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What we think, test and learn while building software, automation and AI for real businesses.

Build Fast, Understand First: What Finny Taught Us
Jul 19, 2026Strategy

Build Fast, Understand First: What Finny Taught Us

Building fast is not the problem. Building on a shallow understanding is. Deeply understanding the problem prevents costly rewrites, while rapid iteration with real users reveals the right product. Success comes from combining both.

Custom Software Without Starting From Scratch
Jul 19, 2026Custom Software

Custom Software Without Starting From Scratch

Custom software is not a choice between buying or building from scratch. The most effective approach is to extend a strong existing product, customizing only what is unique while leveraging proven functionality for everything else.

Getting Excel Out of Your Critical Processes
Jul 7, 2026Automation

Getting Excel Out of Your Critical Processes

Excel is a powerful tool, but a poor system of record. When planning, pricing, or inventory depend on spreadsheets, errors go unnoticed, knowledge becomes concentrated in one person, and each quick fix increases long-term complexity. There is a practical way out.

Why Your Knowledge Assistant Needs Hybrid Search
Jun 23, 2026Custom Software

Why Your Knowledge Assistant Needs Hybrid Search

Knowledge assistant demos look impressive, but fail in real use where queries rely on exact codes and identifiers. Pure semantic search guesses instead of matching precisely. A hybrid approach combining keyword and semantic search is essential for reliability and trust.

Quick Wins, Not Big Bangs: How SMEs Should Start With AI
Jun 2, 2026Strategy

Quick Wins, Not Big Bangs: How SMEs Should Start With AI

Big-bang AI transformations fail due to scope and adoption, while isolated experiments fail due to weak data foundations. The effective approach combines small, high-impact quick wins with a pragmatic architecture, delivered and validated in phases.

Your AI Project Is a Data Problem First
May 12, 2026AI & Data

Your AI Project Is a Data Problem First

Most AI pilots fail not because of the technology, but because of fragmented and untrusted data. Defining a single source of truth per data type and adding a governed data layer enables reliable, scalable AI without needing full system consolidation.

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