Short answer: The Hyperfocus Method is how Hyperfocus Studio takes a messy business problem to a working AI system in four steps: the Idea Drop (diverge), validate and architect (converge), short visible sprints (build), and launch and scale (ship). You drop the problem, a human replies within 24 hours, and a recommendation with scope, price and timeline lands within 72 hours.
This is the process post. If a colleague asks how working with us would go, forward them this.
In this guide
The four steps: who does each one and what you get
The promises with numbers: 24 hours, 72 hours and 2 hours
The Chaos Meter and its five levels
The Clarity Session and the 10-minute prep
The Plan and the AI Solution, with price ranges
Our standards: what we do and what we never do
Why the team is neurodivergent by design
Why AI projects stall, and which step addresses each cause
A hypothetical example, week by week
What is the Hyperfocus Method?
The Hyperfocus Method is our four-step process for turning a problem into a working AI system: diverge, converge, build, ship. On our site the summary is eight words long. Go wide on ideas. Go deep on delivery.
Hyperfocus Studio is a Melbourne agency that builds custom AI systems into businesses: a plan to solve the problem, an AI solution that solves it, or both. The method is the same whichever one you buy. You bring the problem. We build the solution.
Problems arrive in six shapes: business and AI strategy, intelligent automation, custom platforms, data engineering, AI and LLM products, and technical rescue. All six go through the same four steps.
For background on what custom AI is and where it pays off, read our guide to custom AI solutions for business. For the general job description, see what an AI solutions agency does.
How does the Hyperfocus Method work, step by step?
It starts at the front door, then runs through four steps. Each step has an owner, an output and a typical duration.
Before step one: drop your problem
You send the problem at hyperfocus.studio/problem, typed or as a voice note of up to 3 minutes. The intake asks for two things: the problem and a work email. A human replies within 24 hours. The first step is a free 15-minute Clarity Session, which you book from the confirmation email.
The four steps
The Idea Drop (diverge). Strategists and engineers attack the problem independently. Who: the thinkers lead, with builders alongside. You get: 3 bold ideas, 3 practical ones and 1 clear recommendation. The recommendation names the path (the Plan, the AI Solution or both) with scope, price and timeline. How long: within 72 hours of the problem landing.
Validate and architect (converge). We pressure-test the winning idea against your data, systems and constraints, then design the architecture. Who: thinkers and builders together. You get: a design that has met your real data and, in a Plan, a costed roadmap. How long: the Plan typically takes 2 to 4 weeks.
Short, visible sprints (build). Working software every week, demoed live. Who: the builders, meaning developers, software engineers and AI experts. The Chief Time Wrangler owns the sprint plan and the deadlines. You get: software you can see and try every week. How long: most of the build timeline, which depends on the size of the system.
Launch and scale (ship). The system goes into production, documented and handed over properly, or supported by us as you grow. Who: the builders, with the Chief Time Wrangler keeping the date. You get: a working system in production and clean, documented code your team owns. How long: launch sits inside the timeline quoted for the build.
Step | What happens | Output | Typical time |
|---|---|---|---|
Front door: drop your problem | You send the problem by text or voice note. | A human reply | Reply within 24 hours |
01 Diverge: the Idea Drop | Strategists and engineers attack the problem independently. | 3 bold ideas, 3 practical ones, 1 clear recommendation | Within 72 hours |
02 Converge: validate and architect | The winning idea is tested against your data, systems and constraints. | A validated design and, in a Plan, a costed roadmap | 2 to 4 weeks for the Plan |
03 Build: short, visible sprints | Working software every week, demoed live. | Software you can see and try each week | Most of the build timeline |
04 Ship: launch and scale | Production launch and handover. | A system in production that your team owns | Inside the build timeline |
The two things you can buy map onto the steps. The Plan is steps one and two, worked up in full and costed. The AI Solution is steps three and four, plus step two if you skipped the Plan. For a small AI solution, the plan is included in the build. Many clients start with the Plan, then have us build the Solution.
What do we promise, in numbers?
