Because the preparation is done properly, we can stand behind the result. Pick one workflow. In thirty days we deliver a working agent doing it — and if it doesn't do what we agreed, you owe nothing for the build.
One workflow. One governed agent. Real systems. Real work. Measured result. Yours to keep.
Most AI vendors ask you to pay first and hope it works. We think that's backwards. You should see the thing working on your own business before a single dollar changes hands for the build.
The reason we can make that offer is not confidence for its own sake — it's dandori. The risk in an AI project lives almost entirely in the preparation: the unclear process, the undefined rules, the systems that don't connect the way everyone assumed. We do that preparation up front and in the open, so by the time we're building, the outcome is already largely decided. A vendor who skips preparation can't offer a guarantee like this, because they genuinely don't know if it'll work. We do the part that removes the doubt — so we can stand behind the result.
Before we start, we write down — in plain language — exactly what the agent must do and the boundaries it must respect. That written agreement is the whole deal. No moving goalposts on either side.
At the end of thirty days you decide: keep it running and pay, or walk away owing nothing for the build. Either way, what we prepared is yours — the setup, the rules, the learning. That's not a sales trick. It's how a craftsman stands behind their work.
Four weeks, four clear steps — the same craft as our method, on a fixed clock. You always know what week you're in and what comes out of it.
We choose the one workflow, define what success looks like, identify the data and systems it touches, and set the boundaries — what the agent decides, and what it must bring to you.
We map the workflow in full: the context, the approvals, the rules, the connections, and the test scenarios. This is the eight-tenths — the preparation that makes the build fast.
We connect the agent to real inputs and produce working outputs on a controlled path — reviewed with you as it takes shape, never behind a curtain.
We test accuracy, consistency, escalation, and the audit trail on your real work — until you can see it does the job your way, and decide whether to keep it.
A proof works when both sides know their part. Here's exactly what each of us brings to the thirty days — no surprises.
A guarantee is only fair if both sides know what it means. So we define it up front, in writing, before any money or work is committed. Your engagement is covered by a short written agreement, provided before the build begins.
In plain terms: if the proof does not meet the written success criteria, you owe no build fee. You keep the prepared workflow documentation, rules, success criteria, and integration design. Production operation of the agent begins only if you choose to continue.
What that draws a clean line around: the discovery artifacts, workflow map, rules, success criteria, and agent configuration are prepared and yours to keep regardless of outcome. The deployed working agent is demonstrated against those criteria on your real work. Ongoing production operation is a separate, opt-in step — it starts only when you decide to proceed, never automatically.
You shouldn't have to take "it works" on faith. Every 30-Day Dandori Proof hands over the same six artifacts — concrete, documented, and yours to keep. Here's the full set, with detailed samples below.
Your real process, documented before anything is automated.
What the agent decides, what it escalates, what it must never do.
The human-in-the-loop control path — who signs off, and when.
How the agent connects to your systems of record, cleanly.
Every action recorded — defensible, reviewable, reversible.
The agent, rules, and everything it learned — portable, yours.
Illustrative samples of three of these follow, so you know exactly what to expect before you begin.
Your real process, documented before anything is automated. Example — a purchase-order intake & production-scheduling workflow, integrated with SAP:
Supply chain & forecast, connected: the agent reads live inventory and open purchase orders from SAP for supply-chain visibility, checks demand against the SAP sales forecast, and writes the confirmed schedule back — so the plan reflects what's really on hand and what's really coming.
| Situation | Agent may… | Authority |
|---|---|---|
| Standard PO, SAP shows stock on hand, normal lead time | Acknowledge & schedule on its own | Automatic |
| Order value $10,000–$50,000 | Draft the schedule, wait for planner approval | Review |
| SAP MRP shows a material shortage or supplier delay | Flag the gap, propose options, hold | Human only |
| Demand runs ahead of the SAP sales forecast | Surface the variance, recommend a reorder | Review |
| Repeat order, SAP confirms same specs & stock | Reuse the prior build, schedule directly | Automatic |
Every action logged, traced, and reversible — you can always see what happened and why:
| Time | Action | Detail |
|---|---|---|
| 07:42 | Read SAP · scheduled | PO-2214 · 500 units · SAP MM stock ok · ship 3 days · auto |
| 08:03 | Held for review | PO-2216 · $28,400 · schedule drafted · planner review |
| 08:03 | Repeat order | PO-2217 · SAP confirms specs = PO-1980 · scheduled |
| 09:20 | Forecast variance | Part 88-A · demand 18% over SAP IBP forecast · reorder recommended |
| 10:15 | Escalated | PO-2219 · SAP MRP: steel shortage · options proposed · held |
| 13:48 | Wrote SAP · slotted | PO-2216 · released by planner · schedule posted to SAP · floor notified |
At the end of thirty days, this is the package that belongs to you — documented and portable, no lock-in:
These are illustrative — yours would be shaped around your own workflow. But the form is exactly this: concrete, documented, and handed to you. Nothing hidden, nothing you can't take with you.
The first agent builds the foundation — and the workflow map, rules, context layer, integrations, and approval model don't get thrown away. They become reusable company infrastructure. The next agent inherits all of it. Most AI spending gives diminishing returns; prepared work does the opposite. It compounds.
The first thirty days are the hard ones, because that's where the foundation gets laid — how your systems connect, how your rules are expressed, how your operation actually works, all written down and understood. That preparation doesn't get thrown away when the first agent ships. It carries.
So when you're ready for a second agent — materials reordering after PO intake, say, or shipping after scheduling — much of the groundwork is already done. The systems are connected. The rules are known. The trust is built. The second proof moves faster and costs less than the first, and the third faster still. You're not starting over each time; you're building on prepared ground.
That's the quiet advantage of doing preparation properly: it's the one investment in AI that's worth more the more you use it. One workflow proves the method. The method is what keeps paying.
Every agent you add inherits what the last one prepared — the connections, the rules, the understanding of your operation. So the work gets faster and the foundation gets stronger with each one.
| Order | Example agent | Inherits from the ones before | Time to deliver |
|---|---|---|---|
| 一 | PO intake & scheduling | Nothing yet — this is where the foundation is laid: systems connected, rules written down, your operation understood. | ~30 days |
| 二 | Materials & reorder | The SAP connection, the inventory records, the lead-time rules — already prepared during PO intake. | Faster |
| 三 | Shipping & delivery | The order data, the customer links plus the escalation rules — reused, not rebuilt. | Faster still |
| 四 | Quote & RFQ replies | Your pricing, your specs, your whole prepared context layer — shared across every agent. | Days, not weeks |
| 五 | Quality & compliance logs | Nearly everything. The groundwork is deep now; a new agent mostly needs its own specific task defined. | Fastest yet |
Illustrative order — yours would follow your own priorities. The pattern holds regardless: each agent inherits the prepared ground beneath it, so knowledge accumulates instead of resetting.
Beyond reuse, each agent improves from doing the work. Corrections and outcomes feed back into the prepared context — so the foundation every future agent inherits keeps getting better.
The agent does real work in your operation, day after day.
Every action and correction is logged — what worked, what you fixed.
Those corrections sharpen the rules and enrich the shared context.
The better foundation lifts this agent — and every future one.
Most AI spending gives you diminishing returns — each tool a fresh start. Prepared, connected work does the reverse: it's worth more the more you use it, because the foundation never resets. And because everything stays yours and portable, that growing advantage is never something you're locked into — it's something you own.
It starts with a workshop — no obligation. We'll help you choose the task most worth proving first, and write down exactly what success looks like.
Book a workshop →