It From Bit · Research
The research record.
Our work is grounded in published research. Selected peer-reviewed contributions from our practice areas.
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Responsible and Ethical AI: The Strategic Differentiator for Premium Brands
How premium brands can implement responsible AI governance to foster authenticity, align AI systems with core values, and gain a measurable competitive advantage. Covers regulatory compliance, stakeholder trust, human-centric AI design, and practical implementation strategies.
Peer-reviewed publications
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Comparative Analysis of Modern Machine Learning Models for Retail Sales Forecasting
A triage framework for model selection in retail demand forecasting under real-world conditions: intermittent demand, missing data, and production constraints. Across major retail datasets, tree-based ensemble methods (XGBoost) consistently outperform deep learning architectures — a finding with direct implications for how organizations choose forecasting infrastructure. Co-authored with Babson College and mStart (Fortenova Group).
arXiv preprint → -
AI-Assisted Unit Test Writing and Test-Driven Code Refactoring: A Case Study
Documents a client engagement in which AI coding models generated approximately 16,000 lines of unit tests in hours rather than weeks, enabling safe large-scale refactoring with up to 78% branch coverage. A practical template for organizations managing legacy codebase risk with AI tooling.
arXiv preprint → -
Policy-Bound Triple-Entry Receipts for Autonomous Commerce
Proposes an accounting control architecture for AI-mediated transactions where execution speed outpaces human governance. The Policy-Bound Triple-Entry (PBTE) method uses an Accounting State Machine to gate transaction recognition against pinned policies — enabling event-time governance across ERP systems. Foundational work for agentic commerce infrastructure.
Read on ResearchGate → -
Delivery pattern planning in retailing with transport and warehouse workload balancing
Documents the methodology and results of the supply chain optimization engagement with Konzum — Croatia's largest retail chain — during the COVID-19 pandemic. Presents a discrete optimization model for weekly delivery pattern planning that balances warehouse and transportation utilization while maintaining service levels for fresh food across hundreds of stores in the CEE region.
Read on HRČAK →
See these methods applied in practice → Case Studies