We promise four numbers: 24 hours, 72 hours, 15 minutes and 2 hours. The 24-hour and 72-hour clocks both start the moment you drop the problem. The build gets its own timeline, in writing.
What | Promise | Clock starts |
|---|---|---|
A reply from a human | Within 24 hours | When you drop the problem |
A recommendation | Within 72 hours | When you drop the problem |
Clarity Session | Free, 15 minutes | Booked from the confirmation email |
Urgent callback | Within 2 hours, same day | When you flag it as urgent |
Demo of working software | Every week | During the build |
Urgent mode is for fires: production is down, a deadline just blew up, a project has gone off the rails, or an AI tool is misbehaving. Flag the problem as urgent and leave a phone number. A senior engineer calls back within 2 hours, same day. Urgent work runs at an urgent rate, confirmed on the call.
We do not promise one fixed delivery time for every project. A small automation and an enterprise system are different jobs. Typical ranges are in the price table below.
What is the Chaos Meter?
The Chaos Meter is a five-level dial that runs from Sensible to Unhinged genius. We use it to rate how messy a problem is and to agree how ambitious the answer should be, from proven and safe to properly transformational.
You can drag it yourself on our home page. The example problem there is a finance team whose month-end close takes two painful weeks.
Level | Example answer: month-end close | What changes |
|---|---|---|
1. Sensible | Automate bank and ledger reconciliation, with exceptions flagged for a human to review. | One existing task is automated. The process stays as it is. |
2. Curious | Add an AI assistant that drafts variance explanations for the team to check and approve. | AI drafts and people approve. The process gains a helper. |
3. Spicy | Move to a continuous close: reconcile every day, so month-end becomes a formality. | The rhythm of the process changes. |
4. Wild | Build a live finance cockpit where every number traces back to its source in one click. | The team gets a new place to work. |
5. Unhinged genius | A system that closes itself and only asks humans about the genuinely weird stuff. | The process is redesigned. People handle the exceptions. |
The level is your choice. A higher level usually means more change to how your team works, so there is more to validate in step two and more to build in step three. Level 5 is our favourite, and we will still recommend level 1 when level 1 is the right answer.
What happens in the Clarity Session?
The Clarity Session is a free 15-minute call where we make sure we understand the problem before anyone proposes a build. Nobody presents a slide deck.
The six clarity questions
What is the problem, in one sentence?
Who feels it, and how often?
What does it cost today, in hours, dollars or customers?
What have you already tried?
Which systems and data does the work pass through?
What would done look like, and is there a date attached?
The 10-minute prep
Ten minutes, five items.
Write the problem in one sentence. (2 minutes)
Put one number on it: hours per week, dollars per month or customers affected. A rough estimate is fine. (3 minutes)
List the systems involved: the tools, spreadsheets and inboxes the work passes through. (2 minutes)
Note what you have tried and why it did not stick. (2 minutes)
Name the person who signs off the budget. (1 minute)
Leave out passwords, health records and other people’s personal information. We do not need them to understand the problem. Every project is quoted individually after the Clarity Session.
The Plan or the AI Solution: what does each contain?
The Plan is a clear, costed plan to solve the problem. The AI Solution is the working system, built and handed over. You can buy either one, or both.
The Plan checklist
Root-cause diagnosis of the real problem
3 bold ideas, 3 practical ones and 1 clear recommendation
Business case, costs and expected return
A step-by-step roadmap your team can act on
The Plan is best when you need direction, buy-in or a decision.
The AI Solution checklist
Designed around how your team actually works
Built on your data, tested and secured
Launched into production, not left as a demo
Documented, handed over and supported
The AI Solution is best when you know the problem and want it gone.
Price ranges and typical timelines
All figures are in Australian dollars, ex GST.
