Dark data is the information your company stores but rarely uses—think 60% to 80% of files sitting inactive. It clutters storage, drives up costs, and creates noise when AI tries to learn from messy data
After testing AI customer service platforms across regulated industries, I’ve seen automation hit 70% in just one week. But here’s the catch — most tools excel at assisting agents, not fully resolving issues alone
Claude Max offers two pricing plans: the 5x at $100 and the 20x at $200. The big catch? The 5-hour session multiplier works within a rolling five-hour window but doesn't increase your weekly usage caps
Choosing between website audit services and SEO tools can make or break a complex site migration. This article breaks down how hands-on audits give you ticket-ready specs for your sprint planning, while SEO tools provide quick insights
AI customer service tools in 2026 are shifting from just assisting agents to actually resolving issues on their own. In my experience, some platforms hit 70% automation within a week, cutting costs from $3-$6 per ticket down to under $1
Dark data is the information companies collect but never use or classify—often 55% of all stored data. This unused data not only racks up storage costs, sometimes wasting over $2.5 million annually, but also creates risks like security gaps
Claude Max offers two pricing options: $100 for 5x usage and $200 for 20x usage. Here’s the catch—the 5-hour session multiplier boosts your capacity only within a rolling five-hour window but doesn’t increase your weekly cap
Thinking about a site migration? Website audit services dive deep to catch hidden issues like hreflang errors that automated SEO tools might miss. They provide clear, ticket-ready specs your devs can act on during sprint planning
Thinking about whether to rely on website audit services or SEO tools? This article breaks down the difference using a tricky hreflang setup as an example
Dark data is all the info your team collects but rarely uses—estimates say up to 60%-80% sits inactive. It matters because it wastes money, risks security, and clutters AI projects with irrelevant noise