AI app development company
An AI app development company that tests the model before it builds the app
We build AI apps for founders and product teams: new AI-native web apps and AI features inside apps you already run. The model gets tested on your real data in week one, before anyone designs a screen. If it cannot do the job, you stop there and keep the money.
Free 30-min call. No pitch, no slides.
Built for 60+ companies. See the client list.
Free audit: your automation gaps and AI search visibility
Quick answer
AY Automate is an AI app development company that builds AI-native web applications and AI features in existing products, tests the model on the client's real data before building the interface, and ships with evaluation, logging and monitoring. Typical timeline: feasibility answer in week one; the full build is scoped after the discovery call.
Trusted by teams at
What it is
What an AI app development company does
An AI app development company designs, builds and runs software where a model does part of the work: reading documents, answering questions over your data, matching people to options, drafting text or acting through tools. The app around the model is ordinary software. The hard part is making the model part reliable enough that users trust it.
Most AI apps fail at the model step, not the interface. A demo that answers ten test questions well breaks on how real users phrase things. So we start with the riskiest question: can a model do this job on your data, at a quality and cost that works? We answer it with a test set from real examples before we write the app.
Then we build the app on the stack we run ourselves: Next.js on Vercel, Supabase or PostgreSQL, and Claude or OpenAI models chosen per task. Every release ships with the test set in CI, logging, fallbacks for when the model is wrong and cost tracking per feature.
Built on the stack we run ourselves
Pages on the specific builds and tools we use for AI apps.
Fit check
Is this the right fit?
Where this wins
Founders building an AI-native product
From the first feasibility test to a working web app with real auth, payments and monitoring.
Teams adding AI to an app they already run
Search over your data, an assistant that acts on app state, or matching and prediction inside the live product, without a rebuild.
AI prototypes that will not survive real users
A demo that works on the happy path gets a test set, fallbacks, rate limits and cost controls before it goes in front of customers.
When NOT to use this
- You need a native iOS or Android app first. We build web apps and the AI layer behind them; a mobile-first studio is the better fit.
- Your product has no real AI use case yet. Adding a model for the pitch deck adds cost and variance for nothing, and we will say so.
- You want a new foundation model trained from scratch. We are applied engineers who build on frontier model APIs.
How it works
How an AI app build runs
The riskiest question gets answered first, so the budget is spent only after the model has shown it can do the job.
- 01
Feasibility test
Week 1Run the core AI task against your real data and build the first test set. You get a go or no-go with the numbers behind it.
DeliverableTest results on your data
- 02
Architecture
Week 2App structure, data model with access rules, model routing, caching and streaming. The decisions that are expensive to change get made here.
DeliverableDeployed skeleton with the AI pipeline wired end to end
- 03
Build
Week 3 onwardThe screens and flows users touch, connected to the AI layer, with confidence thresholds and review queues where they are needed.
DeliverableWorking app behind real auth
- 04
Harden
Rate limits, abuse guards, prompt caching, cost tracking and the test set running in CI.
DeliverableMonitoring and cost dashboard
- 05
Launch and iterate
Product analytics on the AI features show what users actually do. We iterate on that, or hand over with docs.
DeliverableLive app, runbook and maintenance plan
Typical timeline
Feasibility answer in week one; the full build is scoped after the discovery call
Stack we build with
Next.js · Vercel · Supabase · PostgreSQL · Claude · OpenAI · TypeScript · Python · PostHog
Not sure a model can do the job?
That is what week one answers. Bring the idea and a sample of the real data, and we tell you on the call how we would test it.
Use cases
AI apps we build most often
Document apps
Apps that read, extract from and summarise documents, including in several languages, with review for low-confidence cases.
Search and answers over your data
Questions answered from your documents and records, with citations and access rules users cannot get around.
Matching and recommendation
Engines that match people to roles, products or options, with the reasons shown.
Assistants inside the product
An assistant that acts on real app state through tools, not a chat widget bolted to the side.
AI concierge and support apps
Customer-facing apps that answer and act in your users' language, with a handoff to a person.
Prototype hardening
An existing demo gets a test set, fallbacks, rate limits and cost controls so it can go live.
First 30 days
What your first 30 days look like
You see the model working on your own data before anyone designs a screen.
Week 1
Feasibility test
The core AI task run on your real data, with a test set and a go or no-go.
Week 2
Skeleton deployed
App structure, data model and the AI pipeline wired end to end on a preview link.
Week 3 to 4
First working flows
The main user flow connected to the AI layer, behind real auth.
