You don't give a frac how it's built. But you do care that it keeps delivering results.
We find the AI work worth doing, apply serious engineering when you need serious outcomes, and make sure it holds up long after go-live.
Fail fast. A diagnostic, then an honest go or no-go you can walk away from.
A capped budget. Agents that share one brain.
Monthly. We keep it delivering as everything underneath it changes.
Which problem is yours?
The work your team does every day.
You're on that pageAI in the product your customers pay for.
Go there insteadNeither? If you're a fractional CxO or AI engineer, join the waitlist .
You're worried you're missing out on AI. That worry is someone else's business model.
Two camps, both extremely loud. Your feed is wall to wall tokenmaxxers and skill files traded for a follow, with a steady drip of failed-project post-mortems in between. Meanwhile the chat you had with GPT this morning hallucinated three times before it gave you what you wanted.
Both camps ran the same experiment. Thin prompts, whatever data was lying around, one go at it. One got garbage back and called it a bubble. The other got something demoable and called it the end of work. Same method, opposite verdicts. Both projected.
Some of them have an income riding on which verdict you believe. The rest are reporting their own skill and calling it the technology. Neither camp has looked at your business.
Three stages, and a real exit halfway through the first one.
Every price is on this page, including the one we make nothing on.
The audit
Fail fast, deliberately.
The diagnostic
A structured look across your goals, your people, your data and your systems. Days rather than weeks.
Stopping here is a normal Tuesday.
We stop and sit down with you, and you get an honest go or no-go. Sometimes the right answer is using an existing product or a simple skill file. You keep everything we found, we shake hands, and you have spent a small amount to avoid spending a large one.
Plenty of firms will tell you they would walk away from bad work. We price the diagnostic at cost and lock this meeting in before anything starts, so we can.
The deep dive
Only the work that cleared the bar, taken to implementation detail. Some of it is small enough to just do; the rest needs designing properly. You pay for each piece on its own terms.
Quick wins
Small pieces - skills, MCP connections, micro-automations - that we implement, verify and document for your team.
The truly simple stuff? Instructions are in your diagnostic for your team to run with.
Solution design
Scoped from your diagnostic: how many agents, and whether they warrant a cohesive brain - a shared context layer your agents all draw on. Starts with a $2,000 feasibility stage; if the direction isn't viable, that's all you pay. The design is yours either way.
The build
For larger organisations a first build won't get you all the way there, and it isn't meant to. It puts your first agents into production, starts building out the brain, and proves the approach on something real. Every stage after that is priced from what the last one proved, and costs less as the foundations grow.
A ceiling you can plan against and scope that can flex inside it, so we change direction when we learn something instead of raising a change request. Ships with the tests, the decision log and the written instructions for whoever runs it.
Hypercare
Our experience building AI platforms has taught us that you don't truly know a system until it hits production. So every build reserves a capped hypercare budget on top of the build price, already approved and there if tuning or rethinking is needed.
Got it right on day one? You don't spend it.
Typically up to 20% of the build, your figure comes with the proposal
30 days from go-live, longer by agreement on a harder build
And it's yours: built to run in your environment, encoded from your business, not rented from ours.
Keeping it sharp
We watch for drift, maintain your tests as your rules change, ship patches, and migrate you as model providers move. Each month you get a report of what changed and what we caught.
All prices AUD, ex GST.
What we look at
A lot of AI reviews start with the technology. We start with what your business is actually trying to do, then work out where AI helps, what it puts at risk, and whether what you build will still be earning its keep in two years.
Your goals
We aren't here to blindly introduce AI for the sake of AI. We need to understand the overall strategic goals of the business, and how you see AI fitting into them.
Your people and process
We interview senior leadership, but we spend just as long with the people at the coal face.
That covers the ROI case, and how the work actually gets done as opposed to how it is documented. It also covers the human side: where your people see the opportunity, what frustrates them, and where AI would actually make their jobs better.
Your systems and data
We don't just think about code and databases. Businesses are a system of systems, and we look at the whole picture to understand how they interact and where the bottlenecks are.
The same goes for your data: what exists, who owns it, and whether the people relying on it trust it. Most of that comes from conversation, not access. If you are comfortable giving us read access we go a level deeper into the data and the code, but that is your call, and the diagnostic will still stand without it.
01Your goals
We aren't here to blindly introduce AI for the sake of AI. We need to understand the overall strategic goals of the business, and how you see AI fitting into them.
02Your people and process
We interview senior leadership, but we spend just as long with the people at the coal face.
That covers the ROI case, and how the work actually gets done as opposed to how it is documented. It also covers the human side: where your people see the opportunity, what frustrates them, and where AI would actually make their jobs better.
03Your systems and data
We don't just think about code and databases. Businesses are a system of systems, and we look at the whole picture to understand how they interact and where the bottlenecks are.
The same goes for your data: what exists, who owns it, and whether the people relying on it trust it. Most of that comes from conversation, not access. If you are comfortable giving us read access we go a level deeper into the data and the code, but that is your call, and the diagnostic will still stand without it.
What you get
A report of findings, clear and to the point, covering:
An overview of where your organisation is today, and where it could be tomorrow.
Every opportunity we found, scored by impact, effort and risk.
Our recommendation, and the reasoning behind it. Including when that recommendation is don't, and what would need to change for it to become a yes.
What change management looks like if you decide to move forward.
What your overall AI strategy should be, and how to get there. For some people it's a few agents, for others it's an entire cohesive brain.
Results on launch day. And every day after it.
A production system has to work every day, on the inputs that never made it into the spec, while the model and context underneath it keep changing.
Independent agents are hires who never talk to each other.
Real leverage is a cohesive brain: your data, your rules, one context every agent draws on.
