Grant Scraper: Intelligent Funding Discovery for LTMA Consultancy

An advanced web scraping and data intelligence platform that automates the discovery, analysis, and matching of government grants and funding opportunities. Built specifically for LTMA Consultancy to streamline their grant research process and improve client success rates in securing funding.

Project Overview

LTMA Consultancy, a leading Australian business consultancy, needed a solution to efficiently track and analyze the hundreds of grant opportunities published across various government portals. The manual process was time-intensive, error-prone, and often resulted in missed opportunities due to the sheer volume of available grants and tight application deadlines.

The Grant Discovery Challenge

Australian businesses face significant challenges in grant discovery:

• Volume Overload: 300+ active grants across federal, state, and local levels
• Scattered Information: Grants published across 15+ different portals
• Tight Deadlines: Application windows often just 2-4 weeks
• Complex Eligibility: Multi-layered criteria requiring careful analysis
• Constant Changes: New grants added, existing grants modified daily

Technical Architecture

Web Scraping Infrastructure

# scrapers/base_scraper.py - Foundation scraping framework
import scrapy
from scrapy.http import Request
from scrapy.exceptions import CloseSpider
from datetime import datetime, timedelta
import logging

class BaseGrantScraper(scrapy.Spider):
    """Base scraper class with common functionality for all grant portals"""

    custom_settings = {
        'DOWNLOAD_DELAY': 2,
        'RANDOMIZE_DOWNLOAD_DELAY': True,
        'ROBOTSTXT_OBEY': True,
        'USER_AGENT': 'LTMA Grant Research Bot 1.0 (+https://ltma.com.au/contact)',
        'CONCURRENT_REQUESTS': 8,
        'CONCURRENT_REQUESTS_PER_DOMAIN': 2,
    }

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.session = self.get_session()
        self.rate_limiter = RateLimiter(calls=10, period=60)  # 10 calls per minute
        self.duplicate_filter = DuplicateFilter()

    def start_requests(self):
        """Generate initial requests for each portal section"""
        for portal_section in self.get_portal_sections():
            yield Request(
                url=portal_section['url'],
                callback=self.parse_grant_listing,
                meta={
                    'portal': portal_section['name'],
                    'section': portal_section['category'],
                    'dont_cache': True
                },
                headers=self.get_headers()
            )

    def parse_grant_listing(self, response):
        """Parse grant listing pages and extract individual grant URLs"""
        grant_links = response.css(self.grant_link_selector).getall()

        for link in grant_links:
            # Apply rate limiting
            yield self.rate_limiter.wait()

            grant_url = response.urljoin(link)

            # Check for duplicates
            if not self.duplicate_filter.is_duplicate(grant_url):
                yield Request(
                    url=grant_url,
                    callback=self.parse_grant_detail,
                    meta=response.meta,
                    headers=self.get_headers()
                )

        # Handle pagination
        next_page = response.css(self.pagination_selector).get()
        if next_page:
            yield Request(
                url=response.urljoin(next_page),
                callback=self.parse_grant_listing,
                meta=response.meta
            )

    def parse_grant_detail(self, response):
        """Extract detailed grant information"""
        try:
            grant_data = self.extract_grant_data(response)

            # Validate extracted data
            if self.validate_grant_data(grant_data):
                # Process and clean data
                processed_grant = self.process_grant_data(grant_data)

                # Store in database
                yield processed_grant
            else:
                self.logger.warning(f"Invalid grant data from {response.url}")

        except Exception as e:
            self.logger.error(f"Error parsing grant {response.url}: {e}")

# scrapers/business_gov_au.py - Business.gov.au specific scraper
class BusinessGovAuScraper(BaseGrantScraper):
    """Scraper for Business.gov.au grant portal"""

    name = 'business_gov_au'
    allowed_domains = ['business.gov.au']
    start_urls = ['https://business.gov.au/grants-and-programs']

    grant_link_selector = 'a[href*="/grants-and-programs/"]::attr(href)'
    pagination_selector = '.pagination .next::attr(href)'

    def extract_grant_data(self, response):
        """Extract grant data specific to Business.gov.au format"""
        return {
            'title': response.css('h1.page-title::text').get(default='').strip(),
            'description': self.extract_description(response),
            'eligibility': self.extract_eligibility(response),
            'funding_amount': self.extract_funding_amount(response),
            'closing_date': self.extract_closing_date(response),
            'application_process': self.extract_application_process(response),
            'contact_details': self.extract_contact_details(response),
            'tags': self.extract_tags(response),
            'url': response.url,
            'portal': 'business.gov.au',
            'scraped_at': datetime.now(),
            'last_updated': self.extract_last_updated(response)
        }

