Enhance your web and mobile applications with powerful AI capabilities. Our AI Integration service helps you incorporate machine learning models into your existing systems to automate processes, improve user experiences, and unlock new business opportunities.
We understand that AI integration can be complex, especially if you're new to machine learning. That's why we take a highly collaborative approach, working closely with you at every stage. We'll handle the technical implementation while ensuring you understand the process, capabilities, and limitations. This partnership ensures the final solution aligns perfectly with your business goals while building your team's knowledge of AI applications.
Our AI integration solution uses Python, Flask, and modern web technologies:
*Example Code AI Integration implementation with Python and Flask
import os
import numpy as np
import pandas as pd
import joblib
from flask import Flask, request, jsonify
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler
# Initialize Flask app
app = Flask(__name__)
# Load pre-trained model and scaler
model_path = os.path.join(os.path.dirname(__file__), 'models', 'prediction_model.pkl')
scaler_path = os.path.join(os.path.dirname(__file__), 'models', 'scaler.pkl')
model = joblib.load(model_path)
scaler = joblib.load(scaler_path)
# Define prediction endpoint
@app.route('/api/predict', methods=['POST'])
def predict():
# Get data from request
data = request.json
# Validate input
required_fields = ['feature1', 'feature2', 'feature3', 'feature4']
for field in required_fields:
if field not in data:
return jsonify({'error': f'Missing required field: {field}'}), 400
# Prepare features
features = [[
data['feature1'],
data['feature2'],
data['feature3'],
data['feature4']
]]
# Scale features
scaled_features = scaler.transform(features)
# Make prediction
prediction = model.predict(scaled_features)[0]
prediction_proba = model.predict_proba(scaled_features)[0].tolist()
# Return result
return jsonify({
'prediction': int(prediction),
'confidence': max(prediction_proba),
'probabilities': prediction_proba
})
# Define model retraining endpoint (admin only)
@app.route('/api/retrain', methods=['POST'])
def retrain_model():
# Authentication would be implemented here
# Get training data
training_data = request.json.get('training_data')
if not training_data:
return jsonify({'error': 'No training data provided'}), 400
# Convert to DataFrame
df = pd.DataFrame(training_data)
# Separate features and target
X = df.drop('target', axis=1)
y = df['target']
# Scale features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Train new model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_scaled, y)
# Save model and scaler
joblib.dump(model, model_path)
joblib.dump(scaler, scaler_path)
return jsonify({'success': True, 'message': 'Model retrained successfully'})
# Web integration example (JavaScript)
'''
// Example of frontend JavaScript to call the prediction API
async function getPrediction(userData) {
const response = await fetch('/api/predict', {
method: 'POST',
headers: {
'Content-Type': 'application/json'
},
body: JSON.stringify({
feature1: userData.age,
feature2: userData.income,
feature3: userData.purchaseHistory,
feature4: userData.websiteActivity
})
});
const result = await response.json();
if (result.prediction === 1) {
// Show personalized recommendation
showRecommendation(result.confidence);
} else {
// Show default content
showDefaultContent();
}
}
'''
# Run the Flask app
if __name__ == '__main__':
app.run(debug=False, host='0.0.0.0', port=5000)
Approximately 8-12 weeks from project kickoff, depending on complexity and scope.
Businesses looking to enhance their digital products with intelligent features such as personalized recommendations, predictive analytics, content moderation, image recognition, or natural language processing. Particularly valuable for e-commerce platforms, content-heavy websites, customer service applications, and data-driven businesses.