Malaysia · AI automation
Automate the work nobody wants to do twice.
The automation that pays back in a Malaysian mid-market business is rarely the impressive kind. It is document capture that stops someone re-keying supplier invoices, reconciliation that runs before anyone asks, and reports that assemble themselves. Unglamorous, high volume, and checkable — which is exactly the profile that survives contact with production.
Before and after
What changes when
the data finally talks.
Today's friction
- Supplier invoices re-keyed by hand into the accounting system
- Bank and supplier statement reconciliation done manually each month
- Reports rebuilt from the same exports every reporting cycle
- MyInvois submissions prepared separately from the accounting record
- Staff time going to transcription between systems rather than judgement
- Errors found at close rather than when they were made
With Ontilus
- Documents captured, validated and posted with exceptions queued for a person
- Reconciliation run continuously, with only the breaks surfaced
- Reports generated from governed data rather than rebuilt each cycle
- Invoice data validated once and reused for submission
- People moved onto exception handling and analysis
- Errors surfaced when they happen, with a record of what the system did
What we build
Engineered for AI automation.
Every system is bespoke to your entity structure. These are the modules this sector almost always needs.
Document capture
Supplier invoices, delivery orders and statements read, validated against the purchase record, and posted — with anything ambiguous held for a person.
Reconciliation
Bank, supplier and inter-company matching run continuously, surfacing only genuine breaks with the reason attached.
Report assembly
Recurring management reports generated from governed data, with lineage back to the source record.
E-invoicing flow
Validation before submission, so the expensive failure — a rejected submission — is caught while it is still cheap to fix.
Exception queues
Everything the system is not confident about routed to a named person, rather than guessed at.
Run records
Inputs, output, model version and approver stored for every run, so an error is investigable rather than mysterious.
Questions
AI automation,
answered.
The questions operators in this sector ask first. If yours isn't here, ask us directly — we answer scoping questions before there's a contract in sight.
Talk to usWhat should we automate first?
High-volume, low-variance work whose output can be checked — document capture, reconciliation and report assembly. Sequence by how checkable the output is rather than by how much manual effort it consumes, because a process whose output nobody can verify will keep a human in the loop anyway and pay back far more slowly.
Will this work with our existing accounting system?
In most cases yes. SQL Accounting, AutoCount and Million are the common stacks here and all can be read from, whether through an interface or a scheduled export with a local sync agent. Automation is built over the system of record rather than replacing it, and results are written back into what your team already opens.
What happens when the automation gets something wrong?
It should be designed for that from the start. Anything the system is not confident about goes to a hold-back queue for a person instead of being guessed at, irreversible actions require approval, and every run stores its inputs, output, model version and approver so an error can be traced rather than argued about.
How much does back-office automation cost to run?
Three lines: AI inference, infrastructure and integration — plus supervision, which is permanent rather than a first-year cost. The running figure should be sized against your actual document and transaction volume and quoted before signature, not discovered afterwards.
How long before it pays back?
Longer in finance and operations than in customer-facing work, and that is rational: where output touches an audit trail, more human review is deliberately retained. Any business case that models supervision as temporary will overstate the payback.
How we deploy
Live in 12 weeks.
Proven before it scales.
We pilot on one entity and validate against your own close reports before anything goes group-wide. See the full deployment approach →