects.

DBA Team

ETL Pipeline Engineering

Robust ETL and streaming data pipelines from PLC, SCADA, MES, ERP and IIoT sources. Designed for reliability, monitored for latency, and maintained by our DBA team around the clock.

AI Team

Anomaly Detection & Alerting

Unsupervised ML models that learn your normal process envelope and alert on any deviation — temperature spikes, vibration shifts, cycle time changes — before a human would notice.

DBA Deep Dive

Database Engineering — The Foundation That Never Fails

Our Database Engineering practice covers the full stack — from schema design and normalisation through to performance tuning on billion-row manufacturing historians. We have delivered databases that serve 24/7 factory operations with zero downtime requirements.

Architecture & Design
Entity-relationship modelling, normalisation, partitioning strategy, indexing design and schema versioning for manufacturing data.
Performance Engineering
Query analysis, execution plan optimization, index tuning, query rewrites and caching strategies for sub-second response on large datasets.
High Availability & DR
Always-on clustering, log shipping, replication, failover configuration and tested disaster recovery procedures.
Data Migration
Legacy database migrations, version upgrades and consolidation projects — zero data loss, tested rollback plans.
Time-Series & Historian
OSIsoft PI, InfluxDB, TimescaleDB and custom historian implementations purpose-built for high-frequency sensor data.
Platforms & Technologies
SQL Server
Microsoft
PostgreSQL
Open Source
Oracle DB
Oracle Corp
MySQL / MariaDB
Open Source
InfluxDB
Time-Series
Azure SQL / Synapse
Cloud
AWS RDS / Redshift
Cloud
MongoDB
NoSQL
AI Model Development Pipeline
1
Data Collection
Raw signals from PLCs, sensors, historian, MES and ERP flow into the data lake
2
Feature Engineering
DBA team and AI team collaborate to extract meaningful features from raw signals
3
Model Training
Supervised and unsupervised models trained on labelled plant data in Python / ML frameworks
4
Validation & Explainability
Model accuracy, confusion matrices, SHAP values — plant engineers sign off before deployment
5
Deployment & Monitoring
Models deployed to edge or cloud, performance monitored, auto-retrained on drift detection
AI Team Deep Dive

Manufacturing AI — Built on Real Process Knowledge

Our AI engineers are not data scientists who have never been on a factory floor. They understand PID loops, cure curves, takt time and batch genealogy. That context is what separates models that work in demo from models that work in production.

Supervised ML
Classification and regression models for defect prediction, quality grading and failure classification. Trained on your labelled historical data.
Unsupervised & Anomaly
Clustering and density-based models for process anomaly detection — no labelled data required. Learns your normal, alerts on deviation.
Time-Series Forecasting
LSTM, Prophet and transformer-based models for demand forecasting, energy prediction and remaining useful life (RUL) estimation.
Explainable AI
SHAP, LIME and custom visualisation so plant engineers understand why the model made a prediction — critical for adoption and compliance.
Before & After

Without Data & AI vs. With It

Dimension
Without Data & AI
With Data & AI
Maintenance
Breakdown maintenance. Machines fail, production stops, engineers scramble. Spares not always available.
Predictive alerts 24–72 hrs ahead. Planned replacement during scheduled downtime. Zero production loss.
Quality
End-of-line inspection catches defects after they are made. Scrap and rework are accepted costs.
In-process AI quality prediction catches drift before a defect is produced. First-pass yield improves consistently.
OEE Reporting
OEE calculated in Excel the following week. By the time you see the problem, it has moved on.
Real-time OEE per machine, line and plant. Drill to root cause in seconds, not days.
Data Silos
SAP data, historian data and MES data in three systems with no single view. Reports built manually.
Unified data warehouse. Single query returns production, quality and maintenance history in one result.
Energy & Yield
Energy consumed, yield achieved — no one knows if either is optimal. Waste unquantified.
ML forecasting optimizes energy scheduling and identifies yield improvement opportunities automatically.
Decision Speed
Decisions based on gut feel and last week’s data. Actions taken too late to prevent problems.
AI-surfaced alerts and recommendations in real time. Operators act within the same shift.
Compliance Reporting
Batch records and quality data compiled manually. Audit prep takes days.
Automated compliance reports from live data. Any batch record reconstructed in under five minutes.
94%
AI Prediction Accuracy
on validated plant models
−60%
Unplanned Downtime
with predictive maintenance
−80%
Reporting Time
automated vs. manual
10×
Faster Decisions
live data vs. week-old reports
Tech Stack

Tools & Technologies

Database (DBA Team)

SQL Server 2019/2022
PostgreSQL 15+
Oracle 19c / 21c
MySQL / MariaDB
TimescaleDB
InfluxDB
OSIsoft PI
Azure SQL / Synapse

AI & ML (AI Team)

Python (3.10+)
scikit-learn
TensorFlow / Keras
PyTorch
XGBoost / LightGBM
Prophet / NeuralProphet
SHAP / LIME (XAI)
OpenCV / YOLO

Visualisation & BI

Power BI
Grafana
Tableau
Custom React Dashboards
Apache Superset
Kibana
D3.js
Chart.js

Data Engineering

Apache Kafka
Apache Spark
dbt (Data Build Tool)
Airflow Orchestration
Azure Data Factory
Pandas / Polars
Great Expectations (DQ)
REST / OData Ingestors

Cloud & Infrastructure

Azure ML Studio
AWS SageMaker
Azure IoT Hub
Docker / Kubernetes
FastAPI Model Serving
MLflow Tracking
Prometheus / Grafana
Git / CI-CD

Connectivity

OPC-UA / OPC-DA
MQTT
Ignition Historian
JDBC / ODBC
SAP RFC / OData
REST API Polling
CSV / Excel Import
Modbus / EtherNet-IP
Why QAS?

Data & AI That Actually Works in Production

Most analytics projects produce beautiful dashboards that nobody looks at after three months. We build systems operators use, models plant managers trust, and databases that DBAs can maintain — because all three are designed by people who understand manufacturing.

Dedicated DBA team — not an afterthought, a core practice
Separate AI team with manufacturing domain expertise
Models trained on your data, not generic datasets
Explainable AI — your team understands every recommendation
Long-term model maintenance and retraining included
2
Dedicated Teams
DBA + AI
94%
Avg. Model
Accuracy
50+
Analytics Projects
Delivered
< 90 days
First Model
to Production

Your Factory Data Is Talking — Are You Listening?

Book a free data maturity session. Our DBA and AI teams will assess your current data architecture, identify where AI can deliver the fastest ROI and outline a practical roadmap.

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