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Custom AI development company
Custom AI development, built for production from day one
We build AI agents, product features and internal tools around your data and your systems. Every build ships with tests against real examples, logging and monitoring, because a demo that cannot be trusted is not a product.
- Your data, your systems
- Tested on real examples
- Monitored in production
Trusted by teams at
What custom AI development means here
Off-the-shelf tools cover the common cases. Custom development is for the rest: an agent that has to work across your specific systems, an AI feature inside a live product, or a tool shaped around how your team works.
We build in a fixed order. Define the job and how success is measured. Collect real examples to test against. Build the smallest version that does the job. Test it against the examples. Release with logging and alerts. The test set is what tells us when the model, the prompt or the data needs fixing.
We work in TypeScript and Python with Claude and OpenAI models, chosen per task. For existing products, we ship inside the live product instead of proposing a rebuild.
Where this wins
Products that need AI features without a rebuild
Personalization, segmentation, matching or prediction shipped inside the live product.
Teams with unique data and workflows
Agents and tools built around your systems, permissions and language needs.
Founders launching an AI-native product
Architecture through to a working product, built to be extended.
When NOT to use this
- · An off-the-shelf tool already does the job. We will say so, and the build vs buy page helps you check.
- · You want a research lab. We build applied systems for defined jobs.
- · You cannot provide examples or data access. Without something to test against, we cannot show it works.
How a custom build runs
Small, tested steps, so you see working software early and the risk shows up before the budget is spent.
01
Define the job
Week 1Write down what the system must do, for whom and how we will know it works.
DeliverableOne-page spec and success measures
02
Collect examples
Week 1 to 2Gather real inputs and the outputs a good result should produce.
DeliverableTest set
03
Build the first version
Week 2 to 5The smallest version that does the job, connected to your systems in a sandbox.
DeliverableWorking first version
04
Test and tighten
Week 5 to 7Run the test set, fix what fails and add approvals where errors are costly.
DeliverableTest report
05
Release and monitor
Go live with logging and alerts, then review the first weeks of real use.
DeliverableLive system and monitoring
Typical timeline
A focused build, scoped after the discovery call
Stack we build with
TypeScript · Python · Claude · OpenAI · Next.js · PostgreSQL · Supabase · n8n
Custom build or off-the-shelf?
We tell you straight if a tool you can buy does the job. If not, we scope the smallest custom build that proves the value.
What we build most often
AI features in a live product
Personalization, segmentation and churn prediction inside an existing platform.
Multilingual document and language tools
Systems that read and summarise documents in the languages your users work in.
Matching and recommendation engines
Automatic matching between people, roles, products or opportunities.
What your first 30 days look like
A spec, a test set and a first version running in a sandbox.
Week 1
Spec and success measures
The job in plain language and the numbers we will judge it by.
Week 2
Test set
Real examples collected, with the outputs a good result would produce.
Week 3 to 4
First working version
Connected to your systems in a sandbox, not yet live.
Day 30
First test report
Where it passes, where it fails and what we change next.
Scope and exclusions
In scope
- · Job definition and success measures
- · Agent, feature or tool build on your data and systems
- · Test set from real examples and regression checks
- · Logging, alerts and a monitoring plan
- · Handover and, if you want it, ongoing maintenance
Not in scope
- · Training foundation models from scratch
- · Hardware or on-device model work
- · Accuracy guarantees on data we have not seen
- · Products with no defined user or job
Results from custom builds
Achieved results only. Clients that have not agreed to be named are described instead.
Neoday, loyalty platform
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.
Kateb, legal AI
A platform built from the ground up in under a month that reads and summarises documents in Arabic, French and Darija.
Tourtlee, AI local concierge
A full AI concierge product delivered from architecture through to a working product, signed and live.
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 $60,000/year
A dedicated engineer building and maintaining the work inside your team, instead of a scoped project.
Scope your custom AI build
Book a free audit call. Bring the job, a few real examples and the systems involved.
Book a free audit callBook a free audit call
Describe the job you want done
A 30 minute call. Tell us the job, the data and the systems, and we tell you what a custom build would involve.
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
Opportunity Map
Implementation Path
Fast Follow-Up
“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
Video Call
Phone Call
In-Person
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.
30min
CalGoogle Meet
Usually responds in 1 hour
No commitment required
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FAQ
Custom AI development questions
Do you build on our data without training a model on it?+
We use retrieval, tools and prompts against your data through approved model routes. Training or fine-tuning is a separate decision and only makes sense when testing shows it helps.
How do you know the AI works?+
We build a test set from real examples before the build and run it after every change. Results and failures go in the test report, so the decision to go live rests on evidence.
Can you add AI to a product we already run?+
Yes. We ship inside the live product and leave an architecture that supports new AI features without a rebuild.
What does it cost?+
Scoped after a discovery call, because cost follows systems, access and review needs. Use the AI agent cost calculator to estimate running costs. Placement of an embedded engineer starts from $60,000/year.
Who owns the code?+
You do. We build in your repositories and cloud accounts, and hand over documentation and a runbook.