Follows discovery · Prove it small, then build it properly

Prove it small, then build it properly.

Discovery produces a list of friction. Not all of it deserves a big project. We start with the challenges that are low risk, low effort and high gain, and prove each one as a working prototype on your data, in days. Your leaders see AI doing real work before anyone commits to a full build.

Days per prototype Led by Gary Cheers On site or remotely
A working prototype being demonstrated on screen
Follow-on Prototypes
Who it is for
  • Leadership teams who want evidence before they commit budget to AI.
  • Businesses coming out of discovery with a list of friction and no clear place to start.
  • Owners who want working systems in their own accounts, not a retainer with no end.
What you receive
  • Two or three working prototypes chosen for low risk, low effort and high gain.
  • A demonstration to your leadership team with measured results from your own data.
  • A written scope, with a price, for anything worth building in full.
  • Production builds delivered in your own accounts, documented, with the team trained. No lock-in to Wingenious.
How it works

What happens, in order.

01

Pick

From the discovery findings we choose two or three challenges that score well on all three tests: low risk if it fails, low effort to prove, high gain if it works.

02

Prototype

Each one is built as a working prototype on your real data, in days rather than months. Built with Make.com where an automation platform fits, or with Claude where it does not.

03

Prove

Your leaders and the people who do the job see it run, side by side with how it is done today. Hours saved and errors removed are measured, not estimated.

04

Build

What proves itself is scoped in writing and built properly: production-grade, documented, with the team trained and a stabilisation period after go-live.

What the time is worth

A team of 12, each saving 2 hours a week, at £25 an hour, recovers about £28,800 a year.

An illustration, not a promise. Your own figures come out of the session, and the roadmap is built on them.

Once a few prototypes have become working tools, the list of ideas tends to grow. Some businesses want someone senior keeping an eye on the whole picture.

Next: Ongoing AI leadership →
Questions

Before you book.

Why prototypes rather than going straight to a build?

Because a demonstration beats a promise. A prototype costs days, not months, and shows your leaders exactly what AI and automation would do in your business. It removes the guesswork from the decision to invest, and it builds the confidence of the people who will use the finished tool.

What makes a challenge low risk, low effort and high gain?

Low risk: if the prototype fails, nothing important breaks and no customer notices. Low effort: the data exists, the process is understood, and it can be proved in days. High gain: it removes hours every week, or an error that costs money, for a team that will feel the difference immediately.

Do we need to have done the discovery stages first?

Not always. If the problem is clear and well understood, a prototype can be scoped from the free call. Where it is not clear, Departmental Discovery is the faster route to choosing the right challenges.

Who owns what is built?

You do. Code, configuration and data live in accounts in your name. Documentation is handed over with every build.

Is a full build a fixed price?

Builds are quoted against a written scope, informed by what the prototype proved. Changes to the scope are agreed in writing before they are built.

Next step

Start with a conversation.

One free hour with Gary. We learn about your business and both decide whether there is a fit. If there is, Leadership Discovery is the first paid stage.

No sales deck. No obligation after the call.