ParallaxAIStart a conversation

AI integration · Melbourne, Australia

AI has leapt ahead. Most businesses are still in the chat window.

Parallax AI turns frontier models into systems that run inside your operations: connected to your data, built around your workflows, private by design and measured against real results.

Start a conversationSee our work

Why now

The capability gap is the opportunity.

Supply

The models keep leaping

Every few months a new generation arrives that can read, reason, write and act across more of a business than the last. What was impossible a year ago is routine now.

Demand

Businesses are stuck at the prompt

Most firms have work that could be transformed: documents to review, data to reconcile, decisions to prepare. Without a way to get there, a chat window is where most of them stop.

Model capabilityBusiness adoptionthe gap we close

Connected

Wired to your documents, data and systems, not fed by copy and paste.

Embedded

Running inside the workflow where the work happens, at the volume it happens.

Accountable

Private by design, measured against results, with people signing off where it matters.

A chat window is the tip of what these models can do. The value is in systems that sit where the work is.

Live demo

Watch the AI never see a name.

Type anything a client might send you, or pick a sample. Names, numbers, emails and addresses become tokens before a model sees a word, then come back for your team. Nothing you type leaves this page.

de-identify · runs on this device

On your systems

Editable

5

removed on your device

0 sent

What the AI sees

Tokens only
12/03 · Letter to PERSON_1 re settlement offer · 0.4h 14/03 · Call with Mr PERSON_2 on PHONE_1 · 0.2h 15/03 · Inspect ADDRESS_1 · 2.0h 16/03 · Email orders to EMAIL_1 · 0.3h

Found 2 people, 1 phone number, 1 email, 1 address.

The AI's draft

Draft objections for review: 1. 15/03, inspection at 14 Elm Street, Carlton VIC 3053: 2.0h is excessive for a site inspection. Allow 1.0h. 2. 14/03, call with David Okafor: reasonable, no objection. Once approved, send the schedule to Sarah Chen at sarah.chen@example.com.

Names put back on your side, after the AI has finished. Simulated reply: no AI is called on this page.

This demo matches patterns so it can run in your browser. In the systems we build, a dedicated de-identification model finds the details, and a final check blocks anything identifying from being sent.

Approach

From first conversation to a system you can measure.

  1. 01

    Find the leverage

    We map how work actually gets done and where the hours and money go, then pick the processes where AI changes the economics, and say plainly where it doesn't.

  2. 02

    Design for trust

    Privacy, human review and how success is measured are settled before any code: what data goes where, who approves what, and what good looks like.

  3. 03

    Build and integrate

    We build on the strongest frontier models and wire them into the tools you already use, so the system works where your people work.

  4. 04

    Prove it, then scale

    Every system ships with a test bench: accuracy, time saved and cost per task, measured on your cases. Then we harden it and roll it out.

Services

What we do.

One partner from the first question to a system in production, so nothing gets lost between strategy and engineering.

AI opportunity mapping

A clear-eyed look at your operations: where AI moves the needle, what it would take, and what it's worth.

Custom AI systems

Purpose-built tools on frontier models: document review, analysis, drafting, and agents that take action.

Integration and automation

Wired into what you already run: files, inboxes, practice software, spreadsheets and databases.

Evaluation and safeguards

Privacy by design, measured accuracy, and people in the loop wherever the stakes call for it.

Work

Built, tested, measured.

Case study · Legal

Law in Check: AI-assisted legal bill review

Law in Check is a Melbourne costs-law firm that reviews what clients were charged by their lawyers, mostly in family law. About two-thirds of a review is spent indexing line items to find the objections; a short-form review takes a day or two, a long-form one up to two weeks.

We built a system that reads the bill (scanned or not), removes every name, number and address on the firm's own computer, runs deterministic checks for rate, arithmetic and billing errors, and has Claude review each item for excessive time, unnecessary work and duplicated effort, processed in Australia. The output is a draft schedule of objections, with reasons, for a lawyer to approve.

  1. Bill PDF
  2. Names removed on-site
  3. Rule checks
  4. Claude, in Australia
  5. Draft schedule
of planted objections found (96% by dollar value)
97%
of planted objections found (96% by dollar value)
of flagged objections correct
92%
of flagged objections correct
identifying details sent to the model
0
identifying details sent to the model
per page of bill reviewed
~2¢
per page of bill reviewed

Measured on fictional test bills with known answers. A pilot on real matters is next.

Product · Careers

PivotPath

“Is AI coming for your job?” A personalised AI-risk score for your role, safer career pivots and a 90-day action plan, in about a minute.

Try PivotPath

R&D · Finance

Nexus Capital

An AI-native quant research desk: teams of AI agents covering research, modelling, risk, portfolio construction and forecasting, trading on paper while it proves itself.

In development

Principles

How we hold ourselves to account.

Privacy by design
Identifying details are removed before any model sees them, and processing stays onshore when it matters.
Measured, not promised
We test against known answers and report the numbers, including the misses.
People stay in charge
AI drafts, your experts decide. Every output is reviewable and every decision traceable.
Built to improve
Model-agnostic by design, so your system gets better as the models do.

Team

Four years watching the frontier. Now building on it.

We're Erik and Dante, commerce students at the University of Melbourne majoring in finance and economics. We started following AI in Year 10, back when it was a curiosity, and have tracked every step since.

Business training on one side, hands-on building on the other: we look at what a model can do for the numbers, not just in a demo.

Have a process that eats your team's hours?

Tell us about it. We'll tell you honestly whether AI can change it, and what that would take.

hello@parallaxai.com.au

Melbourne, Australia