A plain-language workflow map
Where the work starts, where it slows down, and what a good end result looks like.
Real workflow discovery · Human review
Tell us what keeps wasting your time. We will work out what is worth changing.
Do not start with AI, tools, or a solution. Start with one task you keep repeating, the part that frustrates you, and what must remain under your control. We will turn that into a clear case for—or against—automation.
No AI knowledge neededOne question at a timeHuman-reviewedNo confidential data
Choose your perspective
- 01I run a business or teamFind where a team, service, or operation loses time and capacity.Describe the company case →
- 02I am describing my own workShareable with employees and professionals in any field—teacher, accountant, therapist, engineer, or something else.Describe my work →
- 03Personal everyday lifeExplore recurring admin, organisation, reminders, documents, or household obligations.Describe a personal task →
Not a sales quiz
We are collecting the work as it really happens—not forcing AI into it.
The questionnaire asks about the last real case, sequence of steps, frequency, exceptions, risks, and the relief you actually want. A checklist, a simpler integration, a rule-based automation, or no change at all may be a better answer than an AI system.
Open the questionnaire →What happens after you submit
A case first. A solution only if the evidence supports it.
- 01You describe the work
One real recurring task, in your own words and without technical jargon.
- 02We reconstruct the workflow
Trigger, inputs, steps, output, exceptions, time, and people who must review it.
- 03We identify the smallest useful change
What can be simplified, what should stay human, and what information is still missing.
- 04We decide whether to test it
If the case is strong, we agree a small pilot on fictional or redacted examples before any integration.
The initial assessment
What we aim to send back
Automation fit—not an AI pitch
What is repetitive enough to simplify, what needs professional judgment, and what should not be automated.
The smallest sensible experiment
A test that can show value without buying a platform or exposing real data.
Open questions and risks
Missing data, edge cases, permissions, privacy, and the human approval points that matter.
The sort of detail we are looking for
A profession is not a use case. A repeated moment inside the work is.
These are examples of how a vague problem becomes something testable—not claims about completed client projects.
Accountant
Incoming invoice to missing-information request
Not “AI for accounting,” but the repeated sequence of opening invoices, checking fields, comparing records, and requesting what is missing.
Teacher
Raw notes to a reviewed parent update
Not “AI in education,” but turning scattered, non-sensitive notes into a consistent draft that the teacher reviews before sending.
Everyday life
Documents and dates to an obligation checklist
Not a general life assistant, but a bounded way to track recurring renewals, bills, documents, and reminders without exposing account credentials.
The answer may be: do not automate this
Why this starts with questions
A useful system begins with the annoying reality, not a catalogue of AI features.
We need real cases quickly: from professionals we know, associations, and small teams. Those cases reveal recurring patterns, give us honest pilot candidates, and help us build domain-specific workflows people will actually use.
No invented case studiesNo sensitive documentsHuman judgment stays visibleValue measured before scale
CloudTalent's second practice
When the bottleneck is a person rather than a process.
We also run engineer-led technical recruiting for software, cloud, data, and AI roles. The two services are separate: the questionnaire does not turn into a recruiting lead unless you ask us to discuss hiring.
Send a hiring briefPrefer a conversation?
Bring one task you keep repeating.
A 30-minute discovery call is enough to reconstruct the first version of the case. If a simple non-AI fix is better, we will say so.