AI Product Development
Idea to production AI product — built by people who have to keep it running.
- Timeline
- First production slice in weeks, not quarters
- Engagement
- Fixed-scope phases against a signed SOW
- Built for
- Founders and product owners with a real user problem, not a technology mandate
The problem
Demos are cheap now. Anyone can show you a working prototype in a week. Almost nobody can tell you what it costs per task at ten thousand users, or prove it still works after the model version changes.
Who this is for
- Founders and product owners with a real user problem, not a technology mandate
- Businesses adding an AI product line rather than an AI feature
Who this is not for
- Proof-of-concept work with no route to production
- Teams who want a prototype and will decide about production later
How it runs
- 01
Shape
The job to be done, the failure modes, and an honest feasibility call — including telling you when the answer is no.
- 02
Evals before build
How we will know it works, written down first. Without this you cannot tell a good release from a bad one.
- 03
Thin slice to production
One real workflow, real users, in production — not a demo environment.
- 04
Harden and scale
Guardrails, cost controls and the load path to the tenth thousand user.
- 05
Hand to Run
Into AI in Production, or into your team with the runbooks to operate it.
What you get
- Eval suite that runs on every change
- Guardrails and abuse handling
- Per-task cost model at realistic volume
- Production deployment on web, mobile or API
- Runbook and operational handover
AI Product Development: questions we get asked
How long does it take to build an AI product from scratch?
The first real workflow reaches production in weeks. We deliberately ship a thin slice to real users early, because the questions that matter — cost, quality, edge cases — only produce honest answers under real traffic.
How do you know an AI feature actually works before launch?
We write the evaluation suite before we write the feature. It defines what correct output looks like, runs on every change, and is the reason a quality regression shows up as a failing build rather than as a customer complaint.
What does an AI product cost to run per user?
We model cost per task at realistic volume during the build, not after launch. Per-task cost is a design constraint that shapes model choice, caching and retrieval — treat it as an afterthought and it can move ten-fold on a usage pattern change.
What happens when the model version changes?
Your eval suite is run against the new version before it is adopted. That converts a provider deprecation from an emergency into a scheduled change with evidence behind it.
Delivered in your region.
- GDPR
- UAE PDPL
- ISO 27001 practices
- Data residency in the EU, the UAE, or your own cloud account
APPINE L.L.C-FZ, Dubai
$ appine assess --fixed-fee
Two weeks. Fixed fee. You end with a decision, not a deck.
A system and codebase audit, a risk register ranked by severity, an engineering baseline, and an explicit recommendation — keep, harden, rebuild, or don't do it at all.
If the answer is “don't hire us,” we'll write that down too.