Everyone sells
automation.
We build
machine learning.

Horizon ML is a dedicated division within PixelHorizon, staffed by engineers whose full-time job is data and machine learning — not web developers "also doing AI on the side." We inherit PixelHorizon's engineering discipline and add real ML depth on top of it.

No overnight rebrand to ride the AI wave. We're the ML & AI engineering division of PixelHorizon, operating under Hub Horizon LLC — headquartered in Sharjah Media City, UAE, with a European office in Warsaw, Poland. Same legal entity, same engineers, same accountability as the studio that has been shipping production software for years.

No. LLMs are one tool in the toolbox, and we use them well when they fit — with proper RAG, evaluation and guardrails. But plenty of problems are better solved with classical ML, forecasting models or computer vision, and we'll tell you when that's the case instead of forcing an LLM into it.

Neither by default — we pick based on what the job needs, not what's trending. When privacy, latency or cost rules out a hosted API, we deploy open-weight models locally — Qwen, Kimi, Llama and others have all shipped in past engagements — benchmarked against your actual task, not assumed. If a hosted model genuinely serves you better, we'll say so instead of forcing a local setup to look impressive.

Yes. We regularly take over existing notebooks and prototypes, add proper evaluation, harden the training pipeline, and turn it into a monitored production service.

Less than most people think — and we'll tell you honestly if it's not enough yet. Discovery starts with an audit of what you already have. Sometimes that's enough for a strong baseline; sometimes the real first deliverable is a data-collection plan that makes a reliable model possible in three months instead of never.

It starts with a scoped discovery phase to validate feasibility against your actual data — usually 1-2 weeks. From there we move in short iterations with visible checkpoints, not a black box that reappears months later.

You find out early, not after go-live. Every engagement is measured against a baseline and a target metric before we call anything done — if the data or the problem can't support the accuracy you need, that surfaces during discovery or evaluation, not as a surprise in production.

Yes. Every deployment includes monitoring for drift and degradation, with clear thresholds for when retraining is needed — either handled by us on an ongoing basis or handed off to your team with full documentation.