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Industrial solutions

// OUR SOLUTIONS

Three Engagements.
One Focus: Your Facility.

Each service addresses a distinct question that manufacturing operations in Singapore commonly face when considering how data analysis and machine learning can support their processes.

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// METHODOLOGY

How We Structure Every Engagement

Every Inducta engagement follows the same sequence: a scoping conversation to understand your data and operational context, a data quality assessment to determine what the analysis can reliably support, the modelling or assessment work itself, and a structured delivery phase that includes documentation written for the people who will use the outputs.

We work incrementally and communicate at defined milestones rather than disappearing for weeks and reappearing with a presentation. If the data reveals constraints that affect scope, we address those with you rather than working around them silently.

PHASE 01
Scoping
Understand context, data availability, and operational objectives
PHASE 02
Data Review
Assess data quality, coverage, and analytical feasibility
PHASE 03
Analysis / Modelling
Build, validate, and document analytical outputs
PHASE 04
Delivery
Structured handoff with engineering-framed documentation
SVC-01

Predictive Maintenance Modelling

SGD 2,600 / fixed-price engagement

This engagement develops a machine learning model to identify patterns in your equipment sensor and maintenance history data that precede failures or performance degradation. The model is built and validated specifically on your machinery and operating conditions, not calibrated from generic industry templates.

Delivery includes clear guidance on how the model's outputs should inform — not replace — the judgement of your maintenance engineering team. We also include a review of your data collection setup and recommendations for improving signal quality where the current instrumentation creates gaps or noise that limits analytical value.

Model built on your specific sensor and maintenance log data
Validated on held-out data before delivery, with reported performance metrics
Data collection review and signal quality recommendations included
Documentation written for maintenance engineers, not data scientists
No ongoing licence or platform requirement after delivery
TYPICAL TIMELINE
6 – 10 weeks
DATA REQUIRED
Sensor logs + maintenance records
ENQUIRE ABOUT THIS SERVICE
Predictive maintenance

// PROCESS STEPS

01.Equipment data intake and quality review
02.Feature engineering from sensor signals
03.Model development and cross-validation
04.Threshold and alert parameter setting
05.Delivery with engineering guidance document
Quality analytics

// PROCESS STEPS

01.Production and quality record intake
02.Variable correlation and exploratory analysis
03.Interpretable model build (regression / tree-based)
04.Key driver identification and ranking
05.Findings report in process engineer language
SVC-02

Production Quality Analytics

SGD 1,480 / fixed-price engagement

A structured data analysis engagement to identify the process variables most associated with quality outcomes in your production environment. Working from your existing production and quality records, we build an interpretable model that your process engineers can use to understand which parameters to monitor and adjust.

The findings are presented in the operational language of your team rather than the technical language of data science. Where the analysis reveals parameter interactions or threshold sensitivities, these are presented as practical monitoring guidance rather than statistical summaries.

Works from your existing production and quality records
Interpretable model — your engineers can see and evaluate the logic
Key quality driver identification with parameter importance ranking
Presented as process monitoring guidance, not a statistics report
Can serve as foundation for ongoing quality monitoring approach
TYPICAL TIMELINE
4 – 6 weeks
DATA REQUIRED
Production params + quality records
ENQUIRE ABOUT THIS SERVICE
SVC-03

Industry AI Readiness Assessment

SGD 560 / fixed-price engagement

A methodical review of your manufacturing operation's data infrastructure, sensor coverage, and organisational readiness for AI-assisted process improvement. Covers data availability by production line, integration between operational technology and IT systems, and the practical constraints that would affect any AI deployment in your specific facility.

Delivered as a findings report with a clear, prioritised set of foundational recommendations. If you are considering AI modelling work but are uncertain whether your data infrastructure is ready for it, this is the most practical place to begin. The assessment is structured to give you a clear, honest picture without commitments to further work.

Covers OT/IT integration status and data flow architecture
Reviews sensor coverage and data availability by production line
Assesses organisational readiness — team capability and change context
Delivered as prioritised findings report, not a generic checklist
No obligation to proceed to modelling work — stands alone as a useful output
TYPICAL TIMELINE
2 – 3 weeks
DATA REQUIRED
System documentation + facility access
BOOK THIS ASSESSMENT
AI readiness assessment

// ASSESSMENT SCOPE

01.Data infrastructure and historian review
02.Sensor coverage mapping by production line
03.OT/IT integration assessment
04.Organisational readiness evaluation
05.Prioritised recommendations report

// DECISION GUIDE

Which Engagement Fits Your Situation?

Use this table to identify which service matches where your facility currently stands.

YOUR SITUATION READINESS ASSESSMENT
SGD 560
QUALITY ANALYTICS
SGD 1,480
PREDICTIVE MAINTENANCE
SGD 2,600
Unsure whether your data is ready for AI
Quality variation is a recurring operational challenge
Unplanned downtime is a significant cost driver
Need to understand where to invest in AI first
Have historical sensor data and maintenance logs available
Have production parameter records and quality test data

Not sure where you fit? The readiness assessment will tell you — and at SGD 560, it is the most cost-effective way to answer that question before committing to modelling work.

// TECHNICAL STANDARDS

Shared Across All Engagements

Data Security

NDA executed before data transfer. Isolated project environments. Data deleted after delivery unless retention is explicitly agreed in writing.

Reproducible Analysis

All pipelines are versioned and documented. Analysis can be re-run against updated data by your team using delivered code and configuration files.

Honest Scope Assessment

If your data cannot support the analysis you are asking for, we communicate that in scoping — not after billing for an engagement that cannot deliver.

Code and Documentation Handoff

Every engagement delivers working code, a technical documentation package, and an engineering guidance document. You own everything delivered.

PDPA Compliance

Engagements involving personal data are managed in accordance with Singapore's Personal Data Protection Act. Data minimisation principles applied throughout.

Milestone-Based Communication

Structured written updates at defined project milestones. You stay informed without overhead, and nothing is lost between verbal conversations.

// GET STARTED

Not sure which engagement is right?

Send us a brief description of your facility and what you are trying to understand. We will tell you which engagement applies and what we would need to see from you before scoping begins.

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