Know what to do with AI before you start building.
You see opportunities for AI, but first want to know what is wise. We examine one concrete process or up to three ideas and give you a well-founded technical direction. Even if the conclusion is that you are better off without AI.
When is this offer a fit?
When the main question is not yet how to build something, but what you should be building in the first place.
Which processes are suitable for AI or automation?
You see opportunities to make manual work smarter, but first want to know where AI actually adds value and where regular automation fits better.
View use caseCan we build an internal AI assistant with our own knowledge?
You want to know whether employees can reliably use AI to work with internal documents, data and knowledge that is now spread across different sources.
View use caseIs our AI idea technically feasible?
You have a concrete AI idea and want to know whether it can really be built with the available models, data, systems and constraints.
View use caseDo we need AI for our idea?
You have a problem or product idea and suspect AI could be a solution, but first want to know whether that is technically and practically the wisest route.
View use case
Afterwards you have...
A clear technical direction
You know which approach is promising and which options you are better off leaving alone.
The main uncertainties on the table
Integrations, data, reliability, risks and human oversight are made concrete enough to make a decision.
A logical next step
You know whether to build, buy, integrate, validate something first or stop.
What is included in this offer?
A bounded technical investigation with a concrete decision as the result.
What we do
A senior engineer examines the actual question and the context around it.
- Intake and sharpening the problem
- Examining one concrete process or up to three AI ideas
- Assessing feasibility, dependencies, integrations and risks
- Comparing build, buy, integrate or not building
- Determining where AI does and does not make sense
- Taking human oversight and key preconditions into account
What you get
Not a generic AI report, but a compact decision document that lets you move forward.
- Recommended technical approach
- What AI should and should not do
- Rough solution direction and relevant integrations
- Main risks and assumptions
- Build / buy / don't build advice
- Indication of size and the next step
- Review session with a senior engineer
Out of scope
The Reality Check is meant to make a decision, not to build the whole system already.
- Production-ready implementation
- Fully worked-out UX or product design
- Extensive organisation-wide AI strategy
- Unbounded research into multiple processes or business units
How it works.
- 01
Sharpen the question
We determine which decision you need to make and what a good result would be.
- 02
Understand the technical context
We look at processes, data, existing software, integrations and preconditions.
- 03
Test the options
We compare realistic routes on feasibility, risk and technical complexity.
- 04
Deliver decision + route
You get our conclusion, technical direction and a concrete next step.
Why should you believe this?
We don't advise from a fixed preference for AI or custom software. Because we build software ourselves, we can judge when building is wise and when it is not.
View casesEngineers look at the real system
We look at the AI idea and also at data, integrations, software and operations.
AI isn't always the answer
Plain automation, existing software or building nothing are real alternatives.
We can carry out the follow-up too
If building turns out to make sense, the advice doesn't have to go to another technical party first.