The person who finds the money your operation is losing
Every operation runs on manual work that costs more than anyone has measured.
A senior
AI Orchestrator finds that work, puts a number on it, and builds the AI automation that
takes it off your team's plate.
One person. Your problem. Start to finish.
The same person runs the diagnostic, designs the solution, builds it, deploys it, and trains your team. The engagement ends when the system is working.
Who they are
They learn how work actually moves through your operation, agree the scope with you, then own the build end to end.
What they're not
Not a consultant who hands you a report and leaves. Not a contractor you manage. Not a vendor selling a platform. Everything runs in your accounts, on your systems. You own all of it.
How they work
AI handles the engineering: code gen, pipelines, integration, testing. The AI Orchestrator handles the judgment: scoping the problem, designing the system, and deciding what to automate and what to leave alone. That split is why one person now ships what used to take a full team.
Who backs them
Every AI Orchestrator is a senior member of Creative Chaos, shipping enterprise production software since 2000. Behind them sits a ~150-person bench for specialized engineering, design, and infrastructure.
Everything runs inside your infrastructure. Not ours.
The AI Orchestrator works in your accounts, your repositories, your security policy. Nothing is extracted into our systems. NDAs and MSAs sign before the diagnostic.
Why we charge this way
The fee is a share of the first-year value we identify together during the diagnostic, scaled to the scope and complexity of the work.
The fee is proportional to what we find together
Neither of us sets the number. It's calculated jointly from your figures during the diagnostic, and it's set before you sign. A share of value, not a flat fee, keeps our incentive on finding the real problem, not padding hours. You keep the majority of the upside, and all of it from year two onward.
Fixed scope, fixed timeline, agreed before we start
What the system has to do, by when, with acceptance criteria, sits in the contract before work begins. If the build runs long, that's on us, not on your invoice.
One person owns it, end to end
No sales-to-delivery handoff. The person who ran your diagnostic ships the build. One name on it, still in your Slack.
You can stop anytime, no lock-in
Thirty days' notice on maintenance. That's the whole commitment. We hold no keys to your code, servers, or workflows. Stop paying us and the system keeps running.
How the work gets done
One model, two shapes. Which one you get depends on what the work needs, not where your team sits.
Remote-led
Your AI Orchestrator embeds into your business remotely, finds the costly manual work, builds the fix inside your systems, and deploys it.
Discovery, development, testing, and rollout all happen remotely. Most engagements run this way.
The engagement fee covers all of it from start to finish, with no additional costs.
Ground-led
Some work can't be done from a distance. When an engagement needs presence, we deploy an AI Engagement Lead: a senior operator who gets into the business, finds where the money is leaking, earns leadership's trust, and works through your AI Orchestrator and build team to make the change stick.
The engagement fee covers the remote build. The on-site time is a daily rate plus travel, scoped and agreed before work begins.
Ground presence earns its place when:
- Adoption is the hard part: tech you bought before didn't stick.
- The work lives in physical space: factory floors, field service, warehouses, job sites.
- Access needs a body onsite: legacy systems, on-prem security, no remote path.
- The change crosses org lines: getting several GMs onto one system.
Prefer on-site regardless? Available by arrangement on any engagement.
We don't claim to only win if you do. A careful CFO knows this.
Here is what is true. The fee is proportional to the value we find together, and we don't collect the second half until you have reviewed the finished build and accepted that it works: 50% on signature, 50% on acceptance.
Capturing the full value depends on your team adopting the system, and that part sits with you. We would rather build it, hand it over, and leave the upside with you.
Seven rules. Every engagement. Every time.
Published so your CFO can check our math before you get on a call.
First-order only
Real hours, real costs, real revenue. Not "happier employees," not "better culture," not productivity multipliers from a slide deck. If we can't point at it in your P&L or timesheets, it doesn't go in the number.
In-year only
Year one only, starting the day the system goes live. Year two onward is yours. We don't bill on it. We don't take credit for it.
Loaded rates
When pricing saved time, we use the real cost of that person: salary plus benefits plus overhead, usually 1.3 to 1.5x base. If you'd rather not share, we use industry benchmarks and name them.
Revenue enablement requires probability
When the value is revenue, we don't assume you capture all of it. We agree on a realistic percentage, usually 50 to 70. Nobody claims 100.
Conservative bias
When two numbers both hold up, we take the lower one. Every time. Better to undercount and surprise you than overcount and have you wonder if we padded the estimate.
No double-counting
If one fix saves time and brings in revenue, we count the bigger number. Not both. Adding them inflates the value, and a CFO would catch it immediately.
