Runs

Inspect · scoring run

Mirador Architecture · 284f114c

model · google/gemini-2.5-prosegment · industriesconfidence · mediumevidence · 3latency · 3.62 sat · 6/16/2026, 3:00:00 AM

Account

Mirador Architecture

AEC · tier Build

Overall

70

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": "Build",
  "notes": "",
  "company": "<see accounts.name>",
  "overall": 70,
  "segment": "industries",
  "why_now": {
    "date": "2026-06-09",
    "type": "product_launch",
    "label": "Shipped agent-powered workflow",
    "detail": "Captured from public sources during nightly pass.",
    "source_url": "https://example.com/whynow/2baf72b4",
    "source_type": "investors"
  },
  "industry": "",
  "use_case": "",
  "comparable": "",
  "confidence": "medium",
  "dimensions": [
    {
      "key": "kw_density",
      "name": "Knowledge-Worker Density",
      "score": 65,
      "rationale": "Reading/writing heavy workforce",
      "evidence_refs": [
        3
      ]
    },
    {
      "key": "public_ai_commitment",
      "name": "Public AI Commitment",
      "score": 74,
      "rationale": "Earnings-call AI mentions trending",
      "evidence_refs": [
        1
      ]
    },
    {
      "key": "workflow_volume",
      "name": "Automatable Workflow Volume",
      "score": 69,
      "rationale": "High-volume repeatable knowledge tasks",
      "evidence_refs": [
        1
      ]
    },
    {
      "key": "transformation_readiness",
      "name": "Transformation Readiness",
      "score": 71,
      "rationale": "Recent platform consolidation",
      "evidence_refs": [
        3
      ]
    },
    {
      "key": "sector_velocity",
      "name": "Sector AI Velocity",
      "score": 75,
      "rationale": "Investor questions on AI per call",
      "evidence_refs": [
        2
      ]
    }
  ]
}

Parse · five dimensions

Knowledge-Worker Density
76

Analyst-dominant org chart

Public AI Commitment
68

Earnings-call AI mentions trending

Automatable Workflow Volume
77

Manual triage compressible

Transformation Readiness
67

Active change-mgmt practice

Sector AI Velocity
64

Competitive pressure rising

Evidence pack · 3

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

    fetched 6/14/2026 · dims: sector_velocity, public_ai_commitment

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

    fetched 6/4/2026 · dims: transformation_readiness, sector_velocity

  • New executive role created to own AI strategy across business units.

    fetched 5/27/2026 · dims: transformation_readiness, workflow_volume

Re-run scoring for Mirador Architecture