RELIABILITY ENGINEERING × INTELLIGENT AUTOMATION
Turn fragmented plant records into reliable, structured asset intelligence. ViliAI combines hands-on reliability engineering with AI-assisted workflows to build, clean, validate, and put industrial data to work.
THE PROBLEM WE SOLVE
Most industrial facilities already have the information they need—scattered across drawings, spreadsheets, OEM manuals, field notes, and CMMS records. The challenge is converting it into accurate, consistent, usable data without losing the engineering context.
We connect the source documents, technical judgment, and scalable automation needed to close that gap.
WHAT WE DO
Practical engineering deliverables, supported by automation and defensible quality checks.
Develop equipment and functional location structures from P&IDs, equipment lists, drawings, and walkdown data.
Turn inconsistent legacy records into clean, governed, implementation-ready datasets.
Build and reconcile maintainable equipment BOMs using field evidence, OEM documentation, and existing inventory records.
Translate equipment functions and failure modes into practical, risk-informed maintenance strategies.
Prepare structured asset and maintenance data for implementation, migration, and ongoing governance.
Use AI-assisted extraction, matching, and analysis to accelerate repetitive technical work while keeping engineering review in the loop.
OUR APPROACH
Speed is valuable only when the output can be trusted.
Define the equipment scope, governing standards, source hierarchy, and required output format.
Use engineering knowledge and AI-assisted workflows to organize records, identify relationships, and develop deliverables.
Reconcile against source evidence, flag uncertainty, and perform structured technical quality checks.
Provide review-ready data, clear exceptions, and implementation-focused outputs aligned with client systems.
WHY VILIAI
ViliAI is built around a simple principle: advanced tools should strengthen engineering judgment, not replace it.
Our focus is the technical work that industrial teams struggle to complete at scale—building asset structures, developing equipment data, reconciling parts, improving maintenance strategies, and resolving data-quality issues that undermine reliability programs.
We combine practical reliability experience with AI-assisted execution, disciplined source review, and transparent quality control.
LET'S TALK
Tell us what you're working with—P&IDs, equipment lists, manuals, spreadsheets, or existing CMMS exports—and what the finished deliverable needs to look like.
Contact details will be added before launch. No form submissions are collected in this draft.
Asset hierarchy builds
Legacy data cleanup
BOM and PM development
Engineering workflow automation
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