Document search
AI document search for Australian businesses
The problem is not storage. You already hold every document. The problem is that finding the one that answers the question needs a person who was there at the time, and that person is in a meeting or has left.
What we build on
AI document search means every document converted to machine readable text and indexed on your own servers, so staff ask a question in plain English and get an answer that cites the document behind it. It is not a document management system and it does not replace the one you have.
Software we usually meet
- SharePoint
- Microsoft Copilot
- Procore
- Google Drive
- Glean
- Confluence
What Document search does
Questions in plain English
Someone asks what the scope said about temporary works on a job from 2022 and gets the answer, rather than a list of files to open and read.
Every answer cites its source
The answer names the document behind it. A person can check it, which is what makes it usable for a decision rather than a guess with good grammar.
Scanned and handwritten material
Site diaries, marked up drawings and photographed dockets. This is harder than office PDFs and we scope it as its own piece of work rather than assuming it.
On your own servers
A private model inside your environment. Where the work involves commercial terms or personal information, no document leaves the business.
Software or a custom build
If your files are already clean, current and inside SharePoint or Procore, then Microsoft Copilot or the search you already pay for may be enough, and we will say so in discovery. A build earns its cost when the material is scattered, scanned or old, when every answer has to cite its source, or when the documents cannot leave your environment.
How it works
Every step depends on the one before it, and skipping the first two is how these projects stall.
Audit what you actually hold
Where the documents live, how much is scanned rather than typed, and how much is superseded. What is out of date gets resolved before it is indexed, or the system answers confidently from a document nobody should be using.
Make it machine readable
Text extraction across the archive, with scanned drawings, site photos and handwriting treated as their own problem, because they are.
Index in place
Against the systems you already run. Migrations are their own project and folding one into this is how these programs die.
Match permissions to what people already have
A person sees through search exactly what they could see directly. IBM reported in July 2025 that 97 percent of organisations with an AI related breach lacked proper access controls.
Name an owner for each area
Every knowledge domain gets one named person rather than a committee. Without that the index goes stale and people stop trusting it.
In practice
Not for you if
- Your documents are already clean, current and in one system that searches them well.
- What you actually need is a document management system. Buy one.
- Nobody will own keeping it current. It goes stale and people stop asking it.
A Sydney construction business had thousands of job PDFs on their own servers that nobody could search. They can now ask why a supplier was chosen over another on a past job, and the answer comes with the quotes and the email thread that decided it, from a private model inside their environment.
Grounding is what separates this from a chatbot. Stanford researchers reported in January 2024 that general purpose models answering legal questions without retrieval produced incorrect information on a majority of queries. An answer that names its document can be checked. One that cannot is a guess.
Where the documents sit matters under Australian privacy law. The Office of the Australian Information Commissioner issued guidance in October 2024 on privacy and commercially available AI products. Hosting in Australia and processing in Australia are different claims, and vendors are not always careful about the difference.
Answering the same question for a customer rather than a colleague is the same build. Tracking, delivery and document requests get answered from your own systems, with a person approving what goes out.
Case studies
How it works
- 01
Intro call
A short call to talk through your objectives, goals and timeframes and agree what success looks like. No problem is too big or too small, whether that is construction estimation, materials procurement, job quoting or AI SEO.
- 02
Discovery
We sit with your team for an extended stretch to understand your business, the systems you run and how the work gets done day to day.
- 03
Roadmap
We put together a roadmap of action items, in order, that takes you from where you are today to what we agreed success looks like.
- 04
Execution
We do the work, check it against the goals we set on the first call and do not call it finished until you are satisfied.
Questions
What is AI document search?
AI document search means the documents your business already holds made machine readable and indexed, so staff ask a question in plain English and get an answer that cites the document it came from. It runs on your own servers with a private model.
Is this a document management system?
No. We index in place against the systems you already run, whether that is Procore, SharePoint or a shared drive. Nothing is replaced and nothing is migrated. Migrations are their own project and bundling one into this is how these programs die.
Can it read scanned drawings and handwriting?
Often, but not as reliably as a typed PDF, and we scope it separately rather than assuming it. Site diaries, marked up drawings and photographed dockets need more preparation than office documents, and we tell you which parts of the archive are difficult.
How accurate is it?
We do not publish an accuracy figure for our own systems, because we have not measured one we would stand behind. What we do instead is cite the source on every answer, so a person can check it rather than trust it. Treat a single accuracy number quoted without a method with caution.
Who can see what?
A person sees through search exactly what they could see directly. Permissions mirror the access they already have. This gets designed explicitly rather than assumed, because weak access control is the most common way these systems leak. We map it against the groups you already have.
Industries where this comes up
Other things we build
Next step
We map where your hours go and price the work that is worth automating.