Transform data into actionable intelligence. We build custom AI and machine learning solutions that automate decisions, predict outcomes, and unlock new business opportunities.
AI solutions for forward-thinking organizations
Organizations with data assets looking to extract insights and automate decision-making
Add intelligent features to products-recommendations, personalization, predictions
Automate processes, optimize logistics, predict maintenance needs, and reduce costs
Comprehensive AI and machine