AI-Powered Mobile Apps
Intelligence that doesn't wait for a signal
Signal isn't a switch. It's a tide that goes out in the tunnel, the aisle, and the air, and comes back. We build cross-platform apps where the intelligence keeps working through the gap, because that's where phones actually get used.
One ordinary day
Signal · 06:00 → 21:30
3 dead zones between breakfast and bed. A cloud-only app is down for all three, and they land at the exact moments a phone comes out of a pocket.
- 1 codebase
- iOS and Android
- On-device
- Where it earns its place
- Offline-tolerant
- Core flows survive the gap
- OTA
- Fixes without a store wait
The brief
The phone is the best place for AI, and the worst place to depend on the cloud
Mobile is the most personal, most sensor-rich surface you have: a camera, a microphone, location, and context, always in hand and always in the moment. That's exactly where AI earns its keep. Scan instead of type, ask instead of search, get a suggestion instead of a blank screen.
It's also the surface where the network is least reliable. The same pocket that makes the camera useful takes it into an elevator, a basement, and a plane. An app built on the assumption of connectivity fails on schedule, several times a day, at the moments it was reached for.
One question, before any other
Where is the user standing when they open this?
The answer decides what runs on the phone and what can use the network. Get it right at the start and the app feels instant and unbreakable. Get it wrong and you find out in the reviews, where fixing it means a rewrite instead of a release.
What changes
Features that answer when the network doesn't
The moments your users hit most (scan, ask, capture) keep working in the tunnel, the aisle, and the basement, because they never needed the round trip.
Two platforms that stay in step
One codebase means iOS and Android ship the same feature on the same day, and neither quietly drifts a release behind the other.
AI where people already are
Intelligence inside the screens they already use, instead of a chatbot parked in a corner they have to remember to open.
Fixes that reach users in hours
Over-the-air updates carry most improvements straight to phones, so a bad afternoon doesn't wait behind a review queue.
What we build
Intelligence sized to a glance
Vision and scanning
The camera becomes an input. Point it to identify, read, measure, or translate, and skip the form the user would otherwise fill in by thumb.
Voice and dictation
For the hands-full moments a phone is actually used in: gloves on, luggage in both hands, halfway up a ladder. Speak it instead of typing it.
In-app assistants
A helper that answers, guides, and acts inside your app, grounded in your data instead of a general chatbot guessing at what your product does.
Personalization
Feeds, recommendations, and journeys that reshape around each user as they go, so the first screen is useful instead of a blank starting point.
Smart automation
Reminders, drafts, and suggestions that arrive on the next step instead of waiting to be asked for.
The engagement
Nine weeks, in the order they actually happen
Week 1
Map the tide
We find out where your users actually stand when they open the app (the depot, the aisle, the train) and which of those places has no signal. That map decides the architecture.
Weeks 2–3
Shape the moments
We design the AI moments around real mobile behavior: thumbs, glances, and interruptions. A feature that needs two hands and a quiet room won't get used.
Weeks 4–8
Build both shores
One codebase produces iOS and Android, with the AI features engineered as first-class parts of the app instead of bolted on at the end.
Week 9
Through review
Builds, TestFlight and Play testing, listings, privacy declarations, and the review itself. We run the submission so you're not learning provisioning profiles the hard way.
After launch
Don't wait for the tide
Analytics tell us what's actually being used, and over-the-air updates carry most improvements out without queueing for a store release.
How we work
Four moves, and what we're strict about in each
The phases are the ordinary ones. What matters is the rule we hold inside each: the place where mobile projects usually give ground and later pay for it.
Design. Every AI moment gets sized to a glance. If it can't be understood with a thumb on a moving train, it gets redesigned instead of shipped and explained.
Build. React Native and Expo give both platforms a native feel from one codebase. This is modern cross-platform, well past the old hybrid compromise.
Ship. Signing, store metadata, privacy declarations, and review. The unglamorous path that decides whether a finished app is a released app.
Iterate. Crash reporting and analytics land from day one, so the second release is aimed at what real users hit instead of what we assumed they would.
The split
Three questions decide what runs on the phone
On-device isn't a badge to wear. It costs model size, battery, and build complexity, so every feature has to earn its place on the phone by answering yes to at least one of these.
Test 01
Does it have to be instant?
A scan that waits on a round trip is a form with extra steps. Anything the user expects to happen at the speed of pointing a camera runs on the phone, because a good network is still slower than no network at all.
Test 02
Should the data leave the phone?
Some things shouldn't travel: a face, a document, a location trace, a medical note. When the honest answer is that it should stay on the device, the model comes to the data instead of the data going to the model.
Test 03
Will it be needed at low tide?
