Document Intelligence Engine · Perth
If your product is a document, we build the production line.
Every business has documents it produces over and over. For some the document is the product itself: valuations, inspection reports, tenders, clinical notes, compliance packs. We build engines that ingest your records into a structured model, remember everything, retrieve the right evidence, draft the document in your structure and voice, and fact-check every claim against the source. The same engine points at any repeated documenting task, and there are millions of them. Your people review and sign off, instead of starting from a blank page.
Start with a conversationWho this is for
Repeated document burden, or commercially unforgiving deadlines
The recurring-document business
Valuers, building inspectors, architects, engineers, lawyers, conveyancers, brokers, HR advisers, coaches and clinicians: businesses whose product is effectively a recurring document. The document burden is the growth ceiling, and it is exactly what the engine removes.
The deadline business
Tenders, panel applications, proposals and compliance responses, where response time is commercially sensitive and a missed requirement is disqualifying. Our tender response engine is this system pointed at bids.
These are patterns, not categories. Board reports, grant applications, safety documentation, case summaries, handover notes, quarterly reviews: if your business writes the same kind of document more than once a month, it is a candidate. The examples on this page are simply where the engine already runs.
Responding to tenders? The tender response engine is this system pointed at bids: requirements parsing, evidence drafting, per-claim fact-checking and a compliance pass.
How the engine works
Five stages, from your records to a document you can stand behind
Structured capture
Your conversations, site visits, transcripts, forms and files are parsed into a structured record you can see and correct. Not a black box: extraction runs at a validated 85 to 90 per cent accuracy, and you confirm the rest, so the record is yours before anything is written from it.
Memory
The record compounds. Every job, session or bid adds to it, nothing is retyped twice, and the system knows more about your work each month it runs. This is the asset: most AI tools forget everything between uses.
Retrieval
When a document calls for evidence, the right material surfaces from the record, cited to where it came from. Your best past work gets reused instead of rewritten from scratch every time.
Documentation
Finished documents drafted in your structure and your voice: reports, letters, notes, bids, compliance packs. The engine drafts, it never decides. Every document reaches you as your work, ready for your judgement.
Verification
Every claim in the draft is fact-checked against the record it came from, and anything unsupported is flagged with a citation. In one of our production pipelines this pass caught five errors a human review had missed.
Why it holds up
Built for work where being wrong is expensive
Anyone can generate text. The hard part is producing documents your business can stand behind, at pace, without losing authorship of the work.
You stay the author
The engine drafts and assembles; your people review, edit and sign off. Nothing leaves as AI-generated. It leaves as your document, drafted from your records.
Fact-checked claim by claim
Generated text is cheap. Text you can stand behind is not. Every claim traces to the record behind it, so the document holds up when a client, evaluator or regulator pushes on it.
Private by architecture
Your records stay in your control: identifying details masked before the model sees them, content encrypted under keys only you hold, and for the highest bar, AI that runs inside hardware-secured enclaves.
Proof
The same engine runs in production today
This is the pattern behind our own products. ClientJourney parses clinical records into a structured schema and generates a practice's documentation from them, fact-checked against the records with citations; it is projected to cut a psychologist's documentation burden from around fifteen hours a week to around two. ConfideAI runs the same discipline inside hardware-secured enclaves for regulated clinical work.
The verification layer is not theoretical either: in one production pipeline, the fact-check pass caught five errors a human review had missed. And the approach moves fast when it needs to: complete compliance document packs built on this pattern have been delivered in as little as three days.
Two ways to build
Start with a brain, grow into an engine
You do not have to commit to a full build to get value from this. An audit maps your document workflow and tells you which path fits: you build it with our coaching, or we build the engine.
Start with a brain
$500–$990/moWe structure your records and evidence into a local brain and coach you to run your own AI on it, delivered as an AI Working Sessions engagement. Cost-effective, you own it, month to month with no lock-in. Many businesses find this is all they need.
Grow into an engine
From $20,000When you want the production line automated, we build the full engine: structured capture, memory, retrieval, documentation and verification, running as a system. The brain you built first becomes its foundation.
Step one is never throwaway. The brain you build by hand is the exact structure the engine is built on, so you only ever pay to go forward, never to start again.
From our thinking
The methodology behind the engine
How we build systems that produce work you can stand behind.
Questions
Common questions
How much does a document intelligence engine cost?
It starts with an audit ($1,000 to $2,000) that maps your document workflow and tells you which path fits. The cost-effective path is a coached brain, an AI Working Sessions engagement where we structure your records and get you running your own AI on them, from $500 to $990 a month. The full engine is a custom build from $20,000, depending on the volume and complexity of your documents. The brain you build first becomes the foundation of the engine, so step one is never wasted. All prices are in Australian dollars.
What kinds of documents can it produce?
Any recurring document drafted from your own records: valuation and inspection reports, tender and proposal responses, clinical and coaching notes, referral letters, file notes, compliance packs and management reports. If your business produces the same kind of document again and again, it is a candidate.
Does the AI write the documents for us?
It drafts them from your own records and evidence, fact-checks every claim, and flags anything unsupported. Your people review, edit and sign off. The engine removes the blank page and the assembly, not the judgement. You stay the author and the document stays yours.
How is this different from using ChatGPT?
A chat tool forgets everything between sessions and invents what it does not know. The engine is built on structured memory: your records, parsed into a model you can see, compounding over time, with every generated claim checked back against them. The model is the commodity; the structured record and the verification around it are the asset.
Is our material kept confidential?
Yes, by architecture rather than policy. Identifying details are masked before content reaches a model, records are encrypted under keys only you hold, and for regulated work the AI can run inside hardware-secured enclaves with cryptographic attestation. Commercially sensitive material does not leave your control.
Has this actually been built, or is it a concept?
The same pattern runs in production today: our clinical system parses records into a structured schema, generates documents, and verifies each against the records with citations. The verification layer caught five errors a human review had missed in one production pipeline. Compliance document packs built this way have been delivered in as little as three days.
Bring us the document you dread
Show us the report, bid or pack that eats your week and we will show you what the engine would do with it. The prototype runs on your real documents, not a demo. No pitch, no pressure.
Start with a conversation