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}")Comprehensive Matching System
Contractor Profile Management
from dataclasses import dataclass, field
from typing import List, Dict, Optional, Tuple
from datetime import date, datetime
from enum import Enum
import numpy as np
from sklearn.preprocessing import MinMaxScaler
class ContractorSize(Enum):
MICRO = "micro" # < 10 employees
SMALL = "small" # 10-50 employees
MEDIUM = "medium" # 50-250 employees
LARGE = "large" # > 250 employees
class WorkCategory(Enum):
GENERAL = "general_contractor"
CONCRETE = "concrete"
STRUCTURAL_STEEL = "structural_steel"
MEP = "mep"
ELECTRICAL = "electrical"
PLUMBING = "plumbing"
HVAC = "hvac"
FINISHING = "finishing"
FACADE = "facade"
ROOFING = "roofing"
EXCAVATION = "excavation"
FOUNDATION = "foundation"
LANDSCAPING = "landscaping"
DEMOLITION = "demolition"
@dataclass
class ProjectReference:
project_name: str
client: str
value: float
completion_date: date
work_type: str
performance_rating: float # 1-5
on_time: bool
on_budget: bool
client_reference_available: bool
@dataclass
class ContractorProfile:
contractor_id: str
company_name: str
legal_name: str
registration_number: str
size: ContractorSize
founded_year: int
employees_count: int
# Capabilities
specializations: List[WorkCategory]
equipment_owned: List[str]
max_project_value: float
min_project_value: float
# Certifications
certifications: List[Dict] # {name, issuer, valid_until}
licenses: List[Dict] # {type, number, region, valid_until}
# Performance
completed_projects: int
active_projects: int
references: List[ProjectReference] = field(default_factory=list)
# Safety
safety_certifications: List[str] = field(default_factory=list)
incident_rate: float = 0.0 # incidents per 1000 work hours
fatality_count: int = 0
lost_time_incidents: int = 0
# Financial
annual_revenue: float = 0
credit_rating: str = ""
insurance_coverage: float = 0
bonding_capacity: float = 0
# Geographic
headquarters_region: str = ""
operating_regions: List[str] = field(default_factory=list)
willing_to_travel: bool = False
# Current status
current_workload_pct: float = 0 # 0-100
earliest_availability: Optional[date] = None
# Pricing
historical_bid_data: List[Dict] = field(default_factory=list)
def calculate_performance_score(self) -> float:
"""Calculate overall performance score"""
if not self.references:
return 50.0 # Default for new contractors
ratings = [r.performance_rating for r in self.references]
on_time_rate = sum(1 for r in self.references if r.on_time) / len(self.references)
on_budget_rate = sum(1 for r in self.references if r.on_budget) / len(self.references)
# Weighted average
avg_rating = sum(ratings) / len(ratings) / 5 * 100 # Normalize to 0-100
on_time_score = on_time_rate * 100
on_budget_score = on_budget_rate * 100
return avg_rating * 0.5 + on_time_score * 0.3 + on_budget_score * 0.2
def calculate_safety_score(self) -> float:
"""Calculate safety score"""
base_score = 100
# Deductions
if self.incident_rate > 0:
base_score -= min(30, self.incident_rate * 10)
if self.fatality_count > 0:
base_score -= 50 # Major deduction for fatalities
if self.lost_time_incidents > 0:
base_score -= min(20, self.lost_time_incidents * 2)
# Bonuses for certifications
if 'ISO45001' in self.safety_certifications or 'OHSAS18001' in self.safety_certifications:
base_score += 10
return max(0, min(100, base_score))
def get_capacity_score(self) -> float:
"""Calculate available capacity score"""
return 100 - self.current_workload_pctAI Matching Engine
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd
@dataclass
class ProjectRequirements:
project_id: str
project_name: str
work_categories: List[WorkCategory]
required_certifications: List[str]
required_licenses: List[str]
region: str
estimated_value: float
start_date: date
duration_months: int
priority_weights: Dict[str, float] = field(default_factory=dict)
special_requirements: List[str] = field(default_factory=list)
def __post_init__(self):
if not self.priority_weights:
self.priority_weights = {
'performance': 0.25,
'safety': 0.20,
'price': 0.20,
'capacity': 0.15,
'experience': 0.10,
'financial': 0.10
}
class ContractorMatchingEngine:
"""AI-powered contractor matching system"""
def __init__(self):
self.contractors: Dict[str, ContractorProfile] = {}
self.vectorizer = TfidfVectorizer(ngram_range=(1, 2))
self.scaler = MinMaxScaler()
