Practical AI solutions that deliver measurable business impact.
Predict future outcomes based on historical data.
Personalized recommendations that increase engagement and sales.
Impact: Typical recommendation engines increase conversion rates by 15-30% and average order value by 10-20%.
Extract insights from text and enable human-like language understanding.
Enable machines to understand images and video.
Identify unusual patterns that indicate problems or fraud.
Automate complex tasks that require decision-making.
From problem definition to production deployment and ongoing improvement.
Understand business problem and desired outcomes. Assess data availability and quality. Evaluate ML feasibility and expected accuracy. Estimate ROI and success metrics. Recommend approach (ML vs. rules-based vs. hybrid).
Data collection from various sources. Data cleaning and quality improvement. Handling missing values and outliers. Feature engineering. Data labeling (if needed). Train/validation/test split. Privacy and compliance review.
Select appropriate algorithms. Train multiple model variants. Hyperparameter tuning and optimization. Cross-validation and testing. Model evaluation and comparison. Bias and fairness testing. Model interpretation.
API development for model serving. Integration with existing applications. Real-time or batch prediction infrastructure. Model versioning and management. Monitoring and alerting setup. A/B testing framework.
Model performance monitoring. Data drift detection. Retraining triggers and automation. Continuous accuracy tracking. Business metrics monitoring. Model updates and improvements.
Industry-standard frameworks and tools for building production ML systems.
TensorFlow, PyTorch, Scikit-learn, XGBoost, LightGBM-industry-standard frameworks for building ML models.
Keras, PyTorch, TensorFlow for neural networks. Pre-trained models (BERT, GPT, ResNet) when applicable.
spaCy, NLTK, Hugging Face Transformers, OpenAI API for language understanding and generation.
OpenCV, PIL, TensorFlow/PyTorch for image processing. YOLO, Mask R-CNN for object detection.
Pandas, NumPy, Apache Spark for large-scale data processing. Feature stores for production.
MLflow, Kubeflow, AWS SageMaker for model management and deployment. Docker and Kubernetes.
We'll honestly tell you if ML isn't the right solution. Sometimes simpler approaches work better.
Pricing depends on complexity and data requirements.
4-6 weeks
2-3 months
NLP, Recommendations, 3-5 months
Computer Vision, Multi-model, 4-8 months
Enterprise AI Platform: ₹75L+, 6-12 months. Ongoing Monitoring & Retraining: ₹2L - ₹10L per month. Most clients see ROI within 6-18 months through automation, better decisions, or increased revenue.
Ready to leverage AI and machine learning for your business? Let's discuss your use case and determine if ML is the right approach.
Since 2013, Alphonic has delivered numerous award winning projects for more than 8 diverse industries.
ExploreWe have proudly delivered 700+ successful projects since our inception.