AI & Machine Learning
Data science and artificial intelligence solutions tailored to your business goals: prediction, NLP, image processing, GenAI and MLOps.
Solution Areas
Prediction
Demand forecasting, churn, risk and pricing models.
Natural Language (NLP)
Sentiment analysis, search, classification and summarization.
Image Processing
Object detection, quality control, OCR and segmentation.
GenAI & RAG
Secure generative AI assistants with your enterprise data.
MLOps
Pipeline, model versioning, monitoring and automation.
Data Platform
Data lake/lakehouse, ETL/ELT and feature store.
Technologies
Vertex AI
AutoML, Feature Store, model deployment and monitoring.
TensorFlow/PyTorch
Deep learning frameworks and accelerators.
BigQuery ML
Modeling with SQL, rapid prototyping and scoring.
Feast
Feature Store for online/offline feature management.
MLflow
Experiment tracking, model registry, metrics.
Kubeflow
ML pipeline orchestration and production deployment.
ML Project Process
Problem Definition
Business problem analysis, success metrics and data requirements.
Data Collection
Data sources, ETL processes and data quality controls.
Data Preparation
Feature engineering, data cleaning and preprocessing.
Model Development
Algorithm selection, model training and hyperparameter optimization.
Model Evaluation
Testing, validation, performance metrics and A/B tests.
Model Deployment
Go-live, API development and monitoring setup.
Use Cases
E-commerce
Recommendation systems, price optimization, inventory forecasting and customer segmentation.
- Product recommendations
- Dynamic pricing
- Churn prediction
- Fraud detection
Healthcare
Medical image analysis, disease diagnosis, drug discovery and personalized treatment.
- Radiology analysis
- Genetic analysis
- Drug interactions
- Patient risk scoring
Finance
Risk assessment, credit scoring, algorithmic trading and fraud detection.
- Credit risk analysis
- Algorithmic trading
- Anti-money laundering
- Portfolio optimization
Manufacturing
Quality control, predictive maintenance, supply chain optimization and energy management.
- Predictive maintenance
- Quality control
- Supply chain
- Energy optimization
AI/ML Benefits
Data-Driven Decisions
Objective decision-making processes supported by real data.
Automation
Automation of repetitive tasks and productivity increase.
Personalization
Personalization of customer experience and satisfaction increase.
Prediction
Forecasting future trends and proactive actions.
Cost Reduction
Reduction of operational costs and resource optimization.
Competitive Advantage
Market leadership and differentiation with innovative solutions.
Frequently Asked Questions
Source system integration, data cleaning, feature extraction and quality controls are applied. ETL/ELT pipelines are established and data quality is continuously monitored.
Automatic deployment with CI/CD, A/B testing, canary deployment, model monitoring and rollback processes are established.
The most suitable algorithm is selected based on your data size, problem type and performance requirements. Automatic model selection with AutoML is also possible.
Model performance is continuously monitored with drift detection, accuracy tracking, latency monitoring and automatic retraining processes.
Enterprise data security with RAG, prompt injection protection, output filtering and user authorization systems are established.
It varies according to the number of models, data size and usage frequency. A monthly budget of $500-2000 is recommended for starters.
GDPR-compliant data processing, encryption, anonymization and federated learning techniques are applied.
ROI is calculated by measuring time savings, error reduction, automation gains and improvements in business processes.