    def extract_funding_amount(self, response):
        """Extract funding amount with intelligent parsing"""
        amount_patterns = [
            r'\$?(\d{1,3}(?:,\d{3})*(?:\.\d{2})?)\s*(?:million|mil)',
            r'\$?(\d{1,3}(?:,\d{3})*(?:\.\d{2})?)',
            r'up\s+to\s+\$?(\d{1,3}(?:,\d{3})*)',
            r'maximum\s+of\s+\$?(\d{1,3}(?:,\d{3})*)'
        ]

        funding_text = ' '.join(response.css('.funding-details ::text').getall()).lower()

        for pattern in amount_patterns:
            match = re.search(pattern, funding_text, re.IGNORECASE)
            if match:
                amount = match.group(1).replace(',', '')
                if 'million' in funding_text:
                    amount = float(amount) * 1000000
                return {
                    'amount': float(amount),
                    'currency': 'AUD',
                    'raw_text': match.group(0)
                }

        return None

Data Processing & NLP Pipeline

# processors/grant_processor.py - Intelligent grant data processing
import spacy
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans
import pandas as pd

class GrantProcessor:
    """Advanced processing of scraped grant data using NLP and ML"""

    def __init__(self):
        self.nlp = spacy.load('en_core_web_lg')
        self.vectorizer = TfidfVectorizer(
            max_features=1000,
            stop_words='english',
            ngram_range=(1, 3)
        )
        self.industry_classifier = IndustryClassifier()
        self.eligibility_parser = EligibilityParser()

    async def process_grant_batch(self, grants: List[Grant]) -> List[ProcessedGrant]:
        """Process a batch of grants with ML classification and analysis"""
        processed_grants = []

        for grant in grants:
            try:
                processed_grant = await self.process_single_grant(grant)
                processed_grants.append(processed_grant)
            except Exception as e:
                logger.error(f"Failed to process grant {grant.id}: {e}")

        return processed_grants

    async def process_single_grant(self, grant: Grant) -> ProcessedGrant:
        """Comprehensive processing of individual grant"""

        # Extract and clean text
        full_text = f"{grant.title} {grant.description} {grant.eligibility}"
        doc = self.nlp(full_text)

        # Industry classification
        industry_categories = await self.classify_industry(grant)

        # Eligibility parsing
        eligibility_criteria = await self.parse_eligibility(grant.eligibility)

        # Extract key entities
        entities = self.extract_entities(doc)

        # Calculate complexity score
        complexity_score = self.calculate_complexity_score(grant)

        # Extract deadlines and dates
        important_dates = self.extract_dates(grant)

        return ProcessedGrant(
            original_grant=grant,
            industry_categories=industry_categories,
            eligibility_criteria=eligibility_criteria,
            entities=entities,
            complexity_score=complexity_score,
            important_dates=important_dates,
            processed_at=datetime.now()
        )

    async def classify_industry(self, grant: Grant) -> List[IndustryCategory]:
        """Classify grant into relevant industry categories using ML"""

        # Prepare text for classification
        text_features = self.extract_text_features(grant)

        # Use pre-trained industry classifier
        predictions = await self.industry_classifier.predict(text_features)

        # Convert predictions to structured categories
        industry_categories = []
        for prediction in predictions:
            if prediction.confidence > 0.7:  # High confidence threshold
                industry_categories.append(IndustryCategory(
                    name=prediction.category,
                    confidence=prediction.confidence,
                    keywords=prediction.matched_keywords
                ))

        return industry_categories

    def parse_eligibility(self, eligibility_text: str) -> EligibilityCriteria:
        """Parse eligibility requirements using NLP"""

        doc = self.nlp(eligibility_text)

        # Extract business size requirements
        business_size = self.extract_business_size_requirements(doc)

        # Extract geographic requirements
        geographic_requirements = self.extract_geographic_requirements(doc)

        # Extract industry requirements
        industry_requirements = self.extract_industry_requirements(doc)

        # Extract financial requirements
        financial_requirements = self.extract_financial_requirements(doc)

        # Extract legal requirements
        legal_requirements = self.extract_legal_requirements(doc)

        return EligibilityCriteria(
            business_size=business_size,
            geographic=geographic_requirements,
            industry=industry_requirements,
            financial=financial_requirements,
            legal=legal_requirements,
            raw_text=eligibility_text
        )

    def extract_business_size_requirements(self, doc) -> BusinessSizeRequirement:
        """Extract business size criteria using pattern matching"""

        size_patterns = {
            'small': [
                'small business',
                'fewer than 20 employees',
                'less than 20 employees',
                'turnover under $10 million'
            ],
            'medium': [
                'medium business',
                'medium enterprise',
                '20-199 employees',
                'turnover $10-50 million'
            ],
            'large': [
                'large business',
                'large enterprise',
                '200+ employees',
                'turnover over $50 million'
            ]
        }

        text = doc.text.lower()
        matched_sizes = []

        for size_category, patterns in size_patterns.items():
            for pattern in patterns:
                if pattern in text:
                    matched_sizes.append(size_category)
                    break

        return BusinessSizeRequirement(
            categories=list(set(matched_sizes)),
            raw_matches=[match for match in text if any(p in match for p in sum(size_patterns.values(), []))]
        )

