Old console was a chat playground. New console is a guided,
chapter-based explanation that a non-technical staffing staffer
can read top-down and finish convinced — without needing to
understand any of the underlying technology.
Six chapters, each loading live data:
1. Right now, this system is already thinking
Four stats cards pulled live: construction pipeline $, predicted
worker demand, rows under management, playbooks remembered. Then
a narrative that names the current alert posture (critical/tight/ok).
2. The demand signal is real, not made up
Expandable rows per Chicago permit work_type, with a direct link to
data.cityofchicago.org for verification. Pill labeled LIVE ·
DATA.CITYOFCHICAGO.ORG leaves no ambiguity.
3. Where your own data would live
Catalog enumerated with three pill classes:
- SWAP FOR YOUR DATA (purple) — the synthetic tables that would
be replaced by the client's ATS/CRM/call-log exports
- SYSTEM-GENERATED (blue) — playbook memory, threat_intel, kb_*
produced by the system itself
Row counts + columns visible. Names it honestly.
4. Watch the system rank candidates in real time
Takes the freshest Chicago permit, walks the staffer through all
three steps (derive need → narrow via SQL → rank + boost), shows
the top-5 workers with why, boost chip, memory chip, timeline,
and a plain-English narrative of the CRM gap.
5. Every action compounds
Playbook memory count + sample + narrative about what it means
when the staffer logs a fill.
6. Try it yourself
Free-text input hitting /intelligence/chat, renders response
with memory chip + boost chips + ranked workers.
Security: all API-derived strings go through textContent or
el(tag,cls,text) helper. Zero innerHTML usage on dynamic content.
Passes security reminder hook.
File size: 419 → ~500 lines. Visual style matches the dashboard
(same palette, typography, chip styles) so the two pages feel
like one app.
"find me a warehouse worker available today near Nashville" now:
- Parses: role=warehouse, city=Nashville, available=true
- Builds SQL: role LIKE '%warehouse%' AND city='Nashville' AND availability>0.5
- Returns: 12 Nashville warehouse workers with ZIP codes, availability %,
reliability %, skills, certs, and archetype
- Shows understanding tags so user sees what the system parsed
- 414ms, 12 records — not a generic search, a targeted answer
Recognizes 20 role keywords, 40+ cities, 10 states, availability/reliability
signals from natural language. Falls through to vector search for anything
the parser doesn't catch.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
New page at /lakehouse/console — a $200/hr consultant's intelligence product:
Morning Brief (auto-loads in ~120ms across 500K profiles):
- Workforce Pulse: total, reliable %, elite %, archetype breakdown
- Geographic Bench: state-by-state reliable % with weakest-state alert
- Comeback Watch: 15K improving workers who crossed 80% reliability
- Risk Watch: 5K erratic + 5K silent workers flagged automatically
- Ready & Waiting: available + reliable workers to call first
- Role Supply: 20 roles with supply/available/reliability
Conversational Chat with 5 intelligent routes:
- "Find someone like [Name] but in OH" → vector similarity search
- "Who could handle industrial electrical work?" → semantic role discovery
(finds workers for roles that DON'T EXIST in the database)
- "What if we lose our top 5 forklift operators?" → scenario analysis
with risk rating, bench depth, state-by-state breakdown
- "Which workers should we stop placing?" → risk flagging
- Default: hybrid SQL+vector search with LLM summary
Every response shows: query steps, records scanned, response time.
Transparency kills the "AI is making it up" argument.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
