The easy.bi Blog
We build software. We write about building software. That's it.
Why Your MVP Takes 9 Months (And How to Ship in 8 Weeks)
90% of startups fail, with 42% citing no market need. Learn why MVPs take 9 months instead of 8 weeks, and the sprint model that eliminates scope creep, perfectionism, and waterfall disguised as agile.
Andrej Lovsin 14 min read 
How to Run Two Systems at Once: The Parallel-Run Playbook
61% of migration projects exceed planned timelines. This playbook covers the strangler fig pattern, feature flags, data synchronization, and rollback strategies for running legacy and new systems in parallel.
Filip Kralj
How to Write a Software Requirements Document That Developers Actually Use
Traditional SRS documents fail 60% of the time. Learn the user story plus acceptance criteria format that developers actually use, with templates, common mistakes, and collaboration frameworks.
Christian Kaspar
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
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?
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
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 KasparExpert insights, delivered monthly
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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
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
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
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
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
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