Co-signed estimate
A good-faith number we both put our names on. Value from there depends on how your team uses it.
What you actually pay
Publishing a single number won't be honest. Here are three real engagements that show how the same fee model scales with what we build.
Reclaimed capacity
An accounting firm whose team spent around eleven hours a week rebuilding the same reports by hand, with a few formula errors still to chase each time. We built the system that produces them automatically. The team got those eleven hours a week back, the reports now land clean before the Monday stand-up, and nobody reconciles them by hand anymore. Using the firm's own loaded labor cost, reclaimed time came to ~$40,000 in year one.
Recovered revenue
An insurance agency losing policies to renewals nobody followed up on. Outreach depended on someone remembering, and in practice close to half the book slipped through. We built the system that flags every renewal 45 days out, drafts the outreach, and assigns it to a name. We reviewed the prior year with their sales leader and agreed on a conservative recoverable figure of ~$120,000 in year one, counting only policies with a realistic save.
A multi-workflow program
A manufacturer with three manual processes breaking down across the business: order intake, production scheduling, and warranty claims. We addressed all three as one program, sequenced so each went live before the next began. Order intake dropped from two days to same-day, the schedule stopped colliding with itself, and warranty claims that used to pass through four hands now route themselves. Combined first-year value came to ~$550,000, built and billed against milestones as each workflow cleared acceptance.
Bring your figures to the diagnostic. You'll know the fee before you sign.
The asymmetry already favors you
You keep the majority of the first-year value and all of it from year two forward. The rules undercount by design: first-order value only, the lower figure whenever there is a choice, a discount on freed time you would not actually redeploy. The diagnostic puts all of it in front of you before you commit to anything.
The alternative is to tie the fee to your realized savings. That would require us to stay embedded, measure your results, and define what counts as realized, a consulting engagement that never ends. This model is built so you keep the system and we go away.
Know whether it's worth building. Before you build it.
Not a sales call. Not a discovery phase. A structured working session that ends in a quantified problem or an honest "not right now."
Map the business
How revenue is made, where teams sit, and where your time, money, and effort keep disappearing.
Find the leaks
Area by area: manual work, dropped balls, reports that eat a full day. Specific situations with specific costs.
Put numbers on them
Frequency, time, who does it, what it costs when it breaks. Your figures, every number pressure-tested on the spot.
Decide together
One problem. One year-one value. One clear scope. From that value we set the build investment in front of you: a share of it, scaled to the scope and complexity of the work, calculated the same way for every client. You see the value, and you see exactly how the fee comes off it, before anything is signed. If the math works, we move forward. If not, we tell you why.
Common questions
What exactly is an AI Orchestrator?
An AI Orchestrator combines four disciplines in one: business analyst, systems architect, software engineer, and DevOps. They identify the manual workflows costing you the most, build the system, deploy it, and keep it running. One person accountable from diagnosis through deployment.
How does the fee work?
Our fee is a share of the year-one value quantified during the diagnostic, scaled to the scope and complexity of the build. We calculate it the same way for every client, from your figures, and you see the number before work begins. No hourly billing, no day rates, no retainers. The outcome and the acceptance criteria are defined before the engagement starts.
We've already bought AI tools. Why hasn't anything changed?
Because tools don't implement themselves. A ChatGPT subscription doesn't redesign workflows or remove manual work. Most businesses don't need more AI tools. They need someone who can identify the problem, build the solution, and make it work inside the operation.
Do we own everything that gets built?
Yes. Everything runs in your infrastructure, accounts, and repositories from day one. When the engagement ends, the system continues running without dependency on Creative Chaos. Ongoing maintenance is available but optional.
How do you decide whether to put someone on-site?
Some engagements run entirely remotely; others need someone on the ground. We recommend the right model during the diagnostic based on what the work requires. On-site support makes sense when the workflow is physical, change must be driven on the floor, or access constraints require someone in the building. In those cases, we deploy an AI Engagement Lead: a senior operator who works with your AI Orchestrator and build team to drive adoption. On-site time is billed at an agreed daily rate, separate from the engagement fee. If you need a full-time embedded technical engineer instead, that's our FDE model. See Forward Deployed Engineers.
What is a diagnostic?
The diagnostic is a two-to-three-hour session where an AI Orchestrator identifies a high-cost manual workflow in your operations and estimates the value of improving it. You'll leave with a recommendation, an estimate of year-one value, and a clear answer on whether a build is worth pursuing.
You already know which parts of the business cost too much.
The diagnostic puts a number on it. In two to three hours with an AI Orchestrator, you'll identify what's worth fixing and leave with a clear path forward, or the confidence that it's not the right time.