Engagement | Range | Typical timeline |
|---|---|---|
Clarity Session | Free, 15 minutes | Same week |
The Plan | A$15,000 to A$45,000 | 2 to 4 weeks |
Small AI solution (plan included) | A$15,000 to A$30,000 (from about US$10,000) | 2 to 4 weeks |
Single custom AI agent | A$30,000 to A$75,000 | 4 to 8 weeks |
Production AI solution | A$90,000 to A$280,000 | 8 to 16 weeks |
Enterprise AI system | A$280,000 to A$900,000+ | 4 to 9 months |
Care plan | 15% to 25% of build cost per year, billed monthly | Ongoing |
Our AI solutions start at A$15,000 (about US$10,000 at 1 USD = 1.43 AUD, September 2026). A small AI solution is one focused agent or automation on a single workflow, and the plan is included in the build. These are ranges, not a rate card, and the timelines are typical, not guaranteed. Every project is quoted individually after the Clarity Session. We sit above the Australian market midpoint, because senior people do the work and the system ships working.
For what the wider market charges, see how much a custom AI solution costs in Australia. If you are still choosing a route, read AI agency vs in-house team vs off-the-shelf tools.
What are our standards?
Seven things we do and seven we never do. They sit on our site under the heading “What we do. What we never do.” and the table repeats them word for word.
We do | We never |
|---|---|
We start with the business case and the return. | Sell technology for technology’s sake. |
Put senior strategists and engineers on every project. | Hand you over to a junior bench. |
Deliver several genuinely different ideas, then one clear recommendation. | Settle for the first idea in the room. |
Ship working software every week and demo it live. | Disappear for six months and hope you like the surprise. |
Tell you straight when AI isn’t the answer. | Force AI into problems it doesn’t fit. |
Communicate in plain language, in writing. | Hide behind jargon and slide decks. |
Hand over clean, documented code your team owns. | Lock you in to keep you paying. |
Print this out. Hold us to it. Rows three and four are steps one and three of the method, written as rules.
Why a neurodivergent team?
The method needs two kinds of thinking, going wide and going deep, and we built the team to supply both. The team is neurodivergent by design: ADHD thinkers who take problems apart, engineers who build the solution, and a Chief Time Wrangler (who does not have ADHD) who keeps delivery on time.
Level | Who | What they own |
|---|---|---|
01 Planning and ideation | The thinkers | Shaping every Plan and deciding what is worth building |
02 Execution | The builders | Building every AI Solution |
03 On time, every time | The Chief Time Wrangler | Every timesheet, deadline and sprint plan |
Here is what the research says and what it does not say. Small studies by White and Shah link ADHD with higher divergent thinking, the idea-generating kind. Their 2011 study of 60 college students, half with ADHD, found the ADHD group preferred generating ideas, while the other group preferred clarifying problems and developing ideas. Hupfeld and colleagues (2019) found that adults with more ADHD symptoms reported hyperfocus more often.
Both rely on self-report. Neither is evidence of better business performance, and we do not claim it is. Our claim is about how this team works. Thinkers sit at the front of the process, where going wide is useful. Builders sit where depth is useful. The clock belongs to someone whose whole job is the clock.
That is why the Chief Time Wrangler role exists. Going wide has a cost: ideas multiply and rabbit holes appear. The Chief Time Wrangler pulls us out before they become side quests. Every project is time-tracked from day one, so you always know where things stand. Our team page calls the role the steering wheel for the chaos engine. We have not found a more accurate description.
You can meet the team. If this sounds like how you work, you can join the studio. You do not need ADHD to apply, and we never ask anyone for a diagnosis or medical information.
Why do AI projects stall, and which step addresses each cause?
Across the industry, the reported causes are ordinary ones: unclear value, unready data, poor fit with daily work, rising costs and late risk controls. The studies below measured different things, so read each figure on its own.
Gartner (prediction, July 2024): at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025, as reported by Campus Technology. The reasons named were poor data quality, inadequate risk controls, escalating costs and unclear business value. This was a prediction, not a measurement.
S&P Global Market Intelligence (2025): the share of companies abandoning the majority of their AI initiatives rose to 42% from 17% a year earlier, as reported by CIO Dive. The survey covered more than 1,000 respondents in North America and Europe. Obstacles cited were cost, data privacy and security risks.
