AI Strategy & Enablement
Prove the idea before you fund the build
You have an AI idea and no way to test it without a six-month project and a large invoice. We build a working prototype on your own data in a few weeks, so you decide with evidence, not a slide deck.
2 to 4
weeks from kickoff to a working prototype
1
fixed scope, one use case, no mid-sprint additions
AR + EN
prototypes tested in both languages where relevant
The direct answer
An AI proof-of-concept sprint is a fixed-scope, fixed-timeline engagement that builds a working prototype of one AI use case on your own data, so you can see whether it actually works before committing to a full build. It is for companies with a specific idea (automating a document review step, a forecasting model, an internal search tool) who need evidence before they fund it, not a slide deck full of promises.
What this removes.
No way to test before you commit
Today
Vendors want a signed multi-month contract before you see anything working.
With the system
You see a working prototype on your own data in weeks, then decide.
Generic AI demos, not your data
Today
Sales demos run on clean sample data that never resembles your actual files and edge cases.
With the system
The prototype runs on your real documents, your real spreadsheets, your real exceptions.
No clear answer on feasibility
Today
You don't know if the idea is a two-week build or a six-month problem until you're already paying for it.
With the system
You get a written feasibility read, a cost-to-scale estimate, and the risks, before the big spend.
Stalls after the pilot
Today
A pilot works once in a demo, then nobody knows what it would take to run it for real.
With the system
The sprint ends with a scoped handover plan for turning the prototype into a production system.
What lands in your hands.
Working prototype
A functioning version of the use case, running on a sample of your real data.
Feasibility report
A plain-language read on what worked, what didn't, and why.
Data-readiness assessment
What data you have, what's missing, and what needs cleanup to scale.
Cost-to-scale estimate
A rough budget and timeline for turning the prototype into production.
Risk and limitation notes
Where the approach breaks, where it needs a human in the loop.
Go or no-go recommendation
A direct recommendation, including when the honest answer is no.
Systems and platforms we work with
- OpenAI
- Anthropic
- Google Gemini
- Meta
Five stages. You sign off every one.
Read each stage as a small contract: what we need from you, what lands in your hands, and the sentence that has to be true before we move on.
- Scope the use case2 to 3 days
- Data access and setup2 to 4 days
- Build the prototype1 to 2 weeks
- Test and evaluate3 to 5 days
- Handover and next step1 to 2 days
Scope the use case
2 to 3 days
We pin down the one use case the sprint will test, the data it needs, and what a successful outcome looks like, in writing, before any building starts.
- Name the specific use case and its owner
- Share a sample of the relevant data
- A one-page scope with a defined success measure
- A fixed sprint timeline and price
We move on when we both sign off on the same scope, data, and definition of done.
Data access and setup
2 to 4 days
We get access to the real data the prototype will run on, check its quality, and flag any gaps before they cost sprint time later.
- Grant access to the agreed data source
- Confirm any privacy or PDPL constraints on the data
- A short data-quality note
- A build plan adjusted for what the data actually looks like
We move on when we have usable access to real data and a build plan that accounts for its gaps.
Build the prototype
1 to 2 weeks
We build the working version, testing it against your real data as we go rather than saving all testing for the end.
- Answer questions on edge cases as they come up
- Review an early working version midway through
- A functioning prototype covering the agreed scope
- Notes on any scope items that proved harder than expected
We move on when the prototype runs end to end on real data and produces results your team can judge.
Test and evaluate
3 to 5 days
We run the prototype against a broader slice of your data, measure accuracy or output quality against the success measure from stage one, and write up what we found.
- Review the outputs against your own judgment
- Flag any results that look wrong or surprising
- A feasibility report with the results
- A cost-to-scale estimate and a go or no-go recommendation
We move on when you have a written recommendation you can take to whoever holds the budget.
Handover and next step
1 to 2 days
We hand over everything: the prototype, the code, the report, and a scoped plan for what production would take, whether that's with us or another team.
- Decide whether to proceed to a build
- Nominate who owns the decision internally
- Full handover of the prototype and documentation
- A scoped production plan if you choose to proceed
We move on when you hold a decision-ready package and the prototype works whether or not you build with us next.
Asked before signing.
How is the price set for a sprint like this?
Fixed price per sprint, agreed after the scoping call in stage one, based on the use case, the data involved, and the sprint length. No hourly billing and no scope creep mid-sprint. If the use case turns out to need more than one sprint, we say so upfront rather than padding the invoice as we go.
What happens if the answer turns out to be no?
You still get the report, the prototype code, and the reasoning behind the no. A sprint that saves you from a six-month build that wouldn't have worked is not a failed sprint. We'd rather tell you that in week three than let you find out in month five.
Can the sprint run in Arabic, or on Arabic documents?
Yes. Where the use case involves Arabic text, documents, or a bilingual team, we test the prototype in both languages during the sprint, not as an afterthought. Tell us which languages matter during scoping and we build the test data to match.
Who owns the data, and does this comply with PDPL?
You own your data throughout. We work with a copy or a scoped access grant, never a permanent export, and we handle personal data under Saudi PDPL requirements: data minimization, purpose limits, and deletion once the sprint closes unless you ask us to keep it for a follow-on build.
Do you build the production system afterward?
Only if you want us to, and only as a separate, separately scoped engagement. The sprint's job is to answer whether the idea works, not to lock you into a build. The handover plan works whether the next team is us, your own developers, or someone else entirely.
Have an AI idea? Test it before you fund it
Tell us the use case and the data behind it. We'll scope a fixed-price sprint and tell you honestly whether it's worth building.