contractor-matching-ai

AI Contractor Matching

Overview

This skill implements AI-powered contractor matching for construction projects. Analyze project requirements against contractor capabilities, track historical performance, and generate recommendations based on multiple criteria.

Matching Criteria:

• Technical capabilities & expertise
• Past performance scores
• Certifications & licenses
• Geographic availability
• Capacity & current workload
• Pricing competitiveness
• Safety records

Quick Start

from dataclasses import dataclass, field
from typing import List, Dict, Optional
from datetime import date
import numpy as np

@dataclass
class Contractor:
    contractor_id: str
    name: str
    specializations: List[str]
    certifications: List[str]
    performance_score: float  # 0-100
    safety_score: float  # 0-100
    regions: List[str]
    capacity_available: float  # 0-100 percentage
    avg_bid_variance: float  # % above/below average

@dataclass
class ProjectRequirement:
    project_id: str
    work_types: List[str]
    required_certs: List[str]
    region: str
    estimated_value: float
    priority: str  # cost, quality, speed, safety

def match_contractors(project: ProjectRequirement,
                     contractors: List[Contractor],
                     top_n: int = 5) -> List[Dict]:
    """Simple contractor matching"""
    scores = []

    for c in contractors:
        # Check basic eligibility
        if project.region not in c.regions:
            continue

        work_match = len(set(project.work_types) & set(c.specializations))
        if work_match == 0:
            continue

        cert_match = len(set(project.required_certs) & set(c.certifications))
        if cert_match < len(project.required_certs):
            continue

        # Calculate score based on priority
        if project.priority == 'quality':
            score = c.performance_score * 0.6 + (100 - abs(c.avg_bid_variance)) * 0.2 + c.capacity_available * 0.2
        elif project.priority == 'cost':
            score = (100 - c.avg_bid_variance) * 0.5 + c.performance_score * 0.3 + c.capacity_available * 0.2
        elif project.priority == 'safety':
            score = c.safety_score * 0.6 + c.performance_score * 0.3 + c.capacity_available * 0.1
        else:  # speed
            score = c.capacity_available * 0.5 + c.performance_score * 0.3 + c.safety_score * 0.2

        scores.append({
            'contractor': c,
            'score': score,
            'work_match': work_match / len(project.work_types),
            'cert_match': cert_match / len(project.required_certs) if project.required_certs else 1.0
        })

    # Sort and return top matches
    scores.sort(key=lambda x: x['score'], reverse=True)
    return scores[:top_n]

# Example
contractors = [
    Contractor("C001", "ABC Builders", ["concrete", "structural"], ["ISO9001", "OHSAS18001"],
              85, 90, ["Moscow", "SPB"], 60, -5),
    Contractor("C002", "XYZ Construction", ["concrete", "finishing"], ["ISO9001"],
              78, 85, ["Moscow"], 80, 10),
]

project = ProjectRequirement("P001", ["concrete"], ["ISO9001"], "Moscow", 1000000, "quality")
matches = match_contractors(project, contractors)
for m in matches:
    print(f"{m['contractor'].name}: Score {m['score']:.1f}")