Score Leads from Fragmented Data

Add the everyrow connector if you haven't already. Then upload b2b_companies.csv and ask Claude:

Score each company from 0-100 on their likelihood of suffering from data fragmentation challenges. High scores for multi-location operations, M&A history, disconnected systems. Low scores for single-location, cloud-native, integrated stacks.

20 companies scored in about 40 seconds. Multi-location businesses score highest, cloud-native startups lowest.

Claude Code is great at researching a single company's operations. Scoring 20 prospects on data fragmentation risk is harder. Each one needs web research into operational complexity, M&A history, and system diversity, then a consistent 0-100 score calibrated across the full set.

Here, we get Claude Code to score B2B companies on their likelihood of needing data integration solutions.

Add everyrow to Claude Code if you haven't already:

claude mcp add futuresearch --scope project --transport http https://mcp.futuresearch.ai/mcp

Download b2b_companies.csv. Tell Claude:

Score each company from 0-100 on their likelihood of suffering from data
fragmentation challenges. High scores for multi-location operations, M&A
history, disconnected systems. Low scores for single-location, cloud-native,
integrated stacks.

Claude calls everyrow's rank MCP tool:

Tool: everyrow_rank
├─ task: "Score each company from 0-100 on data fragmentation risk..."
├─ input_csv: "/Users/you/b2b_companies.csv"
├─ field_name: "score"
├─ field_type: "int"
└─ ascending_order: false

→ Submitted: 20 rows for ranking.
  Session: https://futuresearch.ai/sessions/0a3de921-5ffd-4031-91be-9a447e51b96a

Tool: everyrow_results
→ Saved 20 rows to /Users/you/scored_leads.csv

View the session.

Multi-location businesses (restaurants, healthcare networks, logistics) score highest. Cloud-native single-location companies score lowest. No web research was needed since the company descriptions contained enough context.

The everyrow SDK's rank() scores every row using LLM reasoning. For B2B lead scoring, it evaluates operational complexity from company descriptions.

pip install everyrow
export EVERYROW_API_KEY=your_key_here  # Get one at futuresearch.ai/api-key

import asyncio
import pandas as pd
from everyrow import create_session
from everyrow.ops import rank

companies_df = pd.read_csv("b2b_companies.csv")

async def main():
    async with create_session(name="Data Fragmentation Lead Scoring") as session:
        result = await rank(
            session=session,
            task="""
                Score each company from 0-100 on data fragmentation risk.
                High (70-100): Multiple locations, M&A history, disconnected systems.
                Low (0-30): Single location, cloud-native, integrated stack.
            """,
            input=companies_df,
            field_name="score",
        )
        return result.data.sort_values("score", ascending=False)

results = asyncio.run(main())

Top scores: QuickServe Restaurants (95), Global Logistics Partners (95), TransGlobal Shipping (92). Bottom scores: CloudFirst Startup (5), SimpleRetail Co (10), Boutique Law LLP (10). Food service, logistics, hospitality, and healthcare companies score highest due to multi-location operations and disconnected systems.