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# file: agents/scorer.py
from datetime import datetime, timedelta
from app.schema import Prospect
from app.config import MIN_FIT_SCORE
class Scorer:
"""Scores prospects and drops low-quality ones"""
def __init__(self, mcp_registry):
self.mcp = mcp_registry
self.store = mcp_registry.get_store_client()
async def run(self, prospect: Prospect) -> Prospect:
"""Score prospect based on various factors"""
score = 0.0
# Industry scoring
high_value_industries = ["SaaS", "FinTech", "E-commerce", "Healthcare Tech"]
if prospect.company.industry in high_value_industries:
score += 0.3
else:
score += 0.1
# Size scoring
if 100 <= prospect.company.size <= 5000:
score += 0.2 # Sweet spot
elif prospect.company.size > 5000:
score += 0.1 # Enterprise, harder to sell
else:
score += 0.05 # Too small
# Pain points alignment
cx_related_pains = ["customer retention", "NPS", "support efficiency", "personalization"]
matching_pains = sum(
1 for pain in prospect.company.pains
if any(keyword in pain.lower() for keyword in cx_related_pains)
)
score += min(0.3, matching_pains * 0.1)
# Facts freshness
fresh_facts = 0
stale_facts = 0
now = datetime.utcnow()
for fact in prospect.facts:
age_hours = (now - fact.collected_at).total_seconds() / 3600
if age_hours > fact.ttl_hours:
stale_facts += 1
else:
fresh_facts += 1
if fresh_facts > 0:
score += min(0.2, fresh_facts * 0.05)
# Confidence from facts
if prospect.facts:
avg_confidence = sum(f.confidence for f in prospect.facts) / len(prospect.facts)
score += avg_confidence * 0.2
# Normalize score
prospect.fit_score = min(1.0, score)
# Decision
if prospect.fit_score < MIN_FIT_SCORE:
prospect.status = "dropped"
prospect.dropped_reason = f"Low fit score: {prospect.fit_score:.2f}"
elif stale_facts > fresh_facts:
prospect.status = "dropped"
prospect.dropped_reason = f"Stale facts: {stale_facts}/{len(prospect.facts)}"
else:
prospect.status = "scored"
await self.store.save_prospect(prospect)
return prospect |