Alexander Alten-Lorenz

Entrepreneur, Developer and Big Data Expert, more than 15 years data experience. Operating at the crossroads of sales and computer science to deliver solutions that people remember.

Introducing DSO-Agent for Grounded, Trustworthy, and Relevant Deep Search

Scalytics introduces DSO-Agent, a groundbreaking framework designed to address key challenges in AI-powered deep search. This blog post explores how DSO-Agent's distributed microservice architecture, iterative verification loops (featuring a "Librarian Check"), and explicit trust scoring mechanisms significantly enhance the factual grounding, trustworthiness, and contextual relevance of AI agents using open LLMs like Mistral and Llama. By engineering for reliability through methodical information retrieval, critical review, and quantifiable confidence, DSO-Agent aims to make AI agents more dependable for complex, knowledge-intensive tasks. The post details the core workflow, including its innovative approach to quantifying trust with the T_score formula, and discusses the impact of this technology on building more reliable AI systems for the future.

Alexander Alten-Lorenz

Benchmarking Contextual AI

Scalytics introduces SynthLink, an open-source framework for benchmarking contextual AI in enterprise settings. Unlike simple Q&A tests, SynthLink’s 60 multi-hop challenges measure an AI’s ability to link evidence, verify claims, and synthesize insights across diverse sources. A transparent five-metric scoring system—covering answer accuracy (F1), retrieval precision (P@5), reasoning quality (RQS), fact-checking (FCS), and iterative efficiency (IE)—yields an aggregate score that reflects research-grade performance.

Alexander Alten-Lorenz

Scalytics Connect - The Runtime for Open Models

Scalytics Connect is the missing runtime for securely operating open-source LLMs in enterprise environments. It solves the core limitations of stateless, blocking models—adding session-aware queuing, enforced system prompts, context window management, and global privacy controls. With full audit logs, admin-configurable behavior, and a token-aware inference router, Connect turns raw model weights into a compliant, reliable AI stack. Built for real-world usage, not just demos.

Alexander Alten-Lorenz

The Future of AI Agents Starts at the Edge

It's only a matter of time before AI becomes a mainstream technology, but to build effective AI systems, we need to rethink how we manage data. Scalytics builds a decentralized approach where data is processed at the edge using AI agents guided by the Agent Context Protocol (ACP). This approach, which uses data in motion and agent-based RAG, makes AI systems faster, more responsive, and more secure. It also challenges the traditional centralized data lake model.

Alexander Alten-Lorenz

Become a FinTech and Get DORA Ready With Scalytics

DORA is here to reshape data security in the financial sector. Traditional practices are no longer sufficient. With stricter regulations on the horizon, IT leaders must prepare for a new era of digital resilience. Adopt solutions like Scalytics to not just comply, but thrive in this evolving landscape. Secure your data, protect your customers, and ensure a resilient future for your financial institution.

Alexander Alten-Lorenz

Understanding LLMs and Why Federated Learning Enables AI for Everyone

Artificial Intelligence (AI) is reshaping competitive landscapes globally. Central to this transformation are Large Language Models (LLMs) and Federated Learning—technologies redefining data utilization in businesses. At Scalytics, we empower organizations to harness these innovations, ensuring they not only keep pace with digital evolution but set the trends. For enterprises committed to maintaining their competitive edge, the adoption of Federated Learning to enable AI is crucial. Federated learning offers unmatched benefits in operational efficiency, compliance, and innovation.

Alexander Alten-Lorenz

Building the Modern AI Stack: Federated AI & Decision Intelligence

How a shift-left strategy can dramatically improve your data architecture's latency, cost, and consistency metrics by moving data preparation and cleansing work from its usual place in the analytical layer to the operational layer.

Alexander Alten-Lorenz

How Mature is Your Data? Assess Your Business' Data Health Now

Understand data maturity's importance for modern businesses. Discover why your business might lag and how to advance with data handling and analytics. This post explores the concept of data maturity and its crucial role in today's data-driven business landscape. We'll delve into the key areas businesses need to focus on, the reasons some fall behind, and how to bridge the gap.