That means the data work is the build work. We do the cleanup, the pipelines and the integration, and we sit down with your stakeholders to understand how your data is actually used, so it is modelled for context and retrieval accuracy. Bad data in, bad data out.
It has to be yours, not a template.
A whole category of firm is building roughly the same agent for everyone and calling it bespoke. Nothing wrong with that if it gets you the outcome you are after.
We take the harder problems, and often they are the ones that set you apart from your competition: the way you operate in your market, or the onboarding experience your customers get.
What does good look like? Constantly evaluated.
AI solutions are not deterministic, so we agree a set of test cases and acceptable boundaries with your team and evaluate them continually.
Good has to hold as models drift and your data changes.
A record of why, not just what.
Every decision that matters gets logged with its reasoning. Why this model, why this threshold, why we rejected the obvious approach.
When someone new picks it up, whether that is your team or ours, they inherit the thinking and not just the code.
01Independent agents are hires who never talk to each other.
Real leverage is a cohesive brain: your data, your rules, one context every agent draws on.
That means the data work is the build work. We do the cleanup, the pipelines and the integration, and we sit down with your stakeholders to understand how your data is actually used, so it is modelled for context and retrieval accuracy. Bad data in, bad data out.
02It has to be yours, not a template.
A whole category of firm is building roughly the same agent for everyone and calling it bespoke. Nothing wrong with that if it gets you the outcome you are after.
We take the harder problems, and often they are the ones that set you apart from your competition: the way you operate in your market, or the onboarding experience your customers get.
03What does good look like? Constantly evaluated.
AI solutions are not deterministic, so we agree a set of test cases and acceptable boundaries with your team and evaluate them continually.
Good has to hold as models drift and your data changes.
04A record of why, not just what.
Every decision that matters gets logged with its reasoning. Why this model, why this threshold, why we rejected the obvious approach.
When someone new picks it up, whether that is your team or ours, they inherit the thinking and not just the code.
What we will not do
We are not vibe coders, and we do not sell vibe-coded solutions.
No multi-year transformation programmes. No RFPs. No open-ended bums on seats.
Workshops that make everyone feel good, but don't actually deliver meaningful change.
frac this:
frac legacy.
The legacy problem in this market is not your old system. It is the firms selling to you. Same delivery model they have run for fifteen years, same process, same shape of team, now with AI in the deck. They will charge you handsomely to transform while transforming nothing about themselves. If a firm has not changed how it works, be careful about what it can teach you about changing how you work.
And no, we will not tell you to replace something because it is old. If a system has been quietly doing its job for twelve years, it has earned some respect.
frac one size fits all.
The market makes you pick one. Firms with real engineering depth turn up with a single playbook and learn your industry at your expense. Firms that have deep industry knowledge will generally provide digital solutions that are flaky and clunky. You are paying for one and you need both.
At Frac Consulting, we not only have a network of seriously impressive software and data engineers - where a build needs domain depth we do not have, we bring in an AI-pilled fractional executive who has actually run that function, working alongside the engineer. CXOs who understand your industry, your problem, and your domain, able to get up to speed and start adding value immediately.
frac big projects that turn into black holes.
You know how this one goes, because you have watched it. The pitch was $500,000. Three years later it was $4.5 million, the partners who pitched never came back, the contractors who did the work left nothing written down, and you were arguing with an account manager about a change request for a system that no longer fit your business.
We scope to the shortest piece of work worth having on its own, we agree a ceiling before it starts, and then we look at the next one. Nobody should be waiting a year to find out whether this was a good idea.
frac headcount as the business case.
AI is the biggest shift in knowledge work since the internet. Some roles will change beyond recognition and some will not survive it. But a firm that treats that as the point, that arrives with a headcount number as the business case, is bringing legacy thinking to it.
AI is not just an efficiency play. It makes what was once unreasonable not only possible, but in many cases outright silly not to do. The savings are the part you can put in a spreadsheet, so they get the attention. Human judgement, taste and instinct do not, and without them you will just do the wrong things faster. If a redundancy headline is the outcome you are after, we are the wrong firm.
Who we are
“frac exists to be the firm I could never hire.”
Frac Consulting was built to be the opposite of the firm that sells you AI it does not use itself. Twenty years of senior technology leadership, two of them building AI systems in production, and a network of senior engineers brought in by name when a build needs them.
Senior technology leadership. Readify, MatchBox Exchange, Brandcrush.
We built and run our own AI platform, Talent Hustler, and carry its uptime and its bill.
Questions people actually ask
Do we need a technical team?
No. Some clients have engineers, some have an IT provider, some have neither. Whatever we build is handed over properly, and whoever looks after your systems today can look after this too. We will brief them ourselves if that helps.
Who owns what you build?
You do. It's yours: built to run in your environment, encoded from your business, not rented from ours. Your business logic, your data, your integrations, all of it transfers on payment rather than on continued payment. Anything of ours that ends up inside your system is licensed to you permanently at no cost, so you are never renting your own software back from us.
What if our requirements change halfway through?
They will. That is why builds run to a capped budget rather than a fixed price. You get a ceiling to plan against, and inside it direction can change as we learn. Nobody has to negotiate to do the obvious thing.
We tried something already and it stalled. Is that a problem?
Not a problem. A stalled pilot usually tells us more about where the real blockers are than a blank page would, and your team has potentially already learnt something expensive. Bring it.
How long does this take?
The audit's first stage is days rather than weeks. Builds are scoped to the shortest piece that stands on its own. We prefer weeks over quarters.
Hard problems do require hard engineering, though, so we can't always deliver in a few weeks. Our goal is to get the time to value as close to zero as possible.
Start with the audit.
Or just ask a question about it.
Bring what you are working on and we will tell you plainly whether the audit is your right next step, or whether it isn't.
Thirty minutes with Stephen. Free, and no obligation.