def register_contractor(self, profile: ContractorProfile):
"""Register contractor in the system"""
self.contractors[profile.contractor_id] = profile
def find_matches(self, requirements: ProjectRequirements,
top_n: int = 10) -> List[Dict]:
"""Find best matching contractors for project"""
eligible = self._filter_eligible(requirements)
if not eligible:
return []
scored = []
for contractor in eligible:
score, breakdown = self._calculate_match_score(contractor, requirements)
scored.append({
'contractor_id': contractor.contractor_id,
'company_name': contractor.company_name,
'total_score': score,
'score_breakdown': breakdown,
'profile': contractor
})
# Sort by score
scored.sort(key=lambda x: x['total_score'], reverse=True)
return scored[:top_n]
def _filter_eligible(self, req: ProjectRequirements) -> List[ContractorProfile]:
"""Filter contractors by basic eligibility"""
eligible = []
for contractor in self.contractors.values():
# Check region
if req.region not in contractor.operating_regions:
if not contractor.willing_to_travel:
continue
# Check work categories
contractor_cats = set(contractor.specializations)
required_cats = set(req.work_categories)
if not required_cats.intersection(contractor_cats):
continue
# Check project size
if req.estimated_value > contractor.max_project_value:
continue
if req.estimated_value < contractor.min_project_value:
continue
# Check certifications
contractor_certs = set(c['name'] for c in contractor.certifications
if c.get('valid_until', date.max) >= date.today())
if not set(req.required_certifications).issubset(contractor_certs):
continue
# Check licenses
contractor_licenses = set(l['type'] for l in contractor.licenses
if l.get('valid_until', date.max) >= date.today())
if not set(req.required_licenses).issubset(contractor_licenses):
continue
# Check capacity
if contractor.current_workload_pct >= 95: # Too busy
continue
# Check availability
if contractor.earliest_availability and contractor.earliest_availability > req.start_date:
continue
eligible.append(contractor)
return eligible
def _calculate_match_score(self, contractor: ContractorProfile,
req: ProjectRequirements) -> Tuple[float, Dict]:
"""Calculate weighted match score"""
weights = req.priority_weights
breakdown = {}
# Performance score
breakdown['performance'] = contractor.calculate_performance_score()
# Safety score
breakdown['safety'] = contractor.calculate_safety_score()
# Price competitiveness (from historical data)
breakdown['price'] = self._calculate_price_score(contractor, req)
# Capacity score
breakdown['capacity'] = contractor.get_capacity_score()
# Experience score (similar projects)
breakdown['experience'] = self._calculate_experience_score(contractor, req)
# Financial stability score
breakdown['financial'] = self._calculate_financial_score(contractor, req)
# Calculate weighted total
total = sum(
breakdown[key] * weights.get(key, 0)
for key in breakdown
)
return total, breakdown
def _calculate_price_score(self, contractor: ContractorProfile,
req: ProjectRequirements) -> float:
"""Calculate price competitiveness score"""
if not contractor.historical_bid_data:
return 50.0 # Neutral score
# Find similar projects
similar_bids = [
bid for bid in contractor.historical_bid_data
if bid.get('project_value', 0) * 0.5 <= req.estimated_value <= bid.get('project_value', 0) * 2
]
if not similar_bids:
return 50.0
# Calculate average variance from winning bids
variances = [bid.get('variance_pct', 0) for bid in similar_bids]
avg_variance = sum(variances) / len(variances)
# Lower variance = higher score
# -10% to +10% is normal range
if avg_variance <= -10:
return 90 # Very competitive
elif avg_variance <= 0:
return 80 - avg_variance # Competitive
elif avg_variance <= 10:
return 70 - avg_variance # Average
else:
return max(30, 60 - avg_variance) # Expensive
def _calculate_experience_score(self, contractor: ContractorProfile,
req: ProjectRequirements) -> float:
"""Calculate relevant experience score"""
if not contractor.references:
return 30.0 # Low score for no experience
relevant_projects = []
for ref in contractor.references:
# Check work type match
try:
work_cat = WorkCategory(ref.work_type)
if work_cat in req.work_categories:
relevant_projects.append(ref)
except ValueError:
continue
if not relevant_projects:
return 40.0
# Score based on number and recency of relevant projects
recent_relevant = [
p for p in relevant_projects
