# Staffing Co-Pilot

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⓪ Not a CRM — an index that learns from you loading growth numbers… ▾ click to collapse

If you've worked on a legacy staffing CRM, your mental model is field inventory — every concept must be a visible column, dropdown, or checkbox, or it doesn't exist. This system works the opposite way: concepts don't need to be pre-declared because the hybrid index + playbook memory learns them when you work a contract. The rows below translate the familiar legacy surface into what actually happens here, with real numbers for every claim.

Analyzing contracts and workers...
① Live Market — Chicago right now Public permit data pulled live · clock aligned to current shift · refreshed every page load

The clock shows where we sit in the 24-hour cycle. Colored arcs mark the 4 standard staffing shifts; the red needle is now. The panel beside it summarizes what Chicago's public permit system is asking for right now — staffing demand before anyone's acted on it. This is the real world the rest of the page is reacting to.

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Staffing Forecast — Next 30 Days Permits → predicted demand · Bench supply · Days to staffing deadline
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② Staffer's Console — what's on your plate Real public permit data + worker bench + past playbook patterns

This is what a recruiter or coordinator sees when they open the console. Each card is one open permit ranked against our 500K worker bench. The fill-probability bar shows cumulative chance of filling by day; the economics panel projects gross revenue, margin, and payout window; the over-bill pool flags workers whose pay exceeds the contract's bill rate — they go into a margin-watch bucket instead of being rejected outright.

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Market Intelligence Public permit data · Updated live
③ Worker Search — find someone specific Natural language search

Type a plain-English description — role, location, trait, certification. The query hits the hybrid SQL + vector index over all 500K worker profiles and ranks by semantic match, reliability, and availability. Try one of the sample searches below or write your own.

④ System Activity — how the substrate learns Playbook memory, pathway traces, self-tuning indices

Every completed fill, every accepted playbook, every rejected candidate feeds back into the substrate. This strip shows what the system has learned since the last run — which patterns are compounding, which memories are fresh, which indices are being exercised. If it's empty, the system hasn't seen enough traffic yet to form a memory worth showing.

⑤ Substrate Signals — architecture health Live probes of the index, memory, and pathway layers

These tiles measure the architecture itself, not the staffing workload. Instant-search latency, index shape, playbook-memory depth, pathway-matrix compounding — four probes that answer "is the substrate healthy right now?"

Probing substrate…