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A We help Rails shops ship production AI features — RAG pipelines, LLM integrations, and evaluation frameworks — without rewriting your stack or hiring an ML team.
Results for every project, with a focus on design and functionality
AI that ships,
AI that ships,
built on the stack you already trust
built on the stack you already trust
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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.
We’ve delivered 50+ projects that help companies generate real results.
Success Stories
We’ve delivered 50+ projects that help companies generate real results.
Working with Llapi AI to add AI search to our Rails app was a game-changer. They delivered a production-ready RAG pipeline that our team could maintain from day one.
Have Any Questions?
Have Any Questions?
Got questions? I'm here for you 24/7, no matter where you are, ready to provide support and answers anytime.
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.
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.
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.
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.
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.
