The easy.bi Blog

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How a German Manufacturer Cut Development Costs 40% by Switching Partners
13 min read
Custom Solutions

How a German Manufacturer Cut Development Costs 40% by Switching Partners

Anonymized case study: a DACH manufacturer (300 employees) switched from a large consultancy to a mid-size partner, cutting development costs 40% and delivering 3x faster in 14-day sprints.

Andrej Lovsin
The 7 Reasons Custom Software Projects Go Over Budget
13 min read
Custom Solutions

The 7 Reasons Custom Software Projects Go Over Budget

Data-driven analysis of 7 reasons custom software projects blow budgets. Root causes, real cost impact, and prevention strategies for each.

Filip Kralj
What Does Custom Software Development Actually Cost in 2026?
12 min read
Custom Solutions

What Does Custom Software Development Actually Cost in 2026?

Real cost breakdown for custom software in 2026. DACH vs. Eastern Europe vs. offshore rates, 5-year TCO, and EUR pricing by project type.

Christian Kaspar
Fixed-Price vs. Time-and-Materials: Which Model Will Not Blow Up
13 min read
Custom Solutions

Fixed-Price vs. Time-and-Materials: Which Model Will Not Blow Up

Honest comparison of fixed-price and T&M contracts for software. When each fails, hybrid models, and how to protect budgets with real data.

Christian Kaspar
Microservices vs. Monolith: When to Split (And When Splitting Kills You)
13 min read
Custom Solutions

Microservices vs. Monolith: When to Split (And When Splitting Kills You)

Practical decision framework for microservices vs. monolith. Team size thresholds, cost data, and the modular monolith middle ground for DACH mid-market.

Filip Kralj
AI Strategy for Mid-Market: You Do Not Need a Data Science Team
13 min read
AI & Innovation

AI Strategy for Mid-Market: You Do Not Need a Data Science Team

Mid-market companies (EUR 20M-500M) do not need data scientists to deploy AI. They need AI-literate engineers and the right implementation partner. A practical AI strategy framework with the 3 opportunities every mid-market company has.

Filip Kralj
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AI Data Readiness: The Assessment Your Team Skips (And Pays For Later)
13 min read
AI & Innovation

AI Data Readiness: The Assessment Your Team Skips (And Pays For Later)

A practical AI data readiness assessment framework for mid-market companies. Covers data quality checklists, infrastructure requirements, governance gaps, and the minimum viable data you actually need versus what vendors tell you.

Filip Kralj
From GPT Wrapper to Production AI: The Engineering Gap Nobody Talks About
14 min read
AI & Innovation

From GPT Wrapper to Production AI: The Engineering Gap Nobody Talks About

The engineering gap between a working ChatGPT demo and production-grade AI. Covers latency, error handling, hallucination management, cost optimization, observability, and security for enterprise LLM deployments.

Filip Kralj
The 5 AI Use Cases That Actually Pay for Themselves in Year 1
13 min read
AI & Innovation

The 5 AI Use Cases That Actually Pay for Themselves in Year 1

5 AI use cases with proven first-year ROI for mid-market enterprises. Real cost vs. savings data for document processing, predictive maintenance, customer service automation, demand forecasting, and code generation.

Filip Kralj
Why 85% of AI Projects Fail Before Production
12 min read
AI & Innovation

Why 85% of AI Projects Fail Before Production

Data-driven analysis of why 85% of AI projects fail before production. Covers organizational, data, process, and partner-selection causes with a diagnostic checklist for mid-market CTOs in the DACH region.

Filip Kralj
What Does an AI Pilot Actually Cost? (Real Numbers, Not Guesses)
11 min read
AI & Innovation

What Does an AI Pilot Actually Cost? (Real Numbers, Not Guesses)

Real cost breakdowns for AI pilots by type: chatbots (EUR 20K-55K), process automation (EUR 30K-80K), and predictive analytics (EUR 40K-120K). Includes hidden costs most budgets miss and a complete budget template.

Filip Kralj
RAG vs. Fine-Tuning vs. Prompt Engineering: When to Use What
13 min read
AI & Innovation

RAG vs. Fine-Tuning vs. Prompt Engineering: When to Use What

A practical decision framework for choosing between RAG, fine-tuning, and prompt engineering. Includes cost breakdowns, accuracy benchmarks, data requirements, and implementation timelines for mid-market CTOs.

Filip Kralj