Inspect · scoring run
Polaris Public Sector · 6b830ff1
Account
Polaris Public Sector
GovTech · tier Priority
Overall
83
Prompt
score-account-industries · v1
You are scoring an industries account (financial services, healthcare, retail, manufacturing) against the Anthropic field rubric. Score each 0-100 with a one-line cited rationale: 1. Knowledge-Worker Density — share of workforce doing reading, writing, analysis (law firms high, manufacturing lower) 2. Public AI Commitment — measured via open roles, exec statements, press releases 3. Automatable Workflow Volume — volume of repetitive knowledge work Claude could compress 4. Transformation Readiness — data, tooling, change-management maturity to deploy 5. Sector AI Velocity — how fast peers in the sector are moving — competitive pressure Same JSON shape as the tech rubric. Confidence "high" only with three live-evidence dimensions.
Raw response
exactly what Claude returned
{
"tier": "Priority",
"notes": "",
"company": "<see accounts.name>",
"overall": 83,
"segment": "industries",
"why_now": {
"date": "2026-06-14",
"type": "regulatory",
"label": "Regulator update forces process redesign",
"detail": "Captured from public sources during nightly pass.",
"source_url": "https://example.com/whynow/ac8c076c",
"source_type": "investors"
},
"industry": "",
"use_case": "",
"comparable": "",
"confidence": "high",
"dimensions": [
{
"key": "kw_density",
"name": "Knowledge-Worker Density",
"score": 86,
"rationale": "Analyst-dominant org chart",
"evidence_refs": [
2
]
},
{
"key": "public_ai_commitment",
"name": "Public AI Commitment",
"score": 89,
"rationale": "Press release on AI program",
"evidence_refs": [
2
]
},
{
"key": "workflow_volume",
"name": "Automatable Workflow Volume",
"score": 80,
"rationale": "Backlog of structured drafting work",
"evidence_refs": [
3
]
},
{
"key": "transformation_readiness",
"name": "Transformation Readiness",
"score": 90,
"rationale": "Recent platform consolidation",
"evidence_refs": [
3
]
},
{
"key": "sector_velocity",
"name": "Sector AI Velocity",
"score": 83,
"rationale": "Peers shipping AI features fast",
"evidence_refs": [
3
]
}
]
}Parse · five dimensions
Reading/writing heavy workforce
Earnings-call AI mentions trending
Manual triage compressible
Data + governance maturity present
Investor questions on AI per call
Evidence pack · 3
Post details internal agent pilot, evaluation harness, and rollout cadence.
fetched 6/9/2026 · dims: public_ai_commitment, workflow_volume
Company announced a multi-year AI transformation program with executive sponsorship.
fetched 6/6/2026 · dims: workflow_volume, sector_velocity
Open roles include Senior ML, Forward Deployed Engineer, AI PM. Hiring across NA + EMEA.
fetched 5/31/2026 · dims: sector_velocity, transformation_readiness