Applied research from delivery, staffing, and AI engagements.
Specific, technical write-ups on problems we've actually worked through: how they showed up, what we did about them, and what it cost.
The AI strategy problem nobody has a playbook for: shared infrastructure, no single owner
Most AI retail case studies assume one company controls the data and the systems. That breaks the moment you're designing for a market, a coalition, or any group of independent businesses sharing one customer base.
StaffingTalent shortages aren't disappearing: they're moving
Aggregate tech unemployment looks soft, but the shortage never went away, it just relocated to a narrower band of skills. What that means for how you source.
StaffingHiring for skills, not job titles
Job titles compress badly across companies. Skills-based screening fixes a real matching problem, if it's done rigorously, not as a slogan.
StaffingAI won't remove the need for recruiters: it'll change what they're doing
AI tooling is genuinely useful for sourcing and screening volume. It doesn't replace the judgment calls that determine whether a placement actually works.
AI & DataDon't measure AI-assisted engineering with lines of code
AI coding tools make output volume easy to inflate and easy to measure, which is exactly why it's the wrong metric to manage a team against.
AI & DataTwo failure modes in uncertainty-aware ML that don't show up in your headline number
A data-splitting bug that quietly voids a statistical guarantee, and a coverage gap invisible in the average. Both are checkable in an afternoon.