Ship production AI in your Rails app

We help Rails shops ship production AI features — RAG pipelines, LLM integrations, and evaluation frameworks — without rewriting your stack or hiring an ML team.

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Clients who trusted us to build for them

AI that ships, built on the stack you already trust

What we do

No Python rewrites. No standalone ML infrastructure. We bring AI capabilities directly into your Rails application — from retrieval-augmented generation to LLM-powered features — using battle-tested patterns that your team can maintain long after we're done.

1–2 wk

Discovery

We map the highest-impact AI opportunities across your codebase and product.

4–8 wk

Build & handoff

Production-ready features shipped with the docs and tests your team owns from day one.

Three ways we add AI to your product

What we build

RAG pipelines

Retrieval-augmented generation for intelligent search, Q&A, and knowledge bases grounded in your own data — not the open internet.

LLM-powered features

Chat, summarization, classification, and content generation woven natively into your Rails app with RubyLLM.

Evaluation frameworks

AI-as-a-Judge and automated test pipelines so quality is measured before and after every deploy — no silent regressions.

FAQ

Have any questions?

Got questions? I'm here for you 24/7, no matter where you are, ready to provide support and answers anytime.

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Do we need to rewrite our app in Python to use AI?

Not at all. We build AI features directly into your existing Rails application using tools like RubyLLM and well-established integration patterns. Your team keeps working in the stack they know, and the AI features we deliver feel native — not bolted on.

What kinds of AI features can you add to our Rails app?

We specialize in LLM integrations (chat, summarization, content generation), retrieval-augmented generation (RAG) for intelligent search and knowledge bases, AI-powered automation, and evaluation frameworks to ensure your AI features perform reliably in production.

How long does a typical engagement take?

It depends on scope, but most projects follow a clear path: a 1–2 week discovery phase to identify the highest-impact AI opportunities, followed by a 4–8 week build phase to deliver production-ready features. We can also work on a retained basis for ongoing AI development.

What happens after the project is done? Can our team maintain it?

Absolutely — that's a core part of how we work. Everything we build uses standard Rails conventions and well-documented patterns. We also provide handoff documentation and optional training sessions so your team feels confident owning the code from day one.

How do you ensure AI features are actually reliable?

We implement AI evaluation frameworks — including AI-as-a-Judge and automated testing pipelines — so you can measure quality before and after every deployment. This means your AI features don't silently degrade over time, and you have real data to back every product decision.

Who you'll work with

Hey, I'm Emilio.

I've been building Rails applications for over a decade — shipping products end to end, from the database to the last pixel. These days I help teams bring AI into the software they already run.

I started Llapi because I kept seeing the same thing: companies sitting on great Rails apps, convinced that adding AI meant a Python rewrite, a new ML team, and months of risk. It doesn't. The tooling has caught up, and most of what teams want — RAG, LLM features, real evaluation — can live right inside the stack they already trust.

When you work with Llapi, you work with me directly. No handoffs to a junior, no account managers. I care about what you're shipping as much as how it's built — and I'll push back when something isn't worth doing.

If that sounds like the kind of partner you're looking for, let's talk. I'd love to hear what you're building.

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Emilio Quintana Founder & Lead Engineer