Day 30
First test report
Where the model passes, where it fails and what changes next.
Scope
What one AI app build includes
In scope
- A feasibility test on your real data in week one, with results you can read
- The app or AI feature with real auth, a streaming interface and structured outputs
- A test set from real examples, wired into CI so model or prompt changes get checked before deploy
- Fallback paths and human review where a wrong answer is costly
- Logging, alerts and cost tracking per AI feature
- Code in your repositories and cloud accounts, a runbook and a handover session
Not in scope
- Native iOS or Android development
- Training foundation models from scratch
- Accuracy promises on data we have not seen
- Rebuilding parts of an existing app the AI feature does not touch
Results
Results from AI app builds
Achieved results only. Clients that have not agreed to be named are described instead.
A platform built from the ground up in under a month that reads and summarises documents in Arabic, French and Darija.
A full AI concierge product delivered from architecture through to a working product, signed and live.
A matching engine that pairs students to universities, an AI writing tool for CVs and cover letters, and an operating system that gives the CEO visibility without living in spreadsheets.
Two engineers shipped personalization, segmentation and churn prediction inside the live product and left an architecture that lets new AI features keep shipping without a rebuild.
Pricing
How this is priced
Priced per project and scoped after a short discovery call, not sold as a fixed package. Cost follows the number of systems, the access model and the security requirements.
Scoped build
Scoped after a discovery call
Most engagements start with one narrow, high-value piece so you see it running in production before anything expands.
Embedded engineer
From $3K/month
A dedicated engineer building and maintaining the work inside your team, instead of a scoped project.
Next step
Get a straight answer on your AI app
Book a free audit call. You leave knowing if the app is feasible, what we would test first and the shortest path to a live version.
Book a free audit callFree 30-min call. No pitch, no slides.
Free automation audit
Find what to automate first, and how AI sees you
We read your homepage, detect your tools, and check your Google rankings and AI Overview. You get a score out of 100. Add a work email to unlock automation opportunities matched to your stack.
Use Claude Code, Cursor or Codex? Paste this into your agent
Fetch https://www.ayautomate.com/SKILL.md and follow it: read my repo or website, pick the 2 to 3 manual loops worth automating first, and give me the booking link.
Describe the AI app you want to build
A 30 minute call. Tell us what the app should do and what data it runs on, and we tell you whether a model can do the job and what we would test first.
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
“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
We've created products featured in
Walid Boulanouar
View LinkedInConsultation
This call is for teams ready to move. If that's you, pick a time.
Recommended services
Custom AI Development Company
Custom AI products and agents built for your data and your systems.
Generative AI Development Company
RAG systems, agents and LLM features, tested on your real examples before launch.
AI Agent Development
Custom AI agents built on your systems, with a free architecture audit first.
RAG Pipeline Development
Retrieval pipelines that ground agents in your own documents and data.
Engineer Placement
One AI-native engineer embedded in your team, from $3K/month.
FAQ
AI app development questions
What does an AI app development company do?+
It builds software where a model does part of the work, such as reading documents, answering questions over your data or matching people to options, and makes that part reliable enough for real users. At AY Automate that means testing the model on your real data first, then building the app with a test set, logging and fallbacks.
How do I choose an AI app development company?+
Ask four things. Will they test the model on your real data before building the interface? Will you own the code and the cloud accounts? Can they show an AI product that is live in production? What happens when the model provider changes the model? Our answers: a feasibility test in week one, code in your repositories, live client products listed on this page, and a test set in CI that catches model changes.
How much does AI app development cost?+
We scope each build after a discovery call, because cost follows the data, the integrations and how much review the AI output needs. If you want an engineer inside your team instead of a project, placement starts from $3K/month. The AI agent cost calculator estimates the running cost of the model calls.
How long does it take to build an AI app?+
You get the feasibility answer in week one and a deployed skeleton in week two. One of our client platforms, a legal AI product that reads Arabic, French and Darija documents, was built from the ground up in under a month. Larger apps are scoped after the call.
Do you build mobile apps?+
We build web apps and the AI layer behind them, which a mobile app can call. If you need a native iOS or Android app first, a mobile-first studio is a better fit, and we will tell you that on the call.
Can you add AI to an app we already have?+
Yes. We ship inside the live product. For a loyalty platform, two of our engineers shipped personalization, segmentation and churn prediction inside the existing app without a rebuild.
Who owns the code and the data?+
You do. We build in your repositories and cloud accounts, use your model accounts and hand over documentation and a runbook.