if (date.today() - p.completion_date).days <= 365 * 3 # Last 3 years
]
count_score = min(50, len(relevant_projects) * 10)
recency_score = min(30, len(recent_relevant) * 15)
# Value similarity
values = [p.value for p in relevant_projects]
avg_value = sum(values) / len(values)
value_ratio = min(req.estimated_value, avg_value) / max(req.estimated_value, avg_value)
value_score = value_ratio * 20
return count_score + recency_score + value_score
def _calculate_financial_score(self, contractor: ContractorProfile,
req: ProjectRequirements) -> float:
"""Calculate financial stability score"""
score = 50.0 # Base score
# Check bonding capacity
if contractor.bonding_capacity >= req.estimated_value:
score += 20
elif contractor.bonding_capacity >= req.estimated_value * 0.5:
score += 10
# Check insurance
if contractor.insurance_coverage >= req.estimated_value:
score += 15
elif contractor.insurance_coverage >= req.estimated_value * 0.5:
score += 7
# Credit rating
credit_scores = {'AAA': 15, 'AA': 12, 'A': 10, 'BBB': 5, 'BB': 0, 'B': -10}
score += credit_scores.get(contractor.credit_rating, 0)
return min(100, max(0, score))
def compare_contractors(self, contractor_ids: List[str],
req: ProjectRequirements) -> pd.DataFrame:
"""Compare specific contractors"""
data = []
for cid in contractor_ids:
contractor = self.contractors.get(cid)
if not contractor:
continue
score, breakdown = self._calculate_match_score(contractor, req)
row = {
'Contractor': contractor.company_name,
'Total Score': f"{score:.1f}",
'Performance': f"{breakdown['performance']:.1f}",
'Safety': f"{breakdown['safety']:.1f}",
'Price': f"{breakdown['price']:.1f}",
'Capacity': f"{breakdown['capacity']:.1f}",
'Experience': f"{breakdown['experience']:.1f}",
'Financial': f"{breakdown['financial']:.1f}",
'Active Projects': contractor.active_projects,
'Workload': f"{contractor.current_workload_pct:.0f}%"
}
data.append(row)
return pd.DataFrame(data)Bid Analysis and Prediction
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
import numpy as np
class BidPredictor:
"""Predict contractor bid prices"""
def __init__(self):
self.model = RandomForestRegressor(n_estimators=100, random_state=42)
self.is_trained = False
def train(self, historical_bids: pd.DataFrame):
"""Train bid prediction model
Expected columns:
- contractor_size, work_type, region, project_value
- contractor_performance, contractor_workload
- winning_bid, contractor_bid
"""
features = ['project_value', 'contractor_performance',
'contractor_workload', 'duration_months']
# One-hot encode categorical
df = pd.get_dummies(historical_bids,
columns=['contractor_size', 'work_type', 'region'])
# Features that exist
X_cols = [c for c in df.columns if c not in ['winning_bid', 'contractor_bid']]
X = df[X_cols]
y = df['contractor_bid']
self.feature_columns = X_cols
self.model.fit(X, y)
self.is_trained = True
def predict_bid(self, contractor: ContractorProfile,
project: ProjectRequirements) -> Dict:
"""Predict expected bid from contractor"""
if not self.is_trained:
# Simple estimation if not trained
base = project.estimated_value
variance = np.random.uniform(-0.1, 0.15)
return {
'predicted_bid': base * (1 + variance),
'confidence': 'low',
'variance_range': (-15, 20)
}
# Build feature vector
features = {
'project_value': project.estimated_value,
'contractor_performance': contractor.calculate_performance_score(),
'contractor_workload': contractor.current_workload_pct,
'duration_months': project.duration_months,
f'contractor_size_{contractor.size.value}': 1,
f'region_{project.region}': 1
}
# Add work type
for cat in project.work_categories:
features[f'work_type_{cat.value}'] = 1
# Create feature vector
X = pd.DataFrame([features]).reindex(columns=self.feature_columns, fill_value=0)
prediction = self.model.predict(X)[0]
# Calculate confidence based on similar historical data
return {
'predicted_bid': prediction,
'confidence': 'medium',
'variance_range': (-10, 15),
'estimated_value': project.estimated_value,
'predicted_variance_pct': (prediction - project.estimated_value) / project.estimated_value * 100
}
class BidEvaluator:
"""Evaluate and score contractor bids"""
def __init__(self, matching_engine: ContractorMatchingEngine):
self.engine = matching_engine
self.predictor = BidPredictor()
def evaluate_bids(self, project: ProjectRequirements,
bids: List[Dict]) -> pd.DataFrame:
"""Evaluate received bids
