Project Data Management is Messy If you’re a project manager, you’d know what I’m talking about. Your team spends hours each week wrestling with spreadsheets. Different teams keep their own database or workbook. You end up chasing down versions, cleaning up duplicates, and trying to piece together a coherent picture. By the time you finally reconcile all that data, you’re on the clock again to compile KPIs and projections for your monthly executive report. According to an interview with MACE in the UK, contractors devote up to 50% of their resources to litigation and administration—much of it capturing, organizing, and verifying data throughout a project’s lifecycle. In my own experience, our engineering team spends up to 15% of their time on data entry and report writing that, let’s face it, few ever read. All this manual drudgery not only devours the bulk of your team’s time and budget, it also undermines data quality. Gartner estimates that poor data alone costs industries over $14 million per year on average. Here comes LLM We believe that, a couple of years from now, AI and LLM will eliminate manual data entry, data query and data administration for most of us. This is how