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AI/ML Studio
Powerful machine learning models for intelligent RFP processing
9
Active Models
156K
Predictions Today
94.7%
Avg Accuracy
23ms
Avg Latency
Win Probability Predictor
Gradient Boosting Classifier
Predicts likelihood of winning an RFP based on strategic fit, technical capability, past performance, and competitive factors.
89%
Accuracy
12
Features
15ms
Latency
Response Quality Scorer
NLP Multi-factor Analysis
Evaluates proposal response quality across clarity, completeness, compliance, specificity, and persuasiveness dimensions.
94%
Accuracy
6
Dimensions
45ms
Latency
Requirement Extractor
NLP Entity Recognition
Extracts and classifies requirements from RFP documents. Identifies SHALL/MUST statements and extracts key entities.
96%
Precision
7
Categories
120ms
Latency
Semantic Similarity Engine
Sentence Transformers
Finds semantically similar content using transformer-based embeddings. Powers response matching and duplicate detection.
384
Dimensions
98%
Recall
25ms
Latency
Risk Assessment Model
Multi-factor Risk Scoring
Assesses technical, schedule, cost, compliance, and competitive risks. Generates mitigation recommendations.
91%
Accuracy
6
Risk Types
18ms
Latency
Compliance Analyzer
Framework Mapping Engine
Maps requirements to compliance frameworks (FedRAMP, NIST, HIPAA, CMMC). Identifies gaps and generates evidence mapping.
5
Frameworks
421
Controls
35ms
Latency
ML Playground
Quality Analysis Results
94
Overall Quality Score
Clarity:
92
Completeness:
88
Compliance:
96
AI Recommendations
Strong compliance language with specific control references
Consider adding specific metrics for remediation SLAs
API Endpoints
/api/ml/score-response
12.4K calls
/api/ml/extract-requirements
8.7K calls
/api/ml/predict-win
6.2K calls
/api/ml/find-similar
15.8K calls
/api/ml/assess-risk
4.1K calls
Quick Start - Python SDK
import requests # Score a response result = requests.post('/api/ml/score-response', json={ 'response_text': 'Our solution provides...', 'requirement_text': 'The system shall...' }) score = result.json() print(f"Quality Score: {score['score']}") print(f"Recommendations: {score['recommendations']}") # Predict win probability prediction = requests.post('/api/ml/predict-win', json={ 'features': { 'strategic_fit': 0.85, 'technical_capability': 0.78, 'past_performance': 0.92 } }) print(f"Win Probability: {prediction.json()['win_probability']}%")