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A research document starts the work. The original stays available throughout.
Bring us an operational problem.
We map how it works today, identify where it breaks down and determine what system could improve it.
The assessment is for operations that already run, and already cost more than they should. It is not for ideas without an operation behind them.
The same steps, by the same people, every day or week.
Copy, paste, re-key, export, attach, forward.
A spreadsheet that one person understands is holding the process together.
You find out something went wrong when a customer tells you.
What happens on site and what the system says are two different stories.
You suspect AI or automation could help, but not where, or whether it is worth it.
Every lens produces evidence, not opinion: who does what, which system holds which record, where a decision is made and by whom, where work waits. The bottleneck is usually visible only when all ten are laid side by side.
Roles, workload, who actually does the step.
Applications in use, and the ones worked around.
Where each record lives, its quality, who owns it.
Sensors, equipment, cameras and what they could report.
Which are rules, which are judgement, who makes them.
Where work changes hands, and how long it waits there.
What goes wrong, how often, and where it goes.
Which steps need sign-off, by whom, and why.
Who may see, change and act on what.
What is measured today, and what should be.
Everything is written so it is useful whether or not you continue with us. The system map and requirements are yours to hand to any engineering team.
| Nº | DELIVERABLE | ANSWERS | GROUP |
|---|---|---|---|
| 01 | Current-State System Map | How the operation actually runs today, across people and systems. | UNDERSTAND |
| 02 | Bottleneck Analysis | Where work waits, repeats or piles up, with evidence. | UNDERSTAND |
| 03 | Failure-Point Analysis | Where and how often things go wrong, and what it costs. | UNDERSTAND |
| 04 | Responsibility Classification | Which decisions are rules, which are judgement, which need a person. | UNDERSTAND |
| 05 | Data Requirements | Which records a system would need, where they are, how clean they are. | REQUIREMENTS |
| 06 | Integration Requirements | Which applications must connect, and how. | REQUIREMENTS |
| 07 | Permission Requirements | What any component may read, propose and do. | REQUIREMENTS |
| 08 | Edge Requirements | Whether physical sensing or on-site processing is needed, and where. | REQUIREMENTS |
| 09 | Opportunity Matrix | Every candidate improvement scored on value, feasibility and risk. | DECIDE |
| 10 | Recommended Pilot | The one system to build first, and why that one. | DECIDE |
| 11 | Success Metrics | The number the pilot must move, its baseline and how it is measured. | DECIDE |
| 12 | Indicative Architecture | The system drawn in the Stacx24 system language, with human nodes marked. | DECIDE |
| 13 | Risk Notes | What could make the pilot fail, and what we would do about it. | DECIDE |
| 14 | Executive Readout | One session, one document, a clear recommendation. Including "do not build". | DECIDE |
Each stage has a decision at the end of it, made by you. Most engagements move to a single pilot. Some stop at the readout, because the right answer was a process change, not a system.
We engineer the right combination.
A research document starts the work. The original stays available throughout.
Existing records, source details and the approved label list give the document context.
Software checks for duplicates and missing information before any AI suggestion.
AI proposes a label from the approved list. It cannot publish the label itself.
The researcher reads the supporting passage and approves or corrects the suggestion.
Only the approved label enters the record, alongside its source.
The system records the suggestion, the review and the saved result so the team can inspect them.
The team reviews corrections and agrees changes to the guidance before the next document.
A research document starts the work. The original stays available throughout.
Existing records, source details and the approved label list give the document context.
Software checks for duplicates and missing information before any AI suggestion.
AI proposes a label from the approved list. It cannot publish the label itself.
The researcher reads the supporting passage and approves or corrects the suggestion.
Only the approved label enters the record, alongside its source.
The system records the suggestion, the review and the saved result so the team can inspect them.
The team reviews corrections and agrees changes to the guidance before the next document.
An incoming request starts a handoff between the tools your team already uses.
The system brings together the request, existing records and its responsible owner.
Defined rules check the request and pass its details between connected applications.
This example needs no AI decision. Defined rules identify an exception and send it to its owner.
A named person supplies the missing detail and confirms that the work can continue.
The confirmed information reaches the next application and the work continues.
The system records completion and exceptions so the team can see what happened.
The team reviews repeated exceptions and approves rule changes for future requests.
An incoming request starts a handoff between the tools your team already uses.
The system brings together the request, existing records and its responsible owner.
Defined rules check the request and pass its details between connected applications.
This example needs no AI decision. Defined rules identify an exception and send it to its owner.
A named person supplies the missing detail and confirms that the work can continue.
The confirmed information reaches the next application and the work continues.
The system records completion and exceptions so the team can see what happened.
The team reviews repeated exceptions and approves rule changes for future requests.
A new field reading starts the cycle, connecting conditions on the ground to the system.
Field history and recent conditions help the team interpret the new reading.
Local edge checks validate readings before relevant information reaches the monitoring application.
The system uses the checked information to propose a response within its defined scope.
The agronomist checks the proposal and approves or adjusts it before it reaches the operator.
The approved proposal reaches the operator with the instructions they need.
New readings and operator reports help the team understand what happened next.
The agronomist reviews the observations and agrees guidance for the next cycle.
A new field reading starts the cycle, connecting conditions on the ground to the system.
Field history and recent conditions help the team interpret the new reading.
Local edge checks validate readings before relevant information reaches the monitoring application.
The system uses the checked information to propose a response within its defined scope.
The agronomist checks the proposal and approves or adjusts it before it reaches the operator.
The approved proposal reaches the operator with the instructions they need.
New readings and operator reports help the team understand what happened next.
The agronomist reviews the observations and agrees guidance for the next cycle.
Something happens in the operation.
Records, history and state are attached.
Rules, software and integrations do the deterministic work.
One bounded AI component, where judgement can be modelled.
A named person decides where it matters.
Something changes in the world.
Telemetry and monitoring record what happened.
The loop feeds back into the next event.
Describe the operation in your own words. An engineer, not a sales team, reads it and replies within two working days with whether an assessment fits, and if so, a scope and fixed fee.
We'll review the operation and reply within two working days with whether an assessment fits.
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