Build an enriched ICP scoring table

<context>
I have an account list. I want each account enriched with verified firmographics and signals, then scored against my ICP and tiered A/B/C for prioritization.

My ICP:
- Headcount: [RANGE, e.g. 500-5,000]
- Revenue: [RANGE, e.g. $50M-1B]
- Industry: [TARGET, e.g. B2B SaaS]
- Geography: [REGION, e.g. United States]
- Signal preference: [e.g. recent funding round, hiring surge, leadership change]

Scoring weight (rebalance as needed):
- Size fit: [weight]
- Industry match: [weight]
- Geography match: [weight]
- Active signal: [weight]
- Stage indicator (public / growth / early): [weight]
</context>

<task>
1. Take this account list (one row per line, with company name or domain):
   [PASTE LIST]

2. For each account, use Lusha to enrich:
   - Company name, HQ, headcount band, revenue range, industry
   - Funding history (total raised, last round, last round date, IPO status)
   - LinkedIn follower count (as a soft market-presence indicator)
   - Office footprint (HQ + count of regional offices)

3. Apply the ICP framework to each account:
   - Size fit: does the headcount and revenue land inside the target range?
   - Industry match: does Lusha's sub-industry match the ICP industry?
   - Geography match: does the HQ or office footprint match the target region?
   - Active signal: is there a funding round in the last 12 months, or a public-company event?
   - Stage indicator: public (mature stack, replacement sale), growth (rebuilding stack, expansion sale), early-stage (foundational stack, greenfield sale)

4. Assign a tier per account:
   - Tier A — Strong fit on 4+ ICP dimensions plus active signal in last 12 months
   - Tier B — Strong fit on 3 ICP dimensions, or 4 dimensions without an active signal
   - Tier C — Strong fit on 2 or fewer ICP dimensions

5. Output a scoring table:
   Company | Tier | Size fit | Industry | Geography | Active signal | Stage | Notes

6. Summarize at the top: total accounts, count per tier, recommended first-touch count for Tier A.
</task>

<constraints>
- Use Lusha's canonical company name and verified data. Do not invent fields.
- Surface the scoring reasoning per row so the user can adjust weights and re-tier.
- An account that fails industry or geography match cannot be Tier A regardless of size.
- An account without any verifiable signal can still be Tier B if size and industry are strong fits.
- Flag any account Lusha cannot resolve as no-match — do not auto-tier missing data.
</constraints>

Input: 5-account list (Snowflake, Datadog, Notion, Together AI, Verkada) and an ICP defined as B2B SaaS, US-based, headcount 500-10,000, revenue $50M-10B, with preference for accounts that closed funding in the last 12 months.

Output: 3 Tier A, 1 Tier B, 1 Tier C. Below is the real result from running the prompt against the live Lusha connector.

The Together AI row is the most useful demonstration of how the scoring works. Industry, geography, and active signal are all strong. Size fit fails because the headcount is below the ICP floor. The framework holds — a single failed dimension drops it from Tier A to Tier B even with a strong recent round.

Five rows enriched, zero new credits consumed — this demo reuses the firmographic data already verified in the company-enrichment prompt.