Company

Engineering-led by design, built for long-term ownership.

IllumiCore grew out of more than fifteen years of hands-on engineering. We are independent by design: one accountable technical partner, close to the work and clear about what a system will need after launch.

What we care about

Four things we hold to, on every project.

Useful innovation

New tools only matter when they solve a real problem.

We explore modern technology with a builder's mindset: test the value, understand the limits, and turn the useful parts into reliable products. We keep room for experimentation, but experiments do not reach production until they are stable and understandable.

Built carefully

Good software is calm, testable, and maintainable.

Quality starts before the first line of code. We clarify requirements, keep the architecture understandable, and test the parts that carry real business risk. Performance and accessibility are considered from the start, not retrofitted.

Clear work

You should always know what is happening and why.

You get clear decisions, visible progress, and honest feedback when a feature, budget, or timeline needs a better path. When something changes, we explain the impact early so decisions can be made with the right context.

Long-term thinking

A system should still make sense after the first launch.

We design for durability: clean structure, sensible dependencies, efficient hosting, and code that future teams can understand. We prefer solutions that grow with your business instead of forcing a rebuild every time requirements change.

Our journey

Technical roots, independent structure, long-term thinking.

01

The foundation

IllumiCore LTD was founded to make modern technology easier for companies to use, understand, and turn into measurable progress.

02

Technical roots

With more than 15 years in AI research and software development, our work is grounded in hands-on engineering rather than surface-level consulting.

03

Where we are going

We want to help teams turn complex technical choices into focused, useful systems — from AI integration and app development to wider digital strategy.

15+

Years building software

Long enough to have maintained our own earlier decisions — which is where most of the useful lessons came from.

100+

Projects shipped

Across AI, web, commerce and internal tooling. Most of them are still running, which matters more than the count.

50+

Client collaborations

From single-founder companies to teams with their own engineering department, in and beyond the EU.

Technologies

What we build with.

Tools we use regularly and know well enough to explain the trade-offs — not a badge wall. The right choice still depends on the problem, and part of the job is telling you when it is not one of these.

Languages

TypeScript and Python cover most of the work — TypeScript wherever a browser or a team of developers needs to stay in sync, Python wherever data and AI work benefit from its ecosystem. PHP still runs a large share of the web's backends, Swift and Kotlin come in for native mobile work, and SQL remains the most direct way to ask a real question of real data.

  • TypeScript
  • JavaScript
  • Python
  • PHP
  • Swift
  • Kotlin
  • SQL
  • Bash

Frontend & mobile

React and Next.js for interfaces that need real interactivity and a fast first paint, React Native where one codebase should reasonably cover iOS and Android. Where a project does not need a framework at all, we reach for plain Web Components and CSS instead of adding one — fewer dependencies for the next team to carry.

  • React
  • Next.js
  • React Native
  • Web Components
  • Tailwind CSS
  • CSS

Backend & data

Node.js and FastAPI for APIs and background work, PostgreSQL as the default database unless a project gives us a specific reason to choose otherwise, and Redis where something needs to be fast rather than durable. REST covers most integrations; GraphQL comes in when a frontend genuinely needs to shape its own queries.

  • Node.js
  • FastAPI
  • PostgreSQL
  • Redis
  • REST APIs
  • GraphQL

AI & machine learning

PyTorch and the Transformers ecosystem for models that need training or fine-tuning, vector search for anything that has to be found by meaning rather than by keyword, and retrieval-augmented pipelines where an answer needs to be grounded in your own documents, not just a model's training data. MLflow keeps experiments and versions traceable once more than one model is in play.

  • PyTorch
  • Transformers
  • Vector search
  • RAG pipelines
  • MLflow
  • OpenAI & Anthropic APIs

Platform & DevOps

Docker so a project runs the same way on a laptop as it does in production, continuous integration so changes are tested before they ship, and Linux underneath almost everything we deploy. Cloud hosting is chosen per project — AWS, GCP or a smaller European provider — rather than defaulting to one out of habit, with monitoring wired in from the start rather than added after the first incident.

  • Docker
  • CI/CD
  • Linux
  • AWS
  • Google Cloud
  • Monitoring & logging

Design

Figma for interface and system work, design tokens so a colour or spacing change updates everywhere at once, and Storybook so components are reviewed in isolation before they reach a real screen. Every palette is contrast-checked rather than approved on looks alone.

  • Figma
  • Design tokens
  • Storybook
  • Prototyping
  • Accessibility audits

Commerce & marketing

Shopify and WooCommerce for storefronts, Stripe and PayPal for checkout, and Search Console and analytics for the work that happens after launch — because a shop or a site that nobody finds still has not done its job.

  • Shopify
  • WooCommerce
  • Stripe
  • PayPal
  • Search Console
  • Analytics

Next step

Want to know whether we are a good fit?

Describe what you are working on. We will tell you plainly whether we are the right team for it.