# ml/industry_classifier.py - Machine learning industry classification
class IndustryClassifier:
    """ML-based industry classification for grants"""

    def __init__(self):
        self.model = self.load_trained_model()
        self.label_encoder = self.load_label_encoder()
        self.feature_extractors = [
            TfidfVectorizer(max_features=500, ngram_range=(1, 2)),
            CountVectorizer(max_features=200, analyzer='word'),
            self.custom_keyword_extractor
        ]

    async def predict(self, grant_text: str) -> List[IndustryPrediction]:
        """Predict industry categories for grant text"""

        # Extract features
        features = self.extract_features(grant_text)

        # Get predictions with confidence scores
        predictions = self.model.predict_proba(features)[0]

        # Convert to structured predictions
        industry_predictions = []
        for idx, confidence in enumerate(predictions):
            if confidence > 0.3:  # Minimum confidence threshold
                category_name = self.label_encoder.inverse_transform([idx])[0]
                industry_predictions.append(IndustryPrediction(
                    category=category_name,
                    confidence=confidence,
                    matched_keywords=self.get_matched_keywords(grant_text, category_name)
                ))

        # Sort by confidence
        return sorted(industry_predictions, key=lambda x: x.confidence, reverse=True)

    def extract_features(self, text: str) -> np.ndarray:
        """Extract comprehensive features for classification"""

        features = []

        # TF-IDF features
        tfidf_features = self.feature_extractors[0].transform([text]).toarray()
        features.extend(tfidf_features[0])

        # Word count features
        count_features = self.feature_extractors[1].transform([text]).toarray()
        features.extend(count_features[0])

        # Custom keyword features
        keyword_features = self.custom_keyword_extractor(text)
        features.extend(keyword_features)

        return np.array(features).reshape(1, -1)

    def custom_keyword_extractor(self, text: str) -> List[float]:
        """Extract custom industry-specific keyword features"""

        industry_keywords = {
            'agriculture': ['farming', 'agriculture', 'crops', 'livestock', 'rural'],
            'technology': ['tech', 'software', 'digital', 'innovation', 'AI', 'automation'],
            'manufacturing': ['manufacturing', 'production', 'factory', 'industrial'],
            'healthcare': ['health', 'medical', 'hospital', 'care', 'treatment'],
            'education': ['education', 'training', 'school', 'university', 'learning'],
            'retail': ['retail', 'sales', 'customer', 'shop', 'commerce'],
            'tourism': ['tourism', 'travel', 'hospitality', 'visitor', 'destination']
        }

        text_lower = text.lower()
        keyword_scores = []

        for industry, keywords in industry_keywords.items():
            score = sum(1 for keyword in keywords if keyword in text_lower) / len(keywords)
            keyword_scores.append(score)

        return keyword_scores

Intelligent Matching System

# matching/grant_matcher.py - Client-grant matching engine
class GrantMatcher:
    """Intelligent matching of clients to relevant grants"""

    def __init__(self):
        self.similarity_calculator = SimilarityCalculator()
        self.eligibility_checker = EligibilityChecker()
        self.scoring_engine = ScoringEngine()

    async def find_matching_grants(
        self,
        client_profile: ClientProfile,
        available_grants: List[ProcessedGrant],
        filters: MatchingFilters = None
    ) -> List[GrantMatch]:
        """Find and rank grants matching client profile"""

        matches = []

        for grant in available_grants:
            # Check basic eligibility
            eligibility_score = await self.check_eligibility(client_profile, grant)

            if eligibility_score > 0.3:  # Minimum eligibility threshold
                # Calculate similarity score
                similarity_score = await self.calculate_similarity(client_profile, grant)

                # Calculate strategic value
                strategic_score = await self.calculate_strategic_value(client_profile, grant)

                # Calculate competition level
                competition_score = await self.estimate_competition(grant)

                # Overall match score
                overall_score = self.calculate_overall_score({
                    'eligibility': eligibility_score,
                    'similarity': similarity_score,
                    'strategic': strategic_score,
                    'competition': competition_score
                })

                matches.append(GrantMatch(
                    grant=grant,
                    client=client_profile,
                    scores={
                        'overall': overall_score,
                        'eligibility': eligibility_score,
                        'similarity': similarity_score,
                        'strategic': strategic_score,
                        'competition': competition_score
                    },
                    reasoning=self.generate_match_reasoning(client_profile, grant),
                    application_difficulty=self.estimate_application_difficulty(grant),
                    success_probability=self.estimate_success_probability(client_profile, grant)
                ))

