Find the right procedure
Help teams search approved documents and identify relevant instructions, with source references, access controls and human review.
AI-enabled asset decisions
Explore our wider enterprise AI services →AI for maintenance, asset information and field work.
Help maintenance teams find procedures, identify asset-data issues and prioritise work. Hastha assesses the task and information available, designs the workflow and tests the proposed solution with the people who will use it.
Practical applications
Where AI can help asset teams.
Review asset information
Flag missing, inconsistent or potentially duplicated records for the responsible data owner to review and correct.
Support maintenance planning
Use suitable work history, condition and operating data to identify patterns and support prioritisation. Planners remain responsible for approving the work.
Asset information
Start with usable asset information.
Check the records needed for the task: asset identifiers, work history, condition readings and operating context. We assess whether those records are reliable enough for the proposed use and identify what needs to be corrected.
- Agree the meaning of asset fields and maintenance events.
- Test representative records, including incomplete and difficult cases.
- Assign responsibility for correcting records and reviewing limitations.
Maintenance workflows
Make the result useful to the maintenance team.
An alert is useful only if someone can review and act on it. We design how the result reaches a planner or maintainer, what information they see and how an approved action updates the work-management system.
- Connect the signal to a role, workflow and response time.
- Show uncertainty and provide human review, escalation and override options.
- Measure how the result affects the work, as well as its technical accuracy.
Reference architecture
Connect AI to the systems and records teams use.
The diagram shows how user tools, AI, integration and systems of record fit together. Access, privacy, human review and monitoring must work across these layers; the platform choice depends on your requirements.
Governance and deployment considerations
Assign responsibility for decisions.
Agree who reviews recommendations, who can approve action and how errors or exceptions are handled. Record the inputs, decisions and changes needed to explain the result.
For in-scope Australian Government AI use cases, check the applicable accountable-owner and impact-assessment requirements. From 10 December 2026, relevant Privacy Act changes add privacy-policy transparency requirements for certain automated decisions using personal information.
Australian Government AI policy · OAIC automated-decision guidance consultation. Applicability depends on the organisation and use case; obtain legal advice for your circumstances.
Check more than the hosting location.
Data residency alone does not determine sovereignty. Review provider jurisdiction, support access, subprocessors, encryption and key control, telemetry, retention and deletion against your requirements.
OAIC cross-border disclosure guidance · US Department of Justice CLOUD Act resources.
Address data gaps before extending a test.
If the available records cannot support a useful evaluation, narrow or pause the test. Agree who will fix the gaps and how readiness will be checked again. A successful demonstration alone does not establish production readiness.
Use-case readiness
Check the use case before rollout.
Readiness depends on the decision, data, people and operating constraints. Use these six checks to determine what to test, what to fix and when to stop.
Frequently asked questions
Questions before you begin.
Do we need a large AI programme to begin?
No. Start with one useful task, representative data and the people responsible for the work. A focused test helps establish whether the proposed approach is worth developing further.
Can the proof use our approved platform and security boundary?
We assess your approved platform, data-access rules and security boundary before confirming feasibility. The proof proceeds only after deployment requirements, constraints and responsibilities are agreed.
What happens if the data is not ready?
We identify the gaps and their effect on the proposed use case. If the available data cannot support a useful test, we recommend narrowing the scope or addressing those gaps before proceeding.
Talk to us
Discuss an asset-management task for AI.
Tell us where your team is spending time or struggling to use information. We help assess whether AI, a process change or better integration is the appropriate response.