Runs

Inspect · scoring run

Maris Manufacturing · 0f209192

model · google/gemini-2.5-prosegment · industriesconfidence · lowevidence · 3latency · 2.44 sat · 6/15/2026, 9:00:00 AM

Account

Maris Manufacturing

Manufacturing · tier Watch

Overall

48

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": "Watch",
  "notes": "",
  "company": "<see accounts.name>",
  "overall": 58,
  "segment": "industries",
  "why_now": {
    "date": "2026-06-11",
    "type": "earnings_call",
    "label": "CEO emphasized agent-led ops on earnings",
    "detail": "Captured from public sources during nightly pass.",
    "source_url": "https://example.com/whynow/697ad196",
    "source_type": "investors"
  },
  "industry": "",
  "use_case": "",
  "comparable": "",
  "confidence": "low",
  "dimensions": [
    {
      "key": "kw_density",
      "name": "Knowledge-Worker Density",
      "score": 50,
      "rationale": "Reading/writing heavy workforce",
      "evidence_refs": [
        3
      ]
    },
    {
      "key": "public_ai_commitment",
      "name": "Public AI Commitment",
      "score": 54,
      "rationale": "AI roles open + exec quotes",
      "evidence_refs": [
        3
      ]
    },
    {
      "key": "workflow_volume",
      "name": "Automatable Workflow Volume",
      "score": 61,
      "rationale": "High-volume repeatable knowledge tasks",
      "evidence_refs": [
        2
      ]
    },
    {
      "key": "transformation_readiness",
      "name": "Transformation Readiness",
      "score": 62,
      "rationale": "Active change-mgmt practice",
      "evidence_refs": [
        1
      ]
    },
    {
      "key": "sector_velocity",
      "name": "Sector AI Velocity",
      "score": 60,
      "rationale": "Competitive pressure rising",
      "evidence_refs": [
        2
      ]
    }
  ]
}

Parse · five dimensions

Knowledge-Worker Density
42

Most staff on shop floor — low KW density

Public AI Commitment
38

Limited public AI signal

Automatable Workflow Volume
52

Adequate digital maturity

Transformation Readiness
58

Back-office QA whitespace

Sector AI Velocity
50

Manufacturing AI velocity uneven

Evidence pack · 3

  • Company announced a multi-year AI transformation program with executive sponsorship.

    fetched 6/16/2026 · dims: sector_velocity, public_ai_commitment

  • Post details internal agent pilot, evaluation harness, and rollout cadence.

    fetched 6/12/2026 · dims: public_ai_commitment, workflow_volume

  • Open roles include Senior ML, Forward Deployed Engineer, AI PM. Hiring across NA + EMEA.

    fetched 6/10/2026 · dims: sector_velocity, transformation_readiness

Re-run scoring for Maris Manufacturing