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Pellet Robotics · e49f131d

model · google/gemini-2.5-prosegment · techconfidence · mediumevidence · 3latency · 3.58 sat · 6/15/2026, 11:00:00 PM

Account

Pellet Robotics

Industrial AI · tier Build

Overall

72

Prompt

score-account-tech · v1

You are scoring a mid-market technology account against the Anthropic field rubric.

Score each of the five dimensions 0-100 with a one-line rationale that cites the evidence URL it relied on:
1. Agent Opportunity — surface area for agentic workflows (support, ops, research, code)
2. Internal Transformation — evidence the org is actively rewiring around AI internally
3. AI Commitment — leadership posture, AI hiring velocity, product bets
4. Whitespace vs. Existing Spend — room to grow beyond current Anthropic or LLM usage
5. Industry Fit & Velocity — sector alignment with Anthropic ICP and rate of change

Return JSON: { overall:int, tier:"Priority"|"Build"|"Watch"|"Park", confidence:"high"|"med"|"low", dimensions:[{key,name,score,rationale,evidence_url}], use_case:string, comparable:string, talk_track:string }.

Tier thresholds: 80+ Priority, 62-79 Build, 46-61 Watch, <46 Park. Confidence is "high" only when at least three dimensions cite live public evidence.

Raw response

exactly what Claude returned

{
  "tier": "Build",
  "notes": "",
  "company": "<see accounts.name>",
  "overall": 72,
  "segment": "tech",
  "why_now": {
    "date": "2026-06-14",
    "type": "product_launch",
    "label": "Shipped agent-powered workflow",
    "detail": "Captured from public sources during nightly pass.",
    "source_url": "https://example.com/whynow/104ca584",
    "source_type": "linkedin"
  },
  "industry": "",
  "use_case": "",
  "comparable": "",
  "confidence": "medium",
  "dimensions": [
    {
      "key": "agent_opp",
      "name": "Agent Opportunity",
      "score": 64,
      "rationale": "Repetitive triage at scale",
      "evidence_refs": [
        2
      ]
    },
    {
      "key": "ai_commit",
      "name": "AI Commitment",
      "score": 69,
      "rationale": "Public CEO statement on Claude/agents",
      "evidence_refs": [
        1
      ]
    },
    {
      "key": "transform_ready",
      "name": "Transformation Readiness",
      "score": 75,
      "rationale": "Re-platformed core within 18 months",
      "evidence_refs": [
        2
      ]
    },
    {
      "key": "whitespace",
      "name": "Whitespace vs. Existing Spend",
      "score": 72,
      "rationale": "Open RFP signals fresh budget",
      "evidence_refs": [
        3
      ]
    },
    {
      "key": "industry_fit",
      "name": "Industry Fit & Velocity",
      "score": 72,
      "rationale": "Comparable wins in same category",
      "evidence_refs": [
        1
      ]
    }
  ]
}

Parse · five dimensions

Agent Opportunity
69

Repetitive triage at scale

AI Commitment
64

Roadmap line items shipped in last quarter

Transformation Readiness
72

Change-mgmt office stood up

Whitespace vs. Existing Spend
69

Open RFP signals fresh budget

Industry Fit & Velocity
67

Comparable wins in same category

Evidence pack · 3

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

    fetched 6/6/2026 · dims: agent_opp, transform_ready

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

    fetched 6/3/2026 · dims: industry_fit, transform_ready

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

    fetched 6/2/2026 · dims: whitespace, industry_fit

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