bids: List of {contractor_id, bid_amount, bid_breakdown, proposal}
"""
results = []
for bid in bids:
contractor = self.engine.contractors.get(bid['contractor_id'])
if not contractor:
continue
# Get match score
match_score, breakdown = self.engine._calculate_match_score(
contractor, project
)
# Price score (compared to other bids)
avg_bid = sum(b['bid_amount'] for b in bids) / len(bids)
price_deviation = (bid['bid_amount'] - avg_bid) / avg_bid * 100
if price_deviation <= -10:
price_score = 95 # Very competitive
elif price_deviation <= 0:
price_score = 85 - price_deviation
elif price_deviation <= 10:
price_score = 75 - price_deviation
else:
price_score = max(40, 65 - price_deviation)
# Overall evaluation score (weighted)
eval_score = match_score * 0.6 + price_score * 0.4
results.append({
'contractor_id': bid['contractor_id'],
'company_name': contractor.company_name,
'bid_amount': bid['bid_amount'],
'price_vs_avg': f"{price_deviation:+.1f}%",
'match_score': match_score,
'price_score': price_score,
'evaluation_score': eval_score,
'performance': breakdown['performance'],
'safety': breakdown['safety'],
'recommendation': self._get_recommendation(eval_score, price_deviation)
})
df = pd.DataFrame(results)
df = df.sort_values('evaluation_score', ascending=False)
return df
def _get_recommendation(self, eval_score: float, price_dev: float) -> str:
"""Generate recommendation"""
if eval_score >= 80 and price_dev <= 5:
return "Strongly Recommended"
elif eval_score >= 70:
return "Recommended"
elif eval_score >= 60:
return "Acceptable"
elif price_dev > 20:
return "Price Concerns"
else:
return "Review Required"Contractor Recommendation Report
def generate_recommendation_report(engine: ContractorMatchingEngine,
project: ProjectRequirements,
output_path: str) -> str:
"""Generate contractor recommendation report"""
matches = engine.find_matches(project, top_n=10)
with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
# Summary
summary_data = [{
'Project': project.project_name,
'Estimated Value': project.estimated_value,
'Work Categories': ', '.join(c.value for c in project.work_categories),
'Region': project.region,
'Start Date': project.start_date.isoformat(),
'Duration': f"{project.duration_months} months",
'Contractors Found': len(matches)
}]
pd.DataFrame(summary_data).to_excel(writer, sheet_name='Summary', index=False)
# Rankings
ranking_data = []
for i, match in enumerate(matches, 1):
ranking_data.append({
'Rank': i,
'Contractor': match['company_name'],
'Total Score': f"{match['total_score']:.1f}",
'Performance': f"{match['score_breakdown']['performance']:.1f}",
'Safety': f"{match['score_breakdown']['safety']:.1f}",
'Price': f"{match['score_breakdown']['price']:.1f}",
'Capacity': f"{match['score_breakdown']['capacity']:.1f}",
'Experience': f"{match['score_breakdown']['experience']:.1f}",
'Financial': f"{match['score_breakdown']['financial']:.1f}"
})
pd.DataFrame(ranking_data).to_excel(writer, sheet_name='Rankings', index=False)
# Detailed profiles for top 5
for i, match in enumerate(matches[:5], 1):
profile = match['profile']
profile_data = [{
'Field': 'Company Name', 'Value': profile.company_name
}, {
'Field': 'Size', 'Value': profile.size.value
}, {
'Field': 'Employees', 'Value': profile.employees_count
}, {
'Field': 'Completed Projects', 'Value': profile.completed_projects
}, {
'Field': 'Active Projects', 'Value': profile.active_projects
}, {
'Field': 'Current Workload', 'Value': f"{profile.current_workload_pct}%"
}, {
'Field': 'Bonding Capacity', 'Value': f"${profile.bonding_capacity:,.0f}"
}, {
'Field': 'Safety Incidents', 'Value': profile.lost_time_incidents
}]
pd.DataFrame(profile_data).to_excel(
writer, sheet_name=f'Contractor_{i}', index=False
)
return output_pathQuick Reference
| Criterion | Weight Range | Data Sources |
|---|---|---|
| Performance | 20-30% | Project references, ratings |
| Safety | 15-25% | OSHA records, certifications |
| Price | 15-25% | Historical bids |
| Capacity | 10-20% | Current workload |
| Experience | 10-15% | Similar projects |
| Financial | 10-15% | Credit rating, bonding |
Resources
- DDC Website: https://datadrivenconstruction.io
- Construction contractor databases: BuildingConnected, PlanHub
Next Steps
- See
risk-assessment-mlfor contractor risk analysis - See
document-classification-nlpfor proposal analysis - See
open-construction-estimatefor bid validation