If the feature has to work in the tunnel, the aisle, or the air, it can't live in the cloud. We decide this per feature, early, because retrofitting offline into an app that assumed connectivity is close to a rewrite.
Yes to any one of the three, and it runs on the device. No to all three, and it doesn't need to.
Why it's built this way
One codebase costs less to keep
Two native apps means two of everything: two bug queues, two release trains, two teams drifting apart. One codebase is the version you can still afford to maintain in year three.
Privacy you can point at
On-device processing is more than a promise in a policy document. If the data never leaves the phone, there's nothing to leak, and you can say so plainly.
Built for the real world, beyond the demo
Demos happen on office Wi-Fi. Apps happen in parking garages and elevators. Designing for the gap up front is what stops the five-star app from becoming a two-star one in the field.
Speed that reads as quality
Local intelligence responds instantly, and instant is what users read as well-made. The same feature behind a spinner reads as broken.
Selected work
Two apps that had to work with the signal gone
What you get
A released app, not a demo
Live on both stores, instrumented, updatable, and holding up in the places your users actually stand.
- ▸A cross-platform iOS and Android app from a single codebase
- ▸AI features built into the core experience instead of attached to the edge of it
- ▸On-device intelligence wherever speed, privacy, or the dead zones demand it
- ▸App Store and Play Store submission handled end to end
- ▸Analytics and crash reporting wired in before launch
- ▸An over-the-air update path so fixes ship without a store wait
Where the tide goes out
Industries where the dead zone is the job
- Retail and e-commerce
- Scan-to-identify and in-aisle answers, on shop floors where the signal stops at the front doors.
- Travel and tourism
- Companions that still work once roaming is switched off and the traveler is furthest from help.
- Home and field services
- Voice capture and offline job records for crews working in crawlspaces, plant rooms, and basements.
- Healthcare and dental
- Capture and assistance where the data is sensitive enough that on-device is the only honest answer.
- Logistics and distribution
- Scanning and proof-of-delivery that survives the loading bay, the trailer, and the rural route.
- Restaurants and hospitality
- Fast, glanceable ordering and service tools for staff who never have both hands free.
Start here
Tell us where your users are standing
That one answer tells us more about what your app should be than a feature list will. Bring us the places, and we'll tell you what has to live on the phone.
Why us for this
We ask where before we ask what
The first question is where the user is standing, well before which model. Everything downstream follows from that answer, and most teams never ask it.
AI engineers and mobile engineers, same team
On-device intelligence lives exactly where those two disciplines meet. Split them across two vendors and the seam becomes your problem.
We'll tell you when AI isn't the answer
Some features are a button. If a model would only add latency and a failure mode to something a menu already does well, we'll say so.
Working with Flaidex
We hand over the keys. The codebase, the pipeline, the store accounts, and the documentation are yours. Nothing about the setup is designed to keep you calling us.
One team from map to store. The people who mapped the dead zones are the people who ship the release. No handoff to a delivery team that wasn't in the room.
Honest about the store. Review timelines and rejections are real and we plan around them out loud, instead of promising a launch date the process can't hold.
Questions
What people ask before they build
iOS and Android: are we paying for two apps?
No. We build on React Native and Expo, so one codebase produces apps that feel native on both platforms. It's faster to build, cheaper to maintain, and keeps the two in sync, without the compromises of an old-style hybrid app.
Does the AI run on the phone or in the cloud?
Whichever the feature honestly needs. Anything that has to be instant, has to stay private, or has to survive a dead zone runs on the device. Heavier, open-ended work runs in the cloud, where it's stronger and cheaper. We make that call per feature, early, and we'll show you our reasoning.
Will it actually work offline?
The core flows will, because we decide which ones must survive before we design the data layer. Work done offline is stored locally and syncs cleanly when the connection returns, with conflicts resolved instead of silently dropped. What we won't do is claim every feature works offline. Some genuinely need the network, and we'll tell you which.
Can you add AI to our existing app?
Often, yes. If your app is on a modern stack we can build AI features into it directly. If it's aging, we'll tell you honestly whether extending it or modernizing first is the better use of your money, even when that answer costs us the work.
Do you handle the app store submissions?
Yes: builds, TestFlight and Play testing, store listings, privacy declarations, review, and updates. Store review is a real gate with real rejections, so we plan for it as part of the timeline instead of treating it as a formality at the end.
How do updates work after launch?
Alongside normal store releases, we use over-the-air updates for many changes, so improvements and fixes reach users in hours instead of waiting on a full review cycle. Changes that touch native code still go through the stores, and we're clear about which is which.
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Let's talk
Running a large platform, shaping a first MVP, or getting a product ready for a funding round? Tell us where you are. We'll shape the process around it, and stay with you after launch.
