Your generative AI systems,
shipped to production in under six months.

Three commitments that de-risk your AI investment

Our method frames every stage of your project, from initial scoping to a production rollout owned by your teams.

Four families of systems, shipped to production

We select each architecture based on its proof in real environments. Deployed on your infrastructure, monitoring included, skill transfer contracted in.

From scoping to production, in short phases

Each phase ships a measurable deliverable and a written decision. You commit the budget for the next phase once the previous one is validated.

Three IgnitionAI products, three distinct audiences

A RAG platform sold to other consultancies, an Agentic AI course for Node.js, React and Next.js developers, an open-source framework to train reinforcement-learning agents in the browser. Three products, three audiences, one editor.

The code we ship to you, written by our engineers

Our consultants are senior AI engineers. They write the code that will run in production on your infrastructure. Here is a representative excerpt from one of our RAG systems.

import { RagPipeline } from '@ignitionai/rag'

const rag = new RagPipeline({
  store: 'qdrant',        // vector DB hosted on your cloud
  model: 'claude-opus-4',
})

await rag.ingest('./docs')   // your proprietary sources
const answer = await rag.ask('What is our SLA?')

Sourced, tested, deployed on your infrastructure, picked up by your teams at handover.

Production-grade AI, once a month

One in-depth article a month, written by IgnitionAI engineers: architectures that hold up, mistakes paid for in real engagements, hard numbers on cost and performance. Plain-spoken, one-click unsubscribe.

Already available, free: the 12-question scoping checklist · the AI governance white paper.