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Machine learning, generative AI, computer vision, and intelligent automation — engineered with rigor and grounded in your business goals.
Most AI projects fail to leave the lab. Ours don't. Founded in November 2025, Cynix Digital brings an engineering-first mindset to AI: clean data pipelines, rigorous evaluation, observability, and a relentless focus on solving the real problem — not chasing the latest hype.
Our founder brings over 24 years of professional experience, having delivered AI and large-scale digital platforms across Asia, Europe, and Africa. At Cynix Digital, every project is led by seasoned practitioners who know how to ship production-grade AI — from LLM-powered knowledge systems to computer vision pipelines.
Classification, regression, recommendation, and forecasting models trained on your data.
Knowledge-grounded chat, retrieval-augmented generation, and LLM fine-tuning for your domain.
Document understanding, image classification, object detection, and visual quality inspection.
AI agents and workflow automation that handle complex tasks end-to-end with human oversight.
Training pipelines, feature stores, model registries, and continuous evaluation infrastructure.
Pragmatic AI roadmaps, model risk assessments, and responsible AI governance frameworks.
We're tech-agnostic but opinionated. Here's the production-grade toolset we lean on for ai development engagements.
We provide custom ML models, LLM integration and fine-tuning, retrieval-augmented generation (RAG), computer vision, intelligent automation, and MLOps infrastructure.
We take an engineering-first approach — clean data pipelines, rigorous evaluation, observability, and a focus on solving real problems rather than chasing hype. Our founder has 24+ years of experience delivering production AI.
Yes. We integrate, fine-tune, and deploy LLMs including OpenAI, Anthropic, and open-source models. We specialize in RAG systems that ground AI in your proprietary knowledge base.
We build responsible AI into every project with bias detection, safety reviews, model risk assessments, and transparent governance frameworks. It's not an afterthought — it's part of our process.
We start with a proof of concept in 2-4 weeks, then iterate into production. Complex systems with custom ML models typically take 3-6 months to full deployment.
Build secure, innovative, and sustainable digital solutions that drive long-term growth.