AI in products

How to build an AI-powered app A feature, not a founding myth

Short answer

Start with a job a person already has: save a thought, start a focus session, quote a wait time. Add AI only where it changes that job. Ship it in the same app as the rest of the loop. RR Tech does not claim to train custom large language models. Public examples are BrainSync AI (capture, NL reminders, search in your own words), Ghost Mode (Adaptive Sessions), and MVPs that list AI/ML — InspectAI and WaitlistAI.

Most “AI app” briefs are two products taped together: a normal CRUD client, plus a chat window. Users ignore the chat. You still paid for it. The builds on this site go the other way: the AI is inside a screen people already needed.

Jobs, not models

What we can point at — no vendor names we did not publish

Product	AI-shaped behaviour (as written)	Stack we name
BrainSync AI	Reminders from phrases like “Call mom tomorrow 9am.” Search in your own words, including voice and suggestions.	Kotlin, AI, Cloud. 6 weeks. In production. OS/store not separately named.
Ghost Mode	Adaptive Sessions can recommend 20, 35, 50, or 90 minutes from the user’s patterns, and explain the pick.	KMP, iOS, Android. 4 weeks. In production.
WaitlistAI	Average wait with an AI confidence band so staff can quote a time.	AI/ML, Mobile, SaaS. 2 weeks. MVP.
InspectAI	AI/ML listed on a field-inspection MVP. The published screens are the briefing and jobs — we do not invent a hidden model demo.	AI/ML, Mobile, Cloud. 2 weeks. MVP.

A sequence that stays honest

Write the non-AI loop first. BrainSync still needs Memories, Reminders, Search, Settings as tabs. Ghost Mode still needs a timer and a block list if Adaptive Sessions is off.
Name the uncertainty. Parsing “Pay rent Friday noon” is language. Suggesting 20 minutes is a pattern over past sessions. A ±3 minute band on wait time is a forecast with a visible error. Those are different engineering objects.
Require data or a fallback. Adaptive Sessions is user-overridable. A wait band should not ship if the floor has no data — the AI page is explicit: we will only sell that class of feature if the data exists.
Keep the model off the founding slide. Ship Kotlin/Swift/KMP/Firebase/cloud as the product needs. The model is a dependency, not the architecture diagram.
Label MVP vs production. InspectAI and WaitlistAI are MVPs. BrainSync and Ghost Mode are in production. Do not borrow production confidence for a 2-week experiment.

Product design that makes AI usable

Cheap capture. BrainSync: plus button, voice, tags, priority. If capture is hard, search never gets a corpus.
Show the why. Ghost Mode’s Adaptive Sessions explains the length. A silent recommendation trains distrust.
Stay in one app. Notes, alarms, and search as three tools is the problem BrainSync was written against.
Do not hide platform work. Ghost Mode still had to block apps on iOS and Android. AI did not replace permissions.

When you should not add AI

Leave it out of v1 if…

The core job is a live feed and a scan-friendly table (FCH’s published value is fixtures and form, not a model)
You cannot describe the input and the output in one sentence
You want a demo for investors more than a behaviour for users
You expect a studio to “just train a model” with no data story — that is not on this website

If the first version is still “prove the loop,” start with how to build an MVP. If you already know the job is an AI-shaped feature inside a product, AI development is the service page that matches this article.

Have a job, or just a model name?

If you can describe the screen that changes, we can scope an AI feature inside a real app. If you need a lab-trained LLM, that is not what this site sells.

Book a free discovery call

If this matches the brief: AI in the product · when the first slice can include AI

Work BrainSync AI · Ghost Mode · InspectAI · WaitlistAI

All resources