Multi-Stage Lead Qualification

Add the FutureSearch connector if you haven't already. Then upload a CSV of 20 investment funds and ask Claude:

I have a CSV of 20 investment funds. Run this pipeline: 1. Score each fund 0-100 on likelihood to adopt research tools. 2. Filter to funds scoring >= 50. 3. For remaining funds, estimate their investment team size. 4. Final filter: include if score >= 70 OR team size <= 5.

Results take about 4 minutes.

Go to futuresearch.ai/app, upload a CSV of 20 investment funds, and enter:

Score each fund 0-100 on likelihood to adopt research tools.

Then filter results to funds scoring >= 50, re-upload, and run additional stages for team size estimation and final classification.

The FutureSearch SDK chains multiple operations in a single session. This example demonstrates a three-stage lead qualification pipeline using rank(), pandas filtering, and classify().

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

import asyncio
import pandas as pd
from futuresearch import create_session
from futuresearch.ops import rank, classify

async def main():
    async with create_session(name="Multi-Stage Lead Screening") as session:
        # Stage 1: Score by research tool adoption
        scored = await rank(
            session=session,
            task="Score funds 0-100 on likelihood to adopt research tools",
            input=funds_df,
            field_name="score",
        )

        # Stage 2: Filter by threshold
        filtered = scored.data[scored.data["score"] >= 50].copy()

        # Stage 3: Research team sizes
        with_teams = await rank(
            session=session,
            task="Estimate investment team size per fund",
            input=filtered,
            field_name="team_size_estimate",
        )

        # Stage 4: Final screening
        final = await classify(
            session=session,
            task="Include if score >= 70 OR team size <= 5",
            categories=["include", "exclude"],
            input=with_teams.data,
        )
        return final.data

results = asyncio.run(main())