Everyone wants to use AI. Most teams are stuck between "this sounds useful" and "we need to spend how much to find out?"
That gap is real. And it causes two kinds of mistakes.
The first: do nothing. The team reads documents by hand, processes data manually, answers the same questions 40 times a day - because nobody can justify a budget for something with no proof yet.
The second: dive in blind. A vendor promises a fixed price, signs a contract, then discovers six months in that your data is messier than expected, the use case is harder to define than it looked, and "it works" is subjective enough that nobody agrees whether the thing succeeded.
Both mistakes are expensive. The second one is more expensive.
Why a blind fixed-price AI build is a trap
AI projects have a specific problem that most software projects do not: the output is probabilistic and the quality of results depends heavily on the quality of your input data.
A developer can quote you a login screen because the requirements are clear. You either log in or you don't.
An AI system that answers questions from your documents? The answer depends on how those documents are written, how consistent they are, whether there are contradictions, and what "a good answer" even means for your use case. Two people can look at the same output and disagree on whether it works.
If a vendor gives you a fixed price before looking at your data, they are guessing. You are paying for that guess.
The 2-week sprint as the safe front door
The GenAI Scoping Sprint is AED 5,000, fixed. It runs for 10 working days. It is designed to answer one question before you commit a large budget: is this actually possible on your data, and what will it take?
It is not a sales call. It is not a presentation. It is a working engagement.
The 4 deliverables you leave with
1. Data assessment. We look at your actual data - documents, spreadsheets, databases, whatever you have. We tell you what it can realistically support, where it falls short, and what would need to change. You get an honest picture, not a pitch.
2. A working proof-of-concept. Built on a sample of your real data. Not a demo on invented data. Not a screenshot. Something you can interact with and test. If it does not work convincingly on a small sample, it will not work at scale either - and you find that out for AED 5,000 instead of far more.
3. Architecture and run-cost estimate. How the full system would be built. Where it runs (cloud, self-hosted, on-device). What it costs to operate per month. No surprises later.
4. A fixed build quote with agreed metrics. If you decide to go ahead, you get a number to build the full system - with the success metrics defined in writing before you commit. Not "it should work well." Specific, measurable, agreed.
Why paying for scoping beats a free quote
A free quote is a sales document. It is optimistic about scope, soft on risk, and based on a conversation rather than a look at your actual data.
The sprint fee pays for real work. You get the assessment, the POC, the architecture plan, and the quote - whether or not you build with us. Everything you learn in the sprint is yours. The sprint fee is credited toward the build if you go ahead.
If the sprint shows that AI will not help you - we tell you. You have saved a much larger mistake for AED 5,000. That is the point.
What happens after
If the sprint goes well and the numbers make sense, you have a fixed quote, agreed metrics, and a clear architecture to hand to any team - ours or someone else's.
If you build with us, the sprint fee comes off the build cost. The POC becomes the foundation. There are no surprises about scope because scope was defined in the sprint.
The sprint is the safe entry point. It keeps a useful idea alive without a six-figure bet on an unknown.
If you want to explore what AI could do on your data, the sprint is where to start. Book a 30-min call or email [email protected].