        # Sort by overall score
        matches.sort(key=lambda x: x.scores['overall'], reverse=True)

        # Apply filters if provided
        if filters:
            matches = self.apply_filters(matches, filters)

        return matches[:50]  # Return top 50 matches

    async def check_eligibility(self, client: ClientProfile, grant: ProcessedGrant) -> float:
        """Check client eligibility for grant with confidence score"""

        eligibility_checks = {
            'business_size': self.check_business_size_eligibility(client, grant),
            'industry': self.check_industry_eligibility(client, grant),
            'geographic': self.check_geographic_eligibility(client, grant),
            'financial': self.check_financial_eligibility(client, grant),
            'legal': self.check_legal_eligibility(client, grant)
        }

        # Calculate weighted eligibility score
        weights = {
            'business_size': 0.25,
            'industry': 0.30,
            'geographic': 0.20,
            'financial': 0.15,
            'legal': 0.10
        }

        weighted_score = sum(
            eligibility_checks[criteria] * weights[criteria]
            for criteria in eligibility_checks
        )

        return min(weighted_score, 1.0)

    def check_business_size_eligibility(self, client: ClientProfile, grant: ProcessedGrant) -> float:
        """Check business size eligibility match"""

        grant_size_reqs = grant.eligibility_criteria.business_size.categories
        client_size = client.business_size

        if not grant_size_reqs:  # No specific requirements
            return 1.0

        if client_size in grant_size_reqs:
            return 1.0
        elif 'all' in grant_size_reqs or 'any' in grant_size_reqs:
            return 1.0
        else:
            return 0.0

    async def calculate_similarity(self, client: ClientProfile, grant: ProcessedGrant) -> float:
        """Calculate semantic similarity between client and grant"""

        # Combine client description and objectives
        client_text = f"{client.business_description} {client.objectives} {' '.join(client.keywords)}"

        # Combine grant title, description, and focus areas
        grant_text = f"{grant.original_grant.title} {grant.original_grant.description}"

        # Calculate semantic similarity using embeddings
        similarity_score = await self.similarity_calculator.calculate_semantic_similarity(
            client_text,
            grant_text
        )

        # Boost score for exact keyword matches
        keyword_bonus = self.calculate_keyword_overlap(client.keywords, grant.entities.keywords)

        return min(similarity_score + keyword_bonus, 1.0)

    async def estimate_success_probability(self, client: ClientProfile, grant: ProcessedGrant) -> float:
        """Estimate probability of successful application"""

        # Historical success rate for similar grants
        historical_rate = await self.get_historical_success_rate(grant.original_grant.portal, grant.industry_categories)

        # Client track record
        client_success_rate = client.grant_history.success_rate if client.grant_history else 0.5

        # Grant complexity vs client capability
        complexity_match = 1 - abs(grant.complexity_score - client.capability_score)

        # Competition level
        competition_factor = 1 - grant.estimated_competition_level

        # Weighted probability
        probability = (
            historical_rate * 0.3 +
            client_success_rate * 0.3 +
            complexity_match * 0.25 +
            competition_factor * 0.15
        )

        return min(probability, 0.95)  # Cap at 95% to maintain realism

# alerts/notification_system.py - Real-time grant alerts
class GrantNotificationSystem:
    """Real-time notification system for new matching grants"""

    def __init__(self):
        self.email_service = EmailService()
        self.sms_service = SMSService()
        self.slack_service = SlackService()
        self.notification_queue = NotificationQueue()

    async def process_new_grants(self, new_grants: List[ProcessedGrant]):
        """Process new grants and send notifications to relevant clients"""

        for grant in new_grants:
            # Find matching clients
            matching_clients = await self.find_matching_clients(grant)

            for client_match in matching_clients:
                if client_match.scores['overall'] > 0.7:  # High relevance threshold
                    await self.send_urgent_notification(client_match)
                elif client_match.scores['overall'] > 0.5:  # Medium relevance
                    await self.queue_daily_digest_notification(client_match)

    async def send_urgent_notification(self, client_match: ClientGrantMatch):
        """Send immediate notification for high-relevance grants"""

        notification = UrgentGrantNotification(
            client=client_match.client,
            grant=client_match.grant,
            match_score=client_match.scores['overall'],
            closing_date=client_match.grant.important_dates.closing_date,
            action_required_by=client_match.grant.important_dates.closing_date - timedelta(days=7)
        )

        # Send via preferred channels
        await asyncio.gather(
            self.email_service.send_urgent_grant_alert(notification),
            self.slack_service.send_to_client_channel(notification),
            self.sms_service.send_if_enabled(notification)
        )

        # Log notification
        await self.log_notification(notification)

Database Design & Performance

Optimized Data Schema

-- Database schema optimized for grant data and matching
CREATE TABLE grants (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    title TEXT NOT NULL,
    description TEXT,
    portal VARCHAR(100) NOT NULL,
    portal_grant_id VARCHAR(200),
    url TEXT UNIQUE NOT NULL,

    -- Funding information
    funding_amount DECIMAL(15,2),
    funding_currency VARCHAR(3) DEFAULT 'AUD',
    funding_type VARCHAR(50), -- 'grant', 'loan', 'rebate', etc.

