Chapter 1
Right now, this system is already thinking
Before you touched anything, it pulled real Chicago building-permit data, measured demand, checked your bench, and began flagging roles that need attention. This isn't theoretical — open your browser network tab and watch the fetches land.
Chapter 2
The demand signal is real, not made up
Chicago's Department of Buildings publishes every permit they issue. Below are the largest categories of construction filed in the last 30 days. If a staffer doesn't believe our numbers, they can verify at
data.cityofchicago.org.
Chapter 3
Where your own data would live
The system stores data in labeled catalogs. Purple pills = synthetic stand-ins you'd swap for your real ATS/CRM/call-log exports. Blue pills = data the system generates about itself (playbooks, audit trails). Nothing else in the pipeline changes — only the source.
The swap path. workers_500k → your ATS export (same schema shape). candidates → your CRM. call_log → your phone system's CDR. timesheets → your payroll export. Once ingested, every behavior you see on the dashboard applies to your real data. No re-training. No replatform.
Chapter 4
Watch the system rank candidates in real time
This takes the most recent Chicago permit, derives the staffing need, pulls ranked candidates from the 500K bench, and shows you why each one ranked. Everything below loaded in about 3 seconds against the live system.
Chapter 5
Every action compounds — the CRM-killer
A CRM stores. This system compounds. Every successful fill, every no-show, every phone call becomes a re-ranking signal on the next query. Below is the live playbook memory state. The number grows as the app gets used.
Chapter 6
Try it yourself
Type any staffing question. The system picks the right search path (smart-parse, semantic discovery, analytics), shows what it understood, and returns ranked results with memory signal.