Industry
AI for community services and NDIS
NDIS providers and community service organisations run on documentation: service agreements, progress notes, incident reports, claims and the evidence behind all of it. AI automation handles the paperwork around care delivery so support workers spend more of their shift with participants and less of it typing.
We integrate with
- Lumary
- ShiftCare
- Brevity
- Deputy
- Xero
- Microsoft 365
- SharePoint
- PRODA / PACE
- and more
The processes we automate in community services and NDIS
Progress notes from the shift, not the evening
A support worker records a short voice note at the end of a shift. It becomes a structured progress note against the participant's goals, in the right format for your system, ready for the worker to review and confirm. The note gets written while the detail is fresh instead of at nine that night.
Incident reporting and NDIS Commission obligations
An incident report captured quickly, classified against reportable-incident criteria, the notification window calculated, the record created and the notification drafted for a qualified person to review and submit. Missing a reportable incident deadline is a serious regulatory matter, so the human gate is not optional.
Claims preparation and reconciliation
Service delivery matched to the participant's plan and the price guide, claims assembled and validated before submission, and rejections triaged with the reason identified rather than landing in a queue for someone to decode.
Rostering administration
Not the rostering decision, the administration around it. Shift confirmations, unfilled-shift chasing, timesheet reconciliation, award interpretation checks and the constant stream of changes that eats a coordinator's day.
Service agreements and plan changes
New and varied plans read, service agreements drafted against them, budget allocations checked, and the participant record updated so what is being delivered matches what is funded.
Worker compliance and screening
Worker screening clearances, first aid, qualifications and mandatory training tracked against expiry, with reminders early enough to renew rather than notifications after a clearance has lapsed.
The documentation burden falls on the wrong people
In most community services organisations, the administrative load lands on support workers and coordinators, the people whose actual value is in the room with a participant. Progress notes written after hours. Incident reports typed on a phone in a car park. Coordinators spending mornings chasing shift confirmations.
This is a retention problem as much as an efficiency one. Sector turnover is high, and unpaid documentation time is consistently among the reasons people give for leaving. Time returned here goes somewhere that matters twice.
Privacy is the first design constraint, not a review at the end
This sector handles some of the most sensitive personal information in the country: health data, disability information, incident details involving vulnerable people. Any AI system touching it has to be designed around that from the first architectural decision.
- Data handling boundaries for participant information, documented and agreed rather than assumed
- Enterprise rather than consumer platforms, with the training and retention terms checked in writing
- Role-based access enforced in the system, so a worker sees only their participants
- De-identification wherever the task does not require identity
- Audit logging: who accessed what, when, and what the system did with it
- Human review on every clinical, behavioural or incident-related judgement
What we will not automate
Clinical judgements, behaviour support decisions, risk assessments and participant-facing communication about sensitive matters stay with qualified people. Automation handles the documentation, the deadlines, the matching and the chasing.
Built for the funding reality
NDIS pricing is set externally and margins are thin. That has two consequences for how these systems have to be built: the payback has to be measured in months rather than years, and the ongoing running cost has to be small enough that it does not simply move the problem.
So we scope narrowly. One process, the one with the clearest hours saved, live and proven before anything else is considered. If the numbers do not work at that scale they will not work at a larger one, and finding that out cheaply is a good outcome.
Frequently asked questions
Is it safe to use AI with participant data?
It can be, and the architecture is the whole answer. That means enterprise rather than consumer platforms, the training, retention and hosting terms confirmed in writing rather than assumed, role-scoped access, de-identification where identity is not required, and audit logging. Which of those a given design needs, and what your obligations require, is worked out before anything is built. Consumer AI tools handling participant data would be a serious problem, which is exactly why a sanctioned governed option matters.
Will the NDIS Commission accept AI-assisted documentation?
The obligation is that records are accurate, complete and made by the responsible person, not that they were typed by hand. A worker reviewing and confirming a note generated from their own voice record satisfies that; a system generating notes with no human confirmation would not. We build to the former.
Our support workers are not technical. Will they use it?
That is the design constraint that matters most, and it is why the intake is a voice note rather than a form. If a system asks a support worker to learn an interface at the end of a twelve-hour shift, it will not be used, and no amount of capability compensates for that.
How does this handle claim rejections?
Prevention first. Validating service delivery against the plan and the price guide before submission catches most of what would be rejected. For the remainder, rejections are triaged automatically with the likely cause identified, so the person picking it up starts from a diagnosis rather than a code.
More questions answered on the full FAQ.
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Which of those is costing you most?
Tell us which one and we'll tell you straight whether it's worth automating, roughly what it would take, and what we'd do first.