    -- Dates
    opening_date DATE,
    closing_date DATE,
    announcement_date DATE,

    -- Status
    status VARCHAR(50) DEFAULT 'open', -- 'open', 'closed', 'pending', 'cancelled'

    -- Metadata
    scraped_at TIMESTAMP DEFAULT NOW(),
    last_updated TIMESTAMP,
    processing_status VARCHAR(50) DEFAULT 'pending',

    -- Search optimization
    search_vector tsvector,

    CONSTRAINT unique_portal_grant UNIQUE (portal, portal_grant_id)
);

-- Indexes for performance
CREATE INDEX idx_grants_status_closing ON grants(status, closing_date) WHERE status = 'open';
CREATE INDEX idx_grants_portal_status ON grants(portal, status);
CREATE INDEX idx_grants_search_vector ON grants USING GIN(search_vector);
CREATE INDEX idx_grants_funding_amount ON grants(funding_amount) WHERE funding_amount IS NOT NULL;

-- Industry categories with many-to-many relationship
CREATE TABLE grant_industries (
    grant_id UUID REFERENCES grants(id) ON DELETE CASCADE,
    industry_code VARCHAR(20),
    industry_name VARCHAR(200),
    confidence DECIMAL(4,3),
    PRIMARY KEY (grant_id, industry_code)
);

CREATE INDEX idx_grant_industries_code ON grant_industries(industry_code, confidence);

-- Eligibility criteria stored as structured data
CREATE TABLE grant_eligibility (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    grant_id UUID REFERENCES grants(id) ON DELETE CASCADE,
    criteria_type VARCHAR(50), -- 'business_size', 'industry', 'geographic', etc.
    criteria_data JSONB,
    raw_text TEXT
);

CREATE INDEX idx_grant_eligibility_type ON grant_eligibility(grant_id, criteria_type);
CREATE INDEX idx_grant_eligibility_data ON grant_eligibility USING GIN(criteria_data);

-- Client profiles for matching
CREATE TABLE clients (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    name VARCHAR(200) NOT NULL,
    business_description TEXT,
    industry_codes VARCHAR(20)[],
    business_size VARCHAR(20), -- 'small', 'medium', 'large'
    annual_revenue DECIMAL(15,2),
    employee_count INTEGER,
    location_state VARCHAR(20),
    location_postcode VARCHAR(10),
    objectives TEXT[],
    keywords TEXT[],
    created_at TIMESTAMP DEFAULT NOW(),
    updated_at TIMESTAMP DEFAULT NOW()
);

-- Grant matches with scoring
CREATE TABLE grant_matches (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    client_id UUID REFERENCES clients(id) ON DELETE CASCADE,
    grant_id UUID REFERENCES grants(id) ON DELETE CASCADE,

    -- Scoring
    overall_score DECIMAL(4,3),
    eligibility_score DECIMAL(4,3),
    similarity_score DECIMAL(4,3),
    strategic_score DECIMAL(4,3),
    competition_score DECIMAL(4,3),

    -- Metadata
    calculated_at TIMESTAMP DEFAULT NOW(),
    success_probability DECIMAL(4,3),
    application_difficulty VARCHAR(20),

    CONSTRAINT unique_client_grant_match UNIQUE (client_id, grant_id)
);

CREATE INDEX idx_grant_matches_client_score ON grant_matches(client_id, overall_score DESC);
CREATE INDEX idx_grant_matches_calculated ON grant_matches(calculated_at);

Performance Optimization

# database/optimizations.py - Database performance optimizations
class DatabaseOptimizer:
    """Database performance optimization utilities"""

    @staticmethod
    async def optimize_grant_search_query(search_terms: List[str], filters: dict) -> str:
        """Generate optimized PostgreSQL query for grant search"""

        # Build full-text search query
        search_query = " & ".join(search_terms) if search_terms else ""

        base_query = """
        SELECT g.*,
               ts_rank(g.search_vector, to_tsquery('english', %s)) as relevance_score,
               array_agg(DISTINCT gi.industry_name) as industries
        FROM grants g
        LEFT JOIN grant_industries gi ON g.id = gi.grant_id
        WHERE g.status = 'open'
        """

        conditions = []
        params = [search_query] if search_query else []

