Cited, data-backed analysis on growth, software spend, and technology decisions, written for the people who approve the budget.
When the core system can’t keep up but the business can’t pause, a full rewrite is the riskiest option. How incremental modernization works, and how to judge a plan before you fund it.
Read the analysis →Before funding a rebuild, a new product, or an AI program, owners and operating leaders need an honest read on what they have and what it will take. What a useful technical assessment answers, and when fractional leadership makes more sense than a full-time hire.
Read the analysis →Depending on how you measure it, between 42% and 95% of enterprise AI efforts stall, get abandoned, or show no financial return. What the research says, and what separates the minority that ship.
Read the analysis →AI speeds up writing code. Early measurements suggest it can also add defects and rework. What the evidence shows, what it can’t yet prove, and how to protect your codebase.
Read the analysis →Overlapping subscriptions cost more than the invoices. The hidden costs of a patchwork of tools, how to audit your own, and when consolidating into one system makes sense.
Read the analysis →The biggest AI model is rarely the right default. How to test whether a smaller, cheaper model is good enough for a given job, before you commit to a bill that scales with usage.
Read the analysis →Book a 30-minute consultation. You’ll talk with someone who builds these systems, not a salesperson, and leave with an honest read on the problem, your options, and whether we’re the right fit.