Everyone Wants AI, but Data Orchestration Comes First Enterprise AI conversations often start in the wrong place. They focus on models, talent, or investment levels, as if the remaining challenge were technical sophistication. In practice, many organizations already operate capable AI systems, supported by modern infrastructure and serious budgets. What
Legacy database models can no longer keep up with fintech’s real-time demands. A Log-First Architecture redefines data management — turning transactions into a living, auditable history that fuels agility, transparency, and innovation across financial systems.
This article examines how rinf.tech’s 8-year partnership with Intel has advanced open-source innovation by addressing real-world challenges in cloud, IoT, and AI ecosystems. It highlights joint contributions to improve workload performance, enhance IoT sensor integrations, and optimize computer vision using OpenCV and OpenVINO. Read more into how these contributions deliver measurable value to the global developer community and accelerate enterprise adoption of scalable, reliable technologies.
Everyone Wants AI, but Data Orchestration Comes First Enterprise AI conversations often start in the wrong place. They focus on models, talent, or investment levels, as if the remaining challenge were technical sophistication. In practice, many organizations already operate capable AI systems, supported by modern infrastructure and serious budgets. What
This article explains the technical realities of building AI-ready wearable devices by orchestrating three constraints – power, latency, and trust – through edge/on-device AI, disciplined power budgeting, and secure-by-design updates. It clarifies why many wearables PoCs stall and offers an in-depth investigation into challenges hindering product advancement, finally scanning predictions and emerging trends in the wearables market.
Securing the AIoT Ecosystem explores how cutting‑edge semiconductors bring edge AI, energy efficiency, and hardware‑rooted security to large‑scale device management.
Exploring the benefits and challenges of implementing localized AI models in financial services, highlighting their transformative impact on the industry.
In this article, we explore a current retail technology landscape and trends shaping the industry, retail tech use cases, as well as what the future holds for retail tech.
This article aims to guide companies on why, when, and how to conduct an AI readiness assessment.
Recapping the 2024 State of Automotive Software Development Report, focusing on how AI and ML are reshaping automotive design.
This paper presents an innovative AI-enhanced SaaS solution designed to tackle the complex challenges of resume classification, job description matching, and the creation of customized resumes and cover letters.
Showcasing rinf.tech’s comprehensive approach to data modernization, highlighting strategies that help transform traditional data systems into robust, agile, and future-proof platforms.
From integrating AI and 5G to adopting renewable energy sources, fleet management tech trends promise enhanced operational efficiency and align with broader sustainability and safety goals.
Exploring the strategies and critical considerations for enterprises venturing on digital transformation journeys through application modernization.
An interview with Marius Lupu, rinf.tech’s Programs Director, where he shares his experience and insights on a typical Digital Transformation project’s what, why, and how.