MIT Project NANDA (2025): for task-specific generative AI tools, 60% of organisations evaluated them, 20% reached a pilot and 5% reached production. The report draws on 52 interviews and calls its own figures directionally accurate. It points to brittle workflows and misalignment with day-to-day operations.
IBM 2025 CEO Study: 50% of the 2,000 CEOs surveyed said rapid investment had left them with disconnected, piecemeal technology.
Deloitte State of AI in the Enterprise (2026): 25% of the 3,235 leaders surveyed had moved 40% or more of their AI pilots into production.
Cause reported | Reported by | Step that addresses it |
|---|---|---|
Unclear business value | Gartner (prediction, 2024) | Step 1. We start with the business case. |
Poor data quality | Gartner (prediction, 2024) | Step 2. The idea is tested against your data before the build. |
Poor fit with day-to-day work | MIT Project NANDA (2025) | Steps 2 and 3. Designed around your team, demoed every week. |
Escalating cost | Gartner (prediction, 2024), S&P Global (2025) | Steps 2 and 3. Costs in writing, and time tracked from day one. |
Privacy, security and risk controls | S&P Global (2025), Gartner (prediction, 2024) | Step 2. Security and privacy are designed in from day one. |
Disconnected, piecemeal technology | IBM (2025) | Step 2. Architecture designed around your existing systems. |
Pilots that stop before production | Deloitte (2026), MIT Project NANDA (2025) | Step 4. Launched into production, not left as a demo. |
One more finding, caveat attached. MIT Project NANDA reported that external partnerships reached deployment about 67% of the time, against about 33% for internally built tools. That is a correlation, and organisations that choose partners may differ in other ways.
A method does not make a project immune. It puts the known causes in front of you early, when they are cheap to fix.
If we got something wrong about your research or your studio, tell us at hello@hyperfocus.studio. We will fix it fast and owe you a coffee.
What does the method look like week by week? A hypothetical example
This is a hypothetical example. The business is invented, and we have left results out because invented numbers help nobody. It shows the sequence for a Plan followed by a production AI solution.
The problem, for example: a wholesale distributor whose support team answers the same 200 questions every single day. On the Chaos Meter, the team picks level 2, Curious.
When | Step | What happens |
|---|---|---|
Day 1 | Drop your problem | The operations manager sends a two-minute voice note and books a Clarity Session from the confirmation email. A human replies the same day. |
Day 2 | Clarity Session | A 15-minute call and six questions. |
Day 3 | 01 The Idea Drop | The recommendation: start with the Plan, then build an assistant grounded in the company’s own documentation, with clean handoff to a human. |
Weeks 1 to 3 | 02 Validate and architect | The Plan: the idea is tested against real support questions, then costed. |
Weeks 4 to 11 | 03 Short, visible sprints | A live demo every week. The support team tries each version. |
Weeks 12 to 13 | 04 Launch and scale | Production launch, documentation and handover. |
Here the Plan takes 3 weeks and the build takes 10, both inside the typical ranges above. At the end the client holds the Plan, a system in production, the documentation and the code.
Your project will differ. The sequence stays the same.
What to do next
If you have a problem nobody has cracked, drop your problem by text or voice note. A human replies within 24 hours and a recommendation lands within 72. If the honest answer is that you do not need us, we will say so.
Sources
Campus Technology (reporting Gartner), Gartner predicts wave of abandoned AI projects (6 Aug 2024)
CIO Dive (reporting S&P Global Market Intelligence), Voice of the Enterprise: AI and Machine Learning, Use Cases 2025 (14 Mar 2025)
MIT Project NANDA, The GenAI Divide: State of AI in Business 2025 (Jul 2025)
IBM Institute for Business Value, 2025 CEO Study (6 May 2025)
Deloitte AI Institute, State of AI in the Enterprise, 2026 edition (21 Jan 2026)
Personality and Individual Differences (White and Shah), Creative style and achievement in adults with ADHD (2011)
ADHD Attention Deficit and Hyperactivity Disorders (Hupfeld, Abagis and Shah), Living ‘in the zone’: hyperfocus in adult ADHD (2019)