How to Build a Search Trust Score Pipeline

AUTHORITY_TIERS = {
    'tier1': {
        'domains': {'gov', 'edu', 'mil'},
        'known_sites': {'reuters.com', 'apnews.com', 'nature.com', 'science.org',
                        'arxiv.org', 'nih.gov', 'cdc.gov', 'who.int'},
        'score': 90
    },
    'tier2': {
        'domains': set(),
        'known_sites': {'nytimes.com', 'bbc.com', 'washingtonpost.com',
                        'github.com', 'stackoverflow.com', 'docs.python.org',
                        'developer.mozilla.org', 'microsoft.com'},
        'score': 75
    },
    'tier3': {
        'domains': {'org', 'io'},
        'known_sites': {'medium.com', 'dev.to', 'hackernoon.com', 'reddit.com'},
        'score': 50
    },
}

def get_authority_score(url: str) -> int:
    domain = url.split('/')[2] if '/' in url else ''
    tld = domain.split('.')[-1]
    for tier_name, tier in AUTHORITY_TIERS.items():
        if domain in tier['known_sites'] or tld in tier['domains']:
            return tier['score']
    return 30  # unknown domain baseline

test_urls = ['https://nih.gov/study', 'https://github.com/repo',
             'https://randomsite.xyz/blog']
for url in test_urls:
    print(f'  {url}: authority={get_authority_score(url)}')

import re
from datetime import datetime

def get_freshness_score(snippet: str, url: str) -> int:
    """Score freshness from 0-100 based on detected dates."""
    text = snippet + ' ' + url
    # Look for year patterns
    years = re.findall(r'20(2[4-9])', text)
    if years:
        latest_year = max(int('20' + y) for y in years)
        current_year = 2026
        age = current_year - latest_year
        if age == 0:
            return 100  # current year
        elif age == 1:
            return 70
        elif age == 2:
            return 40
        else:
            return 10
    # Look for month-year patterns
    months = re.findall(r'(?:Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)\w*\s+202[4-9]', text)
    if months:
        return 80  # has a recent date reference
    return 20  # no date information found

test_snippets = [
    ('Updated May 2026 - Best CRM tools', 'https://site.com/crm-2026'),
    ('A comprehensive guide from 2024', 'https://site.com/old-guide'),
    ('Learn Python programming basics', 'https://site.com/python'),
]
for snippet, url in test_snippets:
    print(f'  freshness={get_freshness_score(snippet, url):3d}: {snippet[:50]}')

import requests, os

SCAVIO_KEY = os.environ['SCAVIO_API_KEY']

def trust_score_results(query: str) -> list:
    resp = requests.post('https://api.scavio.dev/api/v1/search',
        headers={'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json'},
        json={'query': query, 'country_code': 'us', 'num_results': 10})
    results = resp.json().get('organic_results', [])
    scored = []
    # Collect all snippets for cross-reference
    all_snippets = [r.get('snippet', '').lower() for r in results]
    for i, r in enumerate(results):
        authority = get_authority_score(r['link'])
        freshness = get_freshness_score(r.get('snippet', ''), r['link'])
        # Cross-reference: do other results mention similar facts?
        my_keywords = set(re.findall(r'\b\w{5,}\b', r.get('snippet', '').lower()))
        cross_ref = 0
        for j, other in enumerate(all_snippets):
            if i != j:
                other_words = set(re.findall(r'\b\w{5,}\b', other))
                overlap = len(my_keywords & other_words)
                if overlap > 3:
                    cross_ref += 1
        consistency = min(cross_ref * 20, 100)
        # Weighted composite
        trust = round(authority * 0.4 + freshness * 0.3 + consistency * 0.3)
        scored.append({
            'title': r['title'][:50], 'url': r['link'],
            'trust_score': trust, 'authority': authority,
            'freshness': freshness, 'consistency': consistency
        })
    scored.sort(key=lambda x: -x['trust_score'])
    return scored

results = trust_score_results('best CRM software 2026')
print(f'{"Score":>5} {"Auth":>5} {"Fresh":>5} {"Cross":>5}  Title')
print('-' * 70)
for r in results[:5]:
    print(f'{r["trust_score"]:>5} {r["authority"]:>5} {r["freshness"]:>5} '
          f'{r["consistency"]:>5}  {r["title"]}')