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Generative AI development company
A generative AI development company that tests on your data before launch
We build LLM products that run in production: answers grounded in your documents, agents that work inside your tools, and generative features in a product you already run. Each build starts with a test set of your real examples, so you see how it performs before anyone relies on it.
Book a free audit call
Free 30-min call. No pitch, no slides.
Scoped after a discovery call, because cost follows the systems, the data access and the review steps.
Built for a sample of 40+ named companies. See the client list.
Quick answer
AY Automate is a generative AI development company that builds LLM products for clients: retrieval (RAG) systems over their documents, AI agents connected to their tools, and LLM features inside live products, each tested against a set of real examples before release.
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What it is
What a generative AI development company builds
A generative AI development company builds software where a large language model writes, answers, summarises or decides as part of the product. In practice that means three kinds of work: retrieval systems that answer from your own documents, agents that read and act in your tools, and LLM features shipped inside an existing product.
The model is the easy part to buy. What takes the work is everything around it: which documents it can see, which actions it may take, what it costs per run, and how you know an answer is right. We write those down first, collect real examples with the answers a good result should give, and test every version against them.
We do not train foundation models. We build on Claude and OpenAI models through your approved accounts, chosen per task after testing, in TypeScript and Python. If a non-generative approach or an off-the-shelf tool does the job better, we say so.
Comparing options before you commit? How to choose a generative AI development company.
Fit check
Is this the right fit?
Where this wins
Teams sitting on documents nobody can search
Contracts, policies, tickets or manuals turned into answers with the source attached.
Products that need a generative feature
Drafting, summarising, chat over account data or classification shipped inside the live product.
Founders building an LLM-native product
Architecture through to a working product, with evaluation in place from the first version.
When NOT to use this
- You want a foundation model trained from scratch. That is research lab work, and we do not do it.
- The job needs exact, repeatable outputs every time, such as a tax calculation. Ordinary code is cheaper and safer there.
- There are no real examples to test against and no one who can judge a good answer. Without that, nobody can show the system works.
How it works
How a generative AI build runs
The test set comes before the build, so every version is judged on the same real examples.
- 01
Define the use case
Week 1Write down what the system must produce, for whom, and what a wrong answer costs.
DeliverableOne-page spec and success measures
- 02
Build the test set
Week 1 to 2Collect real inputs from your documents, tickets or product, with the answers a good result should give.
DeliverableTest set
- 03
Build the first version
Week 2 to 5Retrieval, prompts and tools wired to your systems in a sandbox, with the model chosen by testing.
DeliverableWorking first version
- 04
Test and tighten
Week 5 to 7Run the test set, fix retrieval and prompt failures, and add human approval where errors are costly.
DeliverableTest report
- 05
Release and monitor
Go live with logging, cost tracking 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
Claude · OpenAI · TypeScript · Python · Next.js · PostgreSQL · Supabase · n8n
Not sure an LLM is the right tool?
We tell you straight if ordinary code or a product you can buy does the job. If not, we scope the smallest build that proves the value on your data.
Use cases
What we build most often
Answers from your documents
RAG systems that answer staff or customer questions and cite the source document.
Agents that work in your systems
Agents that query a database or CRM and produce reports, limited to what each person may see.
Multilingual document tools
Reading and summarising documents in the languages your users work in.
First 30 days
What your first 30 days look like
A spec, a test set and a first version answering real questions in a sandbox.
Week 1
Spec and success measures
The use case in plain language and the numbers we will judge it by.
Week 2
Test set
Real questions or inputs, with the answers a good result should give.
Week 3 to 4
First working version
Connected to your documents and systems in a sandbox, not yet live.
Day 30
First test report
Where it answers correctly, where it fails and what we change next.
Scope
Scope and exclusions
In scope
- Use case definition and the measures it will be judged by
- Test set built from your real examples
- RAG pipeline, agent or LLM feature built on your data and systems
- Model choice per task, with cost per run estimated
- Logging, alerts and a monitoring plan, then handover or ongoing maintenance
Not in scope
- Training foundation models from scratch
- Image or video model research
- Accuracy guarantees on data we have not tested
- Chatbots with no defined job or owner
Results
Results from generative AI 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.
The whole database became an agent anyone can talk to, with each person seeing and generating reports only for what they are allowed to touch.
A full AI concierge product delivered from architecture through to a working product, signed and live.
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 $60,000/year
A dedicated engineer building and maintaining the work inside your team, instead of a scoped project.
Next step
Scope your generative AI build
Book a free audit call. Bring the use case, a few real examples and the systems involved.
Book a free audit callBook a free audit call
Free 30-min call. No pitch, no slides.
Book a free audit call
Describe the generative AI use case
A 30 minute call. Tell us the job, the documents and the systems, and we tell you what a build would involve and whether an LLM is the right tool.
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
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This call is for teams ready to move. If that's you, pick a time.
30min
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Usually responds in 1 hour
No commitment required
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FAQ
Generative AI development questions
What does a generative AI development company do?+
A generative AI development company builds software where a large language model produces the output: answers from your documents, drafted text, summaries or actions taken by an agent. The work covers the use case, the data the model may see, testing against real examples, and running the system in production with logging and cost tracking.
How do I choose a generative AI development company?+
Ask four things. Will they build a test set from your real examples and show you the results? Can they show a system that is still live in production? Who owns the code and the model accounts? How do they handle a model update from the provider? Our answers: a test set in week 2, code in your repositories, and monitoring plus an optional maintenance plan after release.
Is this different from custom AI development?+
This page covers products built on large language models: RAG, agents and generative features. Our custom AI development company page covers the wider set, including matching, prediction and internal tools that may not use an LLM at all.
Do you train or fine-tune models on our data?+
Usually not. We use retrieval, tools and prompts against your data through approved model routes. Fine-tuning is a separate decision and only makes sense when testing shows it helps.
Which models do you use?+
Mostly Claude and OpenAI models, chosen per task after testing on your examples. A smaller, cheaper model often handles routing or classification steps, with a larger one where the answer quality needs it.
What does it cost?+
Scoped after a discovery call, because cost follows the systems, the data access and the review steps. Use the AI agent cost calculator to estimate running costs. Placement of an embedded engineer starts from $60,000/year.
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.