Practical AI implementation guides, comparisons and the business implications of global developments.
12 articles listed
Which document types pay back first, OCR vs. LLM extraction, architecture next to your ERP, and what it costs — a decision guide built on production numbers.
There is no single threshold: monthly volume, minutes per invoice and rework decide together. A formula you can run with your own numbers.
AI invoice processing sits between the inbox and posting, not in place of your ERP: extraction, matching, approval — no rip-and-replace, no downtime.
RPA carries the deterministic steps: intake, posting, rule-based matching. AI extracts data from varied invoice formats. Production systems use both.
Traditional OCR covers a few stable formats; many suppliers and varying layouts need AI extraction. A decision guide with the hybrid pipeline we run.
Seven criteria and a comparison method for choosing an AI consultant: what to ask, what a good answer sounds like, and the red flags.
Assessment from HUF 500,000 + VAT, AI assistant €15-40K, document processing system €30-80K. Real numbers — and what moves them.
In a 10-50 person company, 2-3 processes consume most of the manual time. Step by step: assessment, pilot, implementation — and when AI is the wrong answer.
IDC found that of 33 AI pilots the average organization launched, only 4 reached production. Ours run in production. Here's what it takes.
Anthropic shipped Opus 4.8: dynamic workflows and 69.2% on SWE-Bench Pro. What matters — and what doesn't — for teams building production AI.
We used to spend weeks writing custom API connectors for every client. Model Context Protocol cut that cost by roughly 60%.
By some estimates more than 80% of AI projects fail, while IDC sees spending passing $630 billion by 2028. Here's what separates failures from wins.