Case study
AI-assisted lease abstraction for a national Australian property management group
The challenge
Lease abstraction, pulling key terms such as rent review dates, renewal options, outgoings and make-good obligations out of lease documents into the group's management system, was done manually by property managers as a side task to their day-to-day tenant and landlord work. With a portfolio in the thousands of tenancies, this created a persistent backlog, inconsistent data entry between offices, and, on a small but real number of files each year, missed rent review dates that cost landlords money and drew complaints. Routine tenancy document processing, bond lodgements, condition reports and renewal paperwork, faced the same bottleneck.
What Corpshore did
The engagement launched with six document processors in Manila and one Adelaide-based quality reviewer working alongside a dedicated account manager. As the group brought its full national portfolio onto the service over the following year, the offshore team grew to fourteen processors organised by state, with two Adelaide-based reviewers now covering quality assurance and liaising with each state office's property management leads. The offshore team handles the bulk data extraction and first-pass lease abstraction, working from scanned and digital lease files supplied by the group's state offices, while the onshore team sets abstraction standards specific to Australian lease conventions and reviews outputs before they are finalised.
Delivery model
Hybrid delivery, an offshore document processing team in Manila with onshore Adelaide-based quality review as the final checkpoint before anything is finalised.
Compliance handling
Lease and tenancy files routinely contain personal information about tenants, landlords and guarantors. Under the Privacy Act 1988 and Australian Privacy Principle 8, disclosing that information to an overseas recipient, including an offshore outsourcing provider processing it, requires the Australian entity to take reasonable steps to ensure the overseas recipient does not breach the APPs, and the property group remains accountable under section 16C if the offshore team mishandles it. The group addressed this with a data processing agreement setting access controls, restricted document retention periods and a strict no-onward-use clause, and by keeping the Adelaide team as the final checkpoint before any abstracted data is relied on operationally. No lease abstraction output is treated as final until it has passed the onshore quality review step. Corpshore's broader security practices are aligned with the Essential Eight framework, though no certification or maturity level is claimed.
Results
- 51%
- Lower cost per lease processed
- ~6 hrs/wk
- Freed per property manager for tenant and landlord work
- 1.5%
- Abstraction error rate, down from an estimated 6%
Average lease abstraction turnaround fell from roughly three business days per file to under 24 hours. A sampled accuracy audit six months into the engagement found abstraction error rates had dropped from an estimated 6 percent under the fully manual process to under 1.5 percent post-review. Property managers freed from abstraction work reported roughly six additional hours per week available for tenant and landlord-facing tasks, and cost per lease processed fell by around 51 percent compared to the group's prior fully manual, property-manager-led approach.
Lease abstraction turnaround
| Stage | Value |
|---|---|
| Before | 72 hrs |
| After | 24 hrs |
Before figure converts roughly 3 business days.
Our property managers were never meant to be data entry clerks. Getting that work off their desks let them actually manage properties again.
Why it worked
Property managers were never meant to be data entry clerks, and putting a dedicated onshore reviewer between the offshore extraction work and the group's own systems is what let the group trust the output enough to actually stop doing it manually.
Draft for Frank to validate against a real engagement before publishing. The client is described by industry and size rather than named, and the metrics stated here are conservative, plausible estimates, not audited figures.
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