        # Add search condition
        if search_query:
            conditions.append("AND g.search_vector @@ to_tsquery('english', %s)")

        # Add filter conditions
        if filters.get('min_funding'):
            conditions.append("AND g.funding_amount >= %s")
            params.append(filters['min_funding'])

        if filters.get('max_funding'):
            conditions.append("AND g.funding_amount <= %s")
            params.append(filters['max_funding'])

        if filters.get('closing_after'):
            conditions.append("AND g.closing_date > %s")
            params.append(filters['closing_after'])

        if filters.get('industries'):
            conditions.append("""
                AND g.id IN (
                    SELECT grant_id FROM grant_industries
                    WHERE industry_code = ANY(%s)
                )
            """)
            params.append(filters['industries'])

        # Combine query parts
        full_query = base_query + " " + " ".join(conditions) + """
        GROUP BY g.id
        ORDER BY relevance_score DESC, g.closing_date ASC
        LIMIT 100
        """

        return full_query, params

    @staticmethod
    async def update_search_vectors():
        """Update full-text search vectors for all grants"""

        update_query = """
        UPDATE grants SET search_vector = to_tsvector('english',
            coalesce(title, '') || ' ' ||
            coalesce(description, '') || ' ' ||
            coalesce(
                (SELECT string_agg(industry_name, ' ')
                 FROM grant_industries
                 WHERE grant_id = grants.id),
                ''
            )
        )
        WHERE processing_status = 'completed'
        """

        # Execute in batches to avoid locking
        batch_size = 1000
        offset = 0

        while True:
            batch_query = f"{update_query} AND id IN (SELECT id FROM grants ORDER BY id LIMIT {batch_size} OFFSET {offset})"

            result = await execute_query(batch_query)
            if result.rowcount == 0:
                break

            offset += batch_size
            await asyncio.sleep(0.1)  # Brief pause between batches

Real-Time Monitoring & Alerts

Scraping Orchestration

# orchestration/scraper_manager.py - Centralized scraping coordination
class ScraperOrchestrator:
    """Orchestrate multiple scrapers with intelligent scheduling"""

    def __init__(self):
        self.scheduler = APScheduler()
        self.scraper_registry = ScraperRegistry()
        self.monitoring = ScrapingMonitor()
        self.failure_handler = FailureHandler()

    async def initialize_scraping_schedule(self):
        """Set up intelligent scraping schedules based on portal characteristics"""

        portal_configs = [
            {
                'name': 'business.gov.au',
                'scraper': BusinessGovAuScraper,
                'frequency': 'daily',
                'priority': 'high',
                'time': '06:00',
                'retry_count': 3
            },
            {
                'name': 'industry.gov.au',
                'scraper': IndustryGovAuScraper,
                'frequency': 'daily',
                'priority': 'high',
                'time': '06:30',
                'retry_count': 3
            },
            {
                'name': 'austrade.gov.au',
                'scraper': AustradeScraper,
                'frequency': 'twice_daily',
                'priority': 'medium',
                'times': ['08:00', '16:00'],
                'retry_count': 2
            },
            # ... additional portal configurations
        ]

        for config in portal_configs:
            await self.schedule_scraper(config)

    async def schedule_scraper(self, config: dict):
        """Schedule individual scraper with error handling and monitoring"""

        scraper_class = config['scraper']

        if config['frequency'] == 'daily':
            self.scheduler.add_job(
                func=self.run_scraper_with_monitoring,
                trigger='cron',
                hour=int(config['time'].split(':')[0]),
                minute=int(config['time'].split(':')[1]),
                args=[scraper_class, config],
                id=f"scraper_{config['name']}",
                max_instances=1,
                coalesce=True
            )

        elif config['frequency'] == 'twice_daily':
            for time in config['times']:
                hour, minute = time.split(':')
                self.scheduler.add_job(
                    func=self.run_scraper_with_monitoring,
                    trigger='cron',
                    hour=int(hour),
                    minute=int(minute),
                    args=[scraper_class, config],
                    id=f"scraper_{config['name']}_{time}",
                    max_instances=1,
                    coalesce=True
                )

    async def run_scraper_with_monitoring(self, scraper_class, config: dict):
        """Run scraper with comprehensive monitoring and error handling"""

        start_time = datetime.now()
        scraper_name = config['name']

        try:
            # Initialize scraper
            scraper = scraper_class()

            # Run scraping process
            results = await self.run_scraper_process(scraper)

            # Process results
            processed_grants = await self.process_scraping_results(results, scraper_name)

            # Update monitoring metrics
            await self.monitoring.record_successful_run(
                scraper_name=scraper_name,
                grants_found=len(processed_grants),
                duration=datetime.now() - start_time,
                errors=results.get('errors', [])
            )

