Kuala Lumpur, Malaysia
An advisory practice built around
careful reading of maintenance records
Insan was founded by people who spent time on plant floors and in engineering offices. We understand why a maintenance log matters — and why the engineer reading it matters more than any algorithm.
Back to HomeOur Story
Where Insan came from
Insan grew out of a straightforward observation: Malaysian maintenance teams were spending more time reading CMMS reports than acting on them. The volume of condition-monitoring data from rotating equipment, electrical assets, and utility systems had grown steadily, but the size of engineering teams had not.
Our founders had worked in facilities management and industrial consultancy across Selangor, Johor, and Sabah before setting up in Kuala Lumpur. What they noticed was that the most useful thing you could give a maintenance manager was not a dashboard — it was a well-written summary memo that had already done the tedious cross-referencing work.
That observation shaped what Insan does today. We deploy AI reading assists that sit at reading distance from your CMMS — drafting memos, flagging patterns, and leaving every decision to your engineers. The word insan means a human being in Bahasa Malaysia. That is where we put our emphasis.
Our Mission
What we are here to do
Reduce reading load without reducing engineering judgement
AI summarises. Engineers decide. That boundary does not shift.
Write outputs that look like maintenance daybooks
Plain language, asset IDs, dated entries. Nothing that looks like marketing material or a software interface.
Stay on the right side of JKKP and OSHA 1994
We track regulatory requirements so you can point to a clear paper trail during audits.
The People
The team behind Insan
Small on purpose. Every engagement is handled by people with direct plant-floor and engineering experience.
Ahmad Hafiz
Principal Adviser
Fifteen years in industrial facilities management across Peninsular Malaysia. Led CMMS migrations for three manufacturing groups before founding Insan.
Suraya Roslan
AI Integration Lead
Previously a senior engineer at a utilities company in Selangor. Specialises in translating condition-monitoring data into practical AI reading configurations.
Kavindran Vijayan
Compliance & Standards
Background in occupational safety and JKKP audit support. Ensures every engagement meets OSHA 1994 obligations and customer audit expectations.
How We Work
Standards we hold ourselves to
These are not marketing claims. They are working boundaries we explain to every client at the start of each engagement.
Read-Only Access Policy
Our AI integration connects only to CMMS read endpoints. No write access. No connection to operational control networks. Documented and auditable.
Engineer Sign-Off Required
Every AI-drafted memo requires a named engineer to confirm it before it is used in any maintenance planning context.
JKKP & OSHA 1994 Alignment
We check our outputs and processes against JKKP guidelines and OSHA 1994 provisions relevant to maintenance operations in Malaysia.
Data Privacy Boundaries
Asset data and maintenance records are processed under a written data handling agreement. No client data is used to train external models.
Quarterly Boundary Reviews
For ongoing clients, we review the boundaries of AI involvement every quarter and produce a written summary for the head of engineering.
Bilingual Working Standard
All training materials and memo templates are available in English and Bahasa Malaysia to ensure usability across your full maintenance team.
Our Expertise
Maintenance knowledge behind the advisory work
The maintenance sector in Malaysia carries a distinct character. Plants run long shifts. Engineering teams are lean. CMMS platforms vary widely between facilities — SAP PM in some, Maximo in others, bespoke in-house systems in a few. Any AI assist that cannot adapt to that variety is more friction than help.
At Insan, we spend the first part of every engagement reading what your current records actually look like. Not what the CMMS vendor says they should look like — what they actually say, in the language your technicians use, with the asset ID conventions your facility has built up over time.
Predictive maintenance in an advisory context means something specific to us: we are not placing sensors or building dashboards. We are helping your existing condition-monitoring data get read more carefully, more consistently, and with a shorter path from observation to written recommendation.
We work most often with maintenance managers who are responsible for rotating equipment, electrical distribution assets, or building services in facilities with 50 to 500 assets under management. If that describes your situation, we are likely a reasonable fit.
Ready to have a conversation?
We start most engagements with a short call. No pitch deck, no demonstration software — just a discussion about your current maintenance reading load and whether a careful AI assist might help.
Get in Touch