We design mathematically grounded solutions, turn complex data into clear decisions, and deliver production-grade AI and visualization systems—end to end.
Revival27 is a consulting-driven engineering team specializing in mathematical applications, data visualization, and the practical use of AI. We combine rigorous theory with hands-on delivery, building custom software that is reliable, explainable, and fit for real operations.
Our work is defined by clarity, correctness, and accountability. We put strong emphasis on solid theoretical foundations, because that is what makes systems robust, scalable, and defensible—especially in regulated or high-impact domains.
We map the real decision problem before writing code
Not "AI for AI's sake"—rigorous foundations for every solution
Maintainable, testable, production-ready delivery
Experimentation is built into our process
We actively collaborate with the University of Szeged (SZTE) and maintain a talent pathway: several colleagues began with us during their professional internship and later joined the team full-time.
This helps us stay close to research-grade thinking while delivering industry-grade systems.
We designed and implemented the mathematical logic, built the AI pipeline, and delivered the core algorithmic layer powering the system's intelligence and decision rules.
View HeimdallAn agriculture-focused platform centered on data visualization, sensor integration, and clear presentation of time-series data for operational decision-making.
View Soil4NatureWe built and operate an on-prem NVIDIA GPU compute setup for video generation and deep learning workloads — detection, segmentation, classification — deployable as internal R&D or external project compute.
See the infrastructureClarify decisions, constraints, and success criteria
Structure data, define math/AI approach, validate feasibility
Implement pipelines, algorithms, dashboards, and systems
Production rollout, monitoring, iteration
We are a consulting-minded engineering team with a practical bias: deliver value, keep it correct, and make it maintainable. We enjoy challenging problems, experimental development, and solutions that stand up to real usage.
Mihály
Anikó
Viola
Veronika
Máté
Szabolcs
If you need a mathematically sound, clearly visualized, AI-ready solution—tell us what decision you need to make, and we will design the system around it.