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 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
Three coordinators, three views of the same corpus
Maria runs Chicago, Devon runs Indianapolis, Aisha runs Milwaukee. Same database, same playbooks — but the search results, the recurring-skill patterns, and the playbook context all reshape to whoever is acting. This is the per-staffer hot-swap index: the relevance gradient is unique to each person, and gets sharper the more they use it.
Chapter 7
The hidden signal — public issuers in your contractor graph
Every contractor in this corpus is also a forward indicator on the public equities they touch. Permit filings precede construction starts by ~45 days, staffing windows by ~30, revenue recognition by months. The associated-ticker network surfaces this signal before any 10-Q. Below: the top issuers attributable to the contractor activity in this view, with live prices.
Computing the Building Activity Index…
Chapter 8
When something breaks — triage in one shot
A coordinator gets a text: "Marcus running late." Watch what the system does in 250 milliseconds: pulls Marcus's record, scores his attendance pattern, finds five same-role same-geo backfills sorted by responsiveness, and pre-writes the SMS to send to the client. This is the moment the AI becomes worth its weight.
Running the triage scenario…
Chapter 9
Try it yourself — every input below hits a different route
Type any staffing question. The router picks the right path: smart-parse (zip code, headcount, role, state), semantic discovery, name lookup, late-worker triage, "what came in last night" temporal queries. Whatever you type, the system tells you what it understood and how it routed.