Demonstrator

AI Quality & Cost Control Panel

A demonstrator showing how AI can be embedded into a business workflow without giving it full control. The result can be reviewed, assessed and approved by a user, while each run stays connected to cost and decision history.

Output quality User approval Execution cost Decision history

See the process Describe a similar process

Controlled AI workflow Demonstrator active

Current stage Quality review

01Input dataDone
02ValidationDone
03Output and reviewIn progress
04ApprovalPending

One view connects input, AI output, quality, cost and user decision.

This material shows a workflow demonstrator. The description is generalized and does not include client data or production data.

01 / Challenge

Once AI starts doing real work, the output alone is not enough.

The hardest part is not invoking the model itself, but controlling what went into AI, what came out, who approved it and how much the execution cost.

01

It is hard to assess output quality

Without one shared review point, users cannot easily tell whether the result is ready for downstream use.

02

Approval history is incomplete

It is not always clear what was approved, rejected or routed further, and by whom.

03

Cost is scattered

You may see the vendor invoice, but not the cost context of a specific task and scenario.

04

AI should not end the process on its own

The model output should not automatically become a business decision without human control.

Key point: AI should be one element of a workflow whose output can be checked, approved and linked to cost as well as execution history.

02 / How the controlled AI workflow works

From input data to a decision, with control in between.

01Input dataThe system receives structured data for analysis.

02ValidationCompleteness and correctness are checked first.

03AI taskA defined scenario or instruction is executed.

04AI outputThe result returns with its execution context.

05Quality reviewThe output can be assessed before downstream use.

06User approvalA user can approve, reject or route the result further.

07Cost and decisionCost remains visible together with status and final decision.

Workflow principle: the AI output does not have to end the workflow automatically. It still goes through review, user decision and recorded cost context.

03 / What can be controlled

What can be controlled

01

Input data

The source, completeness and correctness of the data before AI is triggered.

02

AI task

The scenario, instruction and execution setup for a given analysis.

03

Output

The generated response, its structure and the context of a single run.

04

Quality

The output assessment, validation status and places that need extra review.

05

Approval

The human decision status, approval path and the outcome of further handling.

06

Cost and history

Execution cost, operation history and decision record in one view.

04 / What the user and manager get

The first thing you see is what needs a decision, not the technology itself.

The demonstrator is valuable not because it uses AI, but because the output remains part of a controlled operational workflow.

You know what data went into AI

Input data and scenario stay inside the workflow rather than disappearing elsewhere.

OK

You can review the output before using it

The result does not have to move forward without assessment and user decision.

OK

You can see the execution cost

The analysis cost is tied to a specific run, not only to a monthly vendor bill.

OK

Status and decision history remain visible

It becomes easier to reconstruct the workflow, exceptions and accountability.

OK

05 / What the demonstrator shows about Elistar competence