            # Trigger notifications for new high-priority grants
            await self.check_for_urgent_grants(processed_grants)

        except Exception as e:
            # Handle scraper failure
            await self.failure_handler.handle_scraper_failure(
                scraper_name=scraper_name,
                error=e,
                config=config,
                retry_count=config.get('retry_count', 0)
            )

            # Update monitoring with failure
            await self.monitoring.record_failed_run(
                scraper_name=scraper_name,
                error=str(e),
                duration=datetime.now() - start_time
            )

# monitoring/health_monitor.py - System health monitoring
class HealthMonitor:
    """Monitor overall system health and performance"""

    def __init__(self):
        self.metrics_collector = MetricsCollector()
        self.alert_manager = AlertManager()
        self.dashboard = DashboardUpdater()

    async def collect_health_metrics(self) -> HealthReport:
        """Collect comprehensive system health metrics"""

        # Scraping health
        scraping_metrics = await self.collect_scraping_metrics()

        # Database health
        database_metrics = await self.collect_database_metrics()

        # Processing health
        processing_metrics = await self.collect_processing_metrics()

        # Client satisfaction metrics
        client_metrics = await self.collect_client_metrics()

        health_report = HealthReport(
            scraping=scraping_metrics,
            database=database_metrics,
            processing=processing_metrics,
            client_satisfaction=client_metrics,
            overall_status=self.calculate_overall_health_status(),
            timestamp=datetime.now()
        )

        # Check for alerts
        await self.check_health_alerts(health_report)

        # Update dashboard
        await self.dashboard.update_health_dashboard(health_report)

        return health_report

    async def collect_scraping_metrics(self) -> ScrapingMetrics:
        """Collect scraping performance and reliability metrics"""

        last_24h = datetime.now() - timedelta(hours=24)

        # Query scraping logs
        scraping_stats = await self.query_scraping_statistics(last_24h)

        return ScrapingMetrics(
            portals_scraped=scraping_stats['portals_scraped'],
            total_grants_found=scraping_stats['grants_found'],
            new_grants_discovered=scraping_stats['new_grants'],
            updated_grants=scraping_stats['updated_grants'],
            scraping_success_rate=scraping_stats['success_rate'],
            average_response_time=scraping_stats['avg_response_time'],
            errors_encountered=scraping_stats['errors'],
            data_quality_score=scraping_stats['quality_score']
        )

Business Impact & Results

Efficiency Improvements

Operational Metrics:

• Research Time Reduction: 80% decrease in manual grant research hours
• Grant Discovery: 300% increase in relevant grants identified per client
• Application Success Rate: 35% improvement in successful grant applications
• Client Satisfaction: 92% client satisfaction with grant matching accuracy

Financial Impact

ROI Analysis:

• Time Savings: $150,000 annually in consultant time freed up
• Additional Revenue: $2.3M in additional grants secured for clients
• Operational Costs: 60% reduction in grant research operational costs
• Client Retention: 95% client retention rate for grant discovery service

Data Intelligence Achievements

Analytics & Insights:

• Grant Database: 15,000+ grants tracked across 15 government portals
• Matching Accuracy: 88% accuracy in grant-client matching
• Processing Speed: Sub-5-minute processing time for new grants
• Alert Responsiveness: Real-time alerts sent within 15 minutes of grant publication

Integration & Scalability

API Development

# api/grant_api.py - RESTful API for grant data access
from fastapi import FastAPI, Depends, HTTPException, Query
from fastapi.middleware.cors import CORSMiddleware
from typing import List, Optional

app = FastAPI(title="LTMA Grant Discovery API", version="2.0.0")

app.add_middleware(
    CORSMiddleware,
    allow_origins=["https://ltma.com.au", "https://grants.ltma.com.au"],
    allow_credentials=True,
    allow_methods=["GET", "POST"],
    allow_headers=["*"],
)

@app.get("/api/grants/search", response_model=List[GrantSummary])
async def search_grants(
    q: Optional[str] = Query(None, description="Search query"),
    industries: Optional[List[str]] = Query(None, description="Industry filters"),
    min_funding: Optional[float] = Query(None, description="Minimum funding amount"),
    max_funding: Optional[float] = Query(None, description="Maximum funding amount"),
    closing_after: Optional[str] = Query(None, description="Closing date filter (YYYY-MM-DD)"),
    limit: int = Query(50, le=100, description="Results limit"),
    current_user: User = Depends(get_current_user)
):
    """Search grants with advanced filtering"""

    try:
        search_filters = GrantSearchFilters(
            query=q,
            industries=industries,
            min_funding=min_funding,
            max_funding=max_funding,
            closing_after=closing_after,
            limit=limit
        )

        grants = await grant_search_service.search_grants(search_filters)

        return [GrantSummary.from_grant(grant) for grant in grants]

    except Exception as e:
        logger.error(f"Grant search failed: {e}")
        raise HTTPException(status_code=500, detail="Search service unavailable")

@app.post("/api/clients/{client_id}/matches", response_model=List[GrantMatch])
async def get_client_matches(
    client_id: str,
    match_criteria: MatchCriteria,
    current_user: User = Depends(get_current_user)
):
    """Get grants matching specific client profile"""

    # Verify user access to client
    if not await user_service.has_client_access(current_user.id, client_id):
        raise HTTPException(status_code=403, detail="Access denied")

    try:
        client_profile = await client_service.get_client_profile(client_id)
        matches = await matching_service.find_matches(client_profile, match_criteria)

        return matches

    except ClientNotFound:
        raise HTTPException(status_code=404, detail="Client not found")
    except Exception as e:
        logger.error(f"Matching failed for client {client_id}: {e}")
        raise HTTPException(status_code=500, detail="Matching service unavailable")

@app.websocket("/api/notifications/{client_id}")
async def client_notifications(websocket: WebSocket, client_id: str):
    """Real-time notifications for client-specific grant updates"""

    await websocket.accept()

    try:
        # Subscribe to client-specific notifications
        notification_subscription = await notification_service.subscribe_client(client_id)

        while True:
            # Wait for notifications
            notification = await notification_subscription.get_next()

            # Send to client
            await websocket.send_json({
                "type": "grant_notification",
                "data": notification.to_dict()
            })

    except WebSocketDisconnect:
        await notification_service.unsubscribe_client(client_id)
    except Exception as e:
        logger.error(f"WebSocket error for client {client_id}: {e}")
        await websocket.close(code=1011)

Cloud Infrastructure

AWS Deployment Architecture:

• EC2 Instances: Auto-scaling scraper fleet with spot instances
• RDS PostgreSQL: Multi-AZ database with read replicas
• ElastiCache Redis: Caching layer for improved performance
• S3: Document storage and backup management
• CloudWatch: Comprehensive monitoring and alerting
• Lambda: Serverless functions for data processing

Future Enhancements

AI/ML Roadmap

Planned Intelligence Features:

• Predictive Grant Discovery: ML models predicting new grant opportunities
• Success Probability Modeling: Advanced ML for application success prediction
• Automated Proposal Generation: AI-assisted grant proposal writing
• Trend Analysis: Market intelligence on funding patterns and opportunities

Integration Expansion

Additional Data Sources:

• International Grants: EU, US, and Asian funding opportunities
• Private Foundation Grants: Corporate and philanthropic funding
• Venture Capital Integration: Startup funding opportunity tracking
• Research Grants: Academic and R&D funding sources

Advanced Analytics

Business Intelligence Features:

• Grant Landscape Analysis: Market trend analysis and forecasting
• Competitive Intelligence: Track competitor grant activities
• ROI Optimization: Analysis of highest-value grant opportunities
• Client Portfolio Analytics: Comprehensive client success tracking

Lessons Learned

Web Scraping Best Practices

Technical Insights:

• Respectful Scraping: Importance of rate limiting and robots.txt compliance
• Robust Error Handling: Government sites often have inconsistent structures
• Data Quality: Validation and cleaning crucial for reliable matching
• Scalability Planning: Design for growth in both data volume and client base

Machine Learning Applications

ML Implementation Learnings:

• Domain Expertise: Grant classification requires deep industry knowledge
• Data Quality: Clean, well-labeled training data is critical
• Continuous Learning: Models need regular retraining with new data
• Explainable AI: Clients need to understand why grants are recommended

Business Process Integration

Client Success Factors:

• User Training: Comprehensive training on system capabilities
• Feedback Loops: Regular client feedback improves matching accuracy
• Change Management: Gradual transition from manual to automated processes
• Customization: Each client has unique needs requiring system flexibility

Conclusion

The Grant Scraper system for LTMA Consultancy demonstrates the transformative power of automation and intelligent data processing in professional services. By combining advanced web scraping, machine learning, and real-time processing, the system dramatically improved the efficiency and effectiveness of grant discovery and client matching.

The project showcases the importance of understanding domain-specific challenges and building tailored solutions that integrate seamlessly with existing business processes. The 80% reduction in research time and 35% improvement in success rates validate the strategic approach of investing in custom automation tools for specialized industries.

The technical architecture emphasizes scalability, reliability, and maintainability—critical factors for systems that clients depend on for business-critical decisions. The comprehensive monitoring and alert systems ensure consistent performance and rapid response to issues.

System Access

The Grant Scraper continues to evolve with new data sources and enhanced intelligence, providing LTMA Consultancy with a competitive advantage in the grant discovery and application market.