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Product Scoping
Know if your AI product works before you pay to build it
Most AI products die between the demo and production, and the expensive way to learn that is after the build. Our scoping sprint tests the one assumption your product depends on, against your real data, and gives you an honest build plan you can execute with us or with anyone else.
- Fixed scope, fixed price
- Real-data prototype
- Plan you can take anywhere
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What product scoping actually covers
Scoping is not a slide deck. The core of the sprint is a working prototype of the riskiest part of your product: can the model actually do the job on your real inputs, at a cost per task that leaves you a margin? Everything else in the plan hangs on that answer.
Around the prototype we map the boring 70% that makes AI products real: data access, auth, queues, rate limits, cost caps, and the human-review path for when the model is wrong. That is where most estimates lie, so it is where we spend the mapping time.
You get the findings either way. If the honest answer is "do not build this yet", we say so and show you what would have to change. That outcome costs you a sprint instead of a failed project.
Where this wins
Founders sizing an AI product idea
You have the idea and maybe designs; you need to know cost, timeline, and whether the AI core is feasible before raising or committing budget.
Teams stuck between vendors
Three quotes, three different architectures, no way to compare. A neutral scoping pass gives you the technical ground truth to decide on.
Products with one scary unknown
The app is standard except one hard part: extraction quality, latency, cost at scale. We prototype exactly that part first.
When NOT to use this
- · You already have a validated spec and a team; you need builders, not scoping.
- · There is no real example data to test against; scoping against guesses produces guesses.
- · The decision is already made and the sprint would only be documentation theater.
How the scoping sprint runs
One to two weeks, fixed scope. The prototype comes first because everything else depends on it.
01
Inputs and success criteria
We collect 10-20 real input examples and define what an acceptable output looks like, including what is unacceptable.
DeliverableEval criteria and example set
02
Riskiest-assumption prototype
The AI core gets built against your examples: model choice, prompting, and the quality-cost tradeoff measured, not guessed.
DeliverableWorking prototype with measured quality and cost per task
03
System mapping
Data sources, auth, integrations, hosting, compliance constraints, and the human-review path get mapped into an architecture.
DeliverableArchitecture recommendation
04
Honest sizing
Build phases with estimates, the assumptions behind each number, and what would move them.
DeliverableExecution plan with phased estimates
05
Decision readout
Build, change the approach, or stop: we present the evidence and our recommendation, and you keep everything either way.
DeliverableReadout call and full artifact handover
Typical timeline
1-2 weeks, fixed scope
Stack we build with
Claude (API) · Next.js · Supabase · n8n · PostHog
Scoping engagements we run
AI feature inside an existing SaaS
Will the model do the job well enough to charge for? Measured on your data before you commit a quarter.
Document-heavy workflow products
Extraction and classification quality is the product; we measure it first.
Agent products
Multi-step agents fail in ways demos hide. We prototype the failure modes, not just the happy path.
Chat and support products
Groundedness against your knowledge base, measured with an eval set you keep.
Internal AI tools
The build is easy; the question is whether the time saved justifies it. We size both sides.
Rebuild-or-extend decisions
Whether the legacy system can host the AI feature or needs replacing, answered with evidence.
Book a Free Scoping Call
Tell us the assumption your product depends on
A 30-minute call: we identify the riskiest part of your product and tell you whether a scoping sprint would answer it, or whether you can answer it cheaper yourself.
Book a 30min Free Strategy Call
In this call, we'll walk through your project scope, timeline, and goals - so we can both check if we're a fit. No obligation, no slide deck, just a working session.
Don't want a call? Email walid@ayautomate.com
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“The team is super fast - sometimes we had to slow them down. We managed to scale the company without investing into hiring.”
Elie Salame
COO, Adstronaut.io
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If you're serious about optimizing your operations or scaling smarter, book your spot now. Otherwise please don't waste your time and our time.
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FAQ
Product Scoping Questions
What does a scoping sprint cost?+
It is a fixed-scope, fixed-price engagement sized on the call, because the honest number depends on how hard the prototype is. It is deliberately priced so that skipping it and building blind would be the expensive choice.
Do we have to build with you afterwards?+
No. The plan is written so any competent team can execute it: architecture, estimates, eval set, and prototype code are yours. Some clients build with us, some take it in-house, and both are fine outcomes.
What if the answer is that our idea does not work?+
Then you learned it in two weeks instead of two quarters, with evidence you can use to redesign the product. We tell you what would have to change for the answer to become yes.
Why not just ask an AI to estimate the build?+
Estimates without a prototype are opinions. The sprint's value is the measured part: real quality and real cost per task on your real inputs, which no amount of asking produces.
What do you need from us to start?+
Real example inputs, one person who knows the process, and access to the systems the product must touch. Without those three, we will tell you to wait rather than start.