Alexander Alten-Lorenz

Data Federation vs. Data Centralization

Organizations deal with huge data sets from multiple sources. Often data consolidation is used to consolidate data using ETL tools into large single data lakes. The downside of this approach is high costs and high complexity, which leads to complex data integration processes. New cloud applications often introduce isolated methods, tied to closed cloud services. Data federations, including in situ data processing, provide a solution to address data access issues and reduce complexity of data integration.

Alexander Alten-Lorenz

In-situ Federated Data Processing: Empowering LLMs and AI with Privacy, Efficiency, and Sustainability

In the era of big data and AI, privacy is a critical concern. Traditional machine learning approaches often require centralized data storage and analysis, raising concerns about the potential misuse of sensitive user information. Federated learning enables collaborative machine learning without compromising user privacy.

Alexander Alten-Lorenz

Why Software Engineers and Data Architects Should Know Federated Data Management

Federated data processing stores & analyzes data across diverse sources, no movement needed. Virtual data lakes provide a unified view, empowering software engineers & data architects for analytics & AI.

Alexander Alten-Lorenz

Democratizing Data - What It Means, Why It Matters, and How to Achieve It

Data democratization is crucial for modern organizations, enabling accessible and understandable data through tools and technologies.

Alexander Alten-Lorenz

Data Silos are Killing Your AI Performance

Data silos hinder advanced analytics and AI, as algorithms require large amounts of data for accurate predictions and learning.

Alexander Alten-Lorenz

Federated Data Lakes Revolutionizing LLM, AI, ML and Analytics

ML and AI improve analytics efficiency in businesses through Scalytics, the federated data lakehouse.

Alexander Alten-Lorenz

Tackling Data Management Head-On: The Story Behind Scalytics

Scalytics Connect solves current ETL, data processing and data privacy issues coming along with data lakes and data centralization. We have built a industry grade federated data processing solution that simplifies today's and tomorrow's complex data analytics and AI developments.

Alexander Alten-Lorenz

Scalytics recognized as "Best of Value" and "Best of Use" at Gartner Digital Markets

Our Virtual Data Lakehouse, Scalytics Connect, has been recognized as one of the best tools in the Data Integration Software category 2022.

Alexander Alten-Lorenz

Federated data processing is necessary to avoid bias in Generative AI

A federated data approach is essential for reducing bias and discrimination in AI across organizations as generative AI usage grows.

Alexander Alten-Lorenz

Big Data Analytics: The Future of Business Intelligence

Big data analytics with Blossom Sky revolutionizes business decision-making, offers 360-degree view, and drives data-driven models.

Alexander Alten-Lorenz

Generative AI, federated learning and digital twins

Create more accurate digital twin models. Digital twin models can also be trained using federated learning by combining data from different sensors and systems.

Alexander Alten-Lorenz

What is an AI Fabric and Why It’s the Future of Scalable, Data-Driven Organizations

AI Fabric is the next step in data management and artificial intelligence, designed to help businesses scale AI-driven processes without the need for centralized data lakes. By decentralizing data architecture, AI Fabric enables seamless data flow, ensuring real-time insights while maintaining data privacy and compliance. This approach modernizes legacy systems and accelerates AI adoption, making it ideal for organizations seeking scalable, flexible AI solutions that avoid data silos.

Alexander Alten-Lorenz

Why is federated learning so crucial?

Federated Learning (FL) is revolutionizing distributed computing frameworks by enabling decentralized data processing and enhancing privacy. Early adopters like Google, Apple, and NASA use FL to improve products, increase operational efficiency, and maintain data privacy. This post delves into FL's role in the real-world adoption of distributed frameworks, its commercial applications, and the future impact on AI and data-driven industries.

Alexander Alten-Lorenz

How can federated learning boost your organization?

Federated Learning can be used to train models on device data, such as sensor readings, in order to perform tasks like image recognition and anomaly detection.

Alexander Alten-Lorenz

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