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What we do

AI development

AI development at Atomatify means putting a language model to work inside real software: an LLM app that answers from your own documents and data, a pipeline that pulls fields out of documents and sorts them, or a feature inside software your team already works in. Most of the work is not the model call. It is deciding what the model is allowed to see, what happens to its output next, and how you find out that a change has made it worse before your users do. One of the products we have built shows this work: the staff console for an automotive marketplace, which includes a trilingual AI assistant.

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Next.jsReactSupabaseCloudflare Workers
Scope

What's included, and what isn't.

Included
  • LLM apps with retrieval over your data: answers drawn from your own documents and records rather than from whatever the model happens to remember.
  • Document extraction and classification: pulling the fields you need out of incoming documents and sorting them by type, so nobody does it by hand.
  • AI features built into the software your team works in, such as an assistant that is part of a staff console, rather than a separate tool your team has to remember to open.
  • LLM integrations, and custom models where the task calls for one.
  • Evaluation harnesses and quality gates, so a change to the prompt, the data, or the model is scored against real cases before it ships.
Not this
  • Training a foundation model from scratch. That is a research lab's budget, not a software team's.
  • AI strategy decks and workshops with nothing built at the end of them.
  • A promise that the model will never be wrong. We build the checks that show how often it is, and what the software does when it is.
Built and running

Products we've shipped that do this.

Automotive marketplace

A car platform and the console that answers every customer

An automotive marketplace for buying, selling, sourcing and importing cars, with a 121-brand catalogue and four lead forms. Behind it we built the staff console: one shared inbox for every DM, a lead queue on a reply clock, inventory, a viewing diary and an AI assistant that works in three languages.

None →100%Web enquiries recordedEvery enquiry from the website is now recorded with a reference; before, none were.
100%DMs in one inboxAll Instagram, Messenger and WhatsApp messages are answered from one shared inbox.
4 → 1Lead forms into one queueFour lead forms (sell, source, import and enquire) feed a single queue with a reply clock.
Shared inboxAI assistantLead CRM
  • Next.js
  • React
  • Supabase
  • Cloudflare Workers
  • Chatwoot
Straight answers

Questions about AI development.

How is this different from your chatbots service? +
A chatbot is one kind of AI feature: a conversational assistant for customers or staff, grounded in a knowledge base, with citations and rules for handing off to a person. That has its own page under chatbots. AI development is the wider discipline: retrieval over your data inside an app, document extraction and classification, and the evaluation behind both. The two overlap where an assistant lives inside a product, as the one in the automotive marketplace's staff console does, and there we scope them as one piece of work.
We built our app with an AI tool. Is this the right service? +
Probably not. That is vibe-code rescue. AI development puts a model inside your software; vibe-code rescue takes software an AI tool wrote and makes it production-grade. If the prototype also calls a model, the two overlap, and we will scope them together.
Can you add AI to a product we already have? +
Yes. Most of that job is deciding what the model can see in your system and where its output goes next, so the feature works from the data and screens your team already uses. One caveat on the proof: in the automotive marketplace on this page, we built both the platform and its staff console, so the assistant shows the pattern rather than a retrofit.
Which model do you use? +
That depends on the task, the data it has to see, and what each call can cost, and it is a conversation for the scoping call. The evaluation harness is what lets us compare models on your cases rather than on someone else's benchmark.
How do you know it is working? +
An evaluation harness: a set of real examples with the answers you expect, scored every time the prompt, the data, or the model changes. Quality gates stop a change that lowers those scores from shipping. Without that, 'it seemed fine when we tried it' is the only test there is.
Start a project

Let's build something you can run your business on.

An automation, a new app, a website, an AI feature or chatbot, or a rescue: start with a call. Tell us the problem; we'll tell you the smartest way to solve it.

What happens on the call Thirty minutes. You describe the problem and the stack. We tell you what we'd build, roughly what it costs, and whether we're the right fit. If we're not, we'll say so.

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