// ABOUT INDUCTA
Practical AI for
Industrial Operations
We work alongside manufacturing engineers in Singapore to build data models that reflect the reality of their specific facilities — not generic templates adapted to fit.
BACK TO HOME// OUR STORY
Built from the Production Floor Up
Inducta was established in Singapore with a clear intent: to make the practical benefits of applied machine learning accessible to manufacturers who do not have in-house data science teams, and who are rightly cautious about generic AI tools that have not been shaped by the realities of their operations.
Our founders came from process engineering and industrial informatics backgrounds. That combination means we understand what a maintenance engineer actually needs from a predictive model — interpretable outputs, clear uncertainty indicators, and guidance that fits within how decisions are already made on the floor.
Every engagement we undertake starts with the client's data, their equipment, and their operational language. We do not arrive with a standard approach and fit it to the brief. We scope each project around what the data can actually support and what the team can realistically use.
Based at Changi Business Park, we work with manufacturing facilities across Singapore and the broader Southeast Asian region. Our engagements are fixed-scope, fixed-price, and always delivered with documentation structured for the people who will use the outputs day to day.
Mission
To help manufacturing organisations in Singapore and the region build a genuine, evidence-based understanding of how AI can support their processes — starting from where they actually are, not where a vendor wants them to be.
Values
- Interpretability over complexity — models your team can act on
- Honest scoping — we work within what your data can support
- Engineering-first framing — outputs in operational, not statistical terms
- Structured confidentiality from project initiation to close
Approach
We treat each engagement as a collaborative technical exercise. Clients contribute domain knowledge and data access; we contribute modelling rigour and structured documentation. The output belongs entirely to the client and is designed to stand on its own once the engagement concludes.
// CORE TEAM
The People Behind the Work
Wei-Lin Tan
PRINCIPAL — DATA SCIENCE
Former industrial informatics engineer with twelve years across semiconductor and precision manufacturing sectors. Leads model development and validation on all engagements.
Arjun Krishnamurthy
PRINCIPAL — PROCESS ENGINEERING
Process engineer background across chemical and electronics manufacturing. Ensures that modelling outputs are framed and documented in terms maintenance and operations teams can act on directly.
Siti Hasnah
ENGAGEMENT MANAGER
Manages project scoping, data handoff protocols, and client communication across all active engagements. Background in industrial project coordination across Singapore and Malaysia.
// STANDARDS & PROTOCOLS
How We Maintain Quality
Every engagement follows a consistent set of technical and operational protocols that govern how we handle data, structure models, and communicate findings.
Data Confidentiality
Non-disclosure agreements are executed before any data transfer. Client operational data is handled in isolated project environments and deleted after final delivery unless retention is explicitly agreed.
Model Validation Protocol
All predictive models are validated on held-out data before delivery. Validation metrics are reported alongside model outputs so clients understand the performance envelope under their specific operating conditions.
Structured Documentation
Every deliverable includes a technical documentation package covering data inputs, modelling decisions, assumptions, and guidance on appropriate use. Designed to remain useful to the client team without our ongoing involvement.
Personal Data Compliance
Engagements that involve any personally identifiable information are handled in accordance with Singapore's Personal Data Protection Act. Data minimisation principles apply throughout.
Reproducible Methodology
Analysis pipelines are written with reproducibility in mind. Code and configuration used in each engagement are documented and can be re-run against updated data by the client's own team where applicable.
Regular Progress Communication
Structured check-ins at defined project milestones keep stakeholders informed without creating overhead. Written summaries accompany each milestone review so nothing is lost between conversations.
// EXPERTISE & CONTEXT
Applied Machine Learning for Singapore Manufacturing
Manufacturing operations in Singapore and the broader Southeast Asian region face a particular challenge when evaluating AI adoption: the gap between what is commercially promoted and what is operationally achievable within a specific facility's data infrastructure. Many manufacturers have invested in sensor arrays and data historians but lack the internal analytical capability to extract structured insight from those systems.
Inducta works specifically at this intersection — taking the sensor data, maintenance logs, and production records that facilities already generate, and building interpretable models that help engineering teams make better-informed decisions about equipment maintenance scheduling, quality parameter monitoring, and process adjustment. The emphasis throughout is on outputs that engineering professionals can evaluate critically and act on confidently, rather than black-box predictions they must accept on trust.
The Singapore manufacturing sector spans precision engineering, electronics, pharmaceuticals, and advanced materials — each with distinct data characteristics and modelling constraints. Our team draws on direct experience across these subsectors, which informs how we approach data quality assessment, feature selection, and the framing of model outputs for different operational audiences.
// NEXT STEP
Ready to discuss your facility?
Start with a conversation. We'll listen to what your operation looks like and indicate honestly whether an engagement is likely to be useful.