AI challenge

Can AI perform this task reliably enough?

"Can AI do this?" is often the wrong question. A better one: how often does it go right, what mistakes does it make and what happens when such a mistake occurs? That tells you whether an AI solution is usable in practice.

Book a call View matching offer

Does this sound familiar?

This mostly comes up when an AI demo is convincing, but the task is important enough that occasional errors have consequences.

Quality varies per example
Some cases go excellently and others surprisingly badly.
Not all errors are equally serious
A wrong phrasing is different from a wrong decision or amount.
Manual checking may still be needed
You want to know how much human review is still needed to run the process responsibly.
You can't compare objectively yet
The assessment is mostly based on a few separate tests or demos.

Where do you want to be?

You want to set a realistic quality bar and know whether the technology can meet it with the right technical measures.

A concrete definition of good enough

We make explicit which errors are acceptable and which are not.

Insight into remaining risks

You know where AI remains uncertain and what the practical impact of that is.

A fitting control layer

We determine where validation, fallback or human review is needed.

Why does this happen?

AI is probabilistic, so you can't rule out every failure scenario. You can understand, measure and contain errors.

See how we approach this

Average quality says too little

A high score can still hide unacceptable errors.

Test examples are often too friendly

Real users and data contain many more exceptions.

System design affects reliability

Validation, retrieval and human-in-the-loop can matter more than yet another prompt.

Matching offer

AI Reality Check

In an AI Reality Check we first define what reliable enough means for this specific task and which risks need to be managed technically. If practical measurements are needed, an AI Prototype Sprint can then actually test the most important scenarios.

View this offer

€1,950 excl. VAT

Fixed price · one process or up to three AI ideas

Result: A reasoned technical direction and build / buy / don't build advice
Duration: About two weeks
Next step: Build, validate with a prototype first, or deliberately stop

Why 9to5 for this?

For us reliability is an engineering question, not a feeling after a good demo.

View cases

Errors become part of the design

We look explicitly at what can go wrong and what impact that has.

Human-in-the-loop where it adds value

A small human check can sometimes be much wiser than pursuing full autonomy.

Criteria first, then technology

Only when "good enough" is clear can you judge whether a solution is feasible.

Can AI perform this task reliably enough?

Does this sound familiar?

Quality varies per example

Not all errors are equally serious

Manual checking may still be needed

You can't compare objectively yet