Automated Grant Writing System
Overview
Natural Language Processing system that assists in generating grant proposals by analyzing successful applications, matching funding opportunities, and automating repetitive writing tasks.
Technical Architecture
System Components
┌─────────────────────────────────────────────┐
│ Grant Database & Web Scraping │
│ Funding Opportunities • Past Proposals │
└─────────────┬───────────────────────────────┘
│
┌─────────────▼───────────────────────────────┐
│ NLP Processing Pipeline │
│ Text Analysis • Pattern Recognition │
└─────────────┬───────────────────────────────┘
│
┌─────────────▼───────────────────────────────┐
│ Content Generation Engine │
│ Template Matching • Text Generation │
└─────────────┬───────────────────────────────┘
│
┌─────────────▼───────────────────────────────┐
│ Quality Assurance & Compliance │
│ Requirements Check • Formatting │
└─────────────────────────────────────────────┘
Core Functionality
1. Opportunity Matching
class GrantMatcher:
def __init__(self, organization_profile):
self.profile = organization_profile
self.vectorizer = TfidfVectorizer(max_features=1000)
def match_opportunities(self, grant_database):
"""
Match organization to relevant grants
"""
# Semantic similarity matching
# Eligibility criteria filtering
# Historical success rate analysis
profile_vector = self.vectorize_profile()
grant_vectors = self.vectorize_grants(grant_database)
similarities = cosine_similarity(profile_vector, grant_vectors)
return self.rank_opportunities(similarities)
2. Content Analysis
class ProposalAnalyzer:
def __init__(self):
self.nlp = spacy.load('en_core_web_lg')
def extract_successful_patterns(self, winning_proposals):
"""
Identify patterns in successful grants
"""
patterns = {
'structure': self.analyze_structure(winning_proposals),
'keywords': self.extract_keywords(winning_proposals),
'sentiment': self.analyze_sentiment(winning_proposals),
'readability': self.calculate_readability(winning_proposals)
}
return patterns
def score_proposal(self, proposal_text, requirements):
"""
Evaluate proposal against requirements
"""
scores = {
'completeness': self.check_requirements(proposal_text, requirements),
'clarity': self.assess_clarity(proposal_text),
'impact': self.measure_impact_language(proposal_text),
'compliance': self.verify_compliance(proposal_text)
}
return scores
3. Content Generation
class ContentGenerator:
def __init__(self, templates, style_guide):
self.templates = templates
self.style_guide = style_guide
self.generator = self.setup_language_model()
def generate_section(self, section_type, context):
"""
Generate grant proposal sections
"""
if section_type == 'executive_summary':
return self.generate_summary(context)
elif section_type == 'budget_narrative':
return self.generate_budget_narrative(context)
elif section_type == 'impact_statement':
return self.generate_impact(context)
def adapt_boilerplate(self, template, specifics):
"""
Customize standard text for specific grant
"""
# Named entity replacement
# Context-aware modification
# Tone adjustment
return customized_content
NLP Technologies
Text Processing Pipeline
-
Document Parsing
- PDF extraction (PyPDF2, pdfplumber)
- Structure recognition
- Table and figure handling
-
Language Analysis
- Named Entity Recognition (NER)
- Dependency parsing
- Topic modeling (LDA, BERT)
-
Text Generation
- Template-based generation
- Fine-tuned language models
- Context-aware text completion
Machine Learning Models
Classification Models:
# Grant success prediction
class SuccessPredictor:
def __init__(self):
self.model = XGBClassifier(
n_estimators=100,
max_depth=5,
learning_rate=0.01
)
def extract_features(self, proposal):
features = {
'word_count': len(proposal.split()),
'readability_score': self.calculate_flesch_score(proposal),
'keyword_density': self.calculate_keyword_density(proposal),
'section_completeness': self.check_sections(proposal),
'budget_clarity': self.assess_budget(proposal)
}
return features
Data Management
Grant Database
- Web scraping of funding portals
- API integration (Grants.gov, Foundation Center)
- Historical proposal archive
- Success rate tracking
Storage Architecture
# Database schema
class GrantDatabase:
def __init__(self):
self.postgres_conn = psycopg2.connect(DATABASE_URL)
self.mongodb_client = MongoClient(MONGO_URL)
def store_opportunity(self, grant_data):
# Structured data → PostgreSQL
# Full text → MongoDB
# Embeddings → Vector database
pass
Integration Features
API Endpoints
# FastAPI implementation
@app.post("/analyze-rfp")
async def analyze_rfp(document: UploadFile):
"""
Extract requirements from RFP document
"""
text = extract_text(document)
requirements = parse_requirements(text)
return {"requirements": requirements, "deadlines": extract_deadlines(text)}
@app.post("/generate-section")
async def generate_section(
section: str,
context: dict,
word_limit: int
):
"""
Generate specific proposal section
"""
content = generator.create_section(section, context, word_limit)
return {"content": content, "word_count": len(content.split())}
Workflow Automation
- Deadline tracking and reminders
- Collaborative editing features
- Version control for proposals
- Submission checklist automation
Technology Stack
Backend:
- Python (FastAPI/Flask)
- PostgreSQL (structured data)
- MongoDB (documents)
- Redis (caching)
NLP Libraries:
- spaCy, NLTK (text processing)
- Transformers (Hugging Face)
- Gensim (topic modeling)
- TextBlob (sentiment analysis)
Infrastructure:
- Docker containerization
- AWS Lambda (serverless functions)
- S3 (document storage)
- CloudWatch (monitoring)
System Metrics
- Processing Speed: <5 seconds per page
- Accuracy: 90% requirement extraction accuracy
- Database: 10,000+ funding opportunities tracked
- Templates: 500+ reusable components
Impact
- 60% reduction in proposal preparation time
- Improved compliance checking
- Standardized quality across submissions
- Knowledge preservation from past proposals
Key Innovations
Smart Templating:
- Dynamic content blocks
- Context-aware customization
- Compliance verification
Learning System:
- Continuous improvement from feedback
- Success pattern recognition
- Style adaptation
Specific client details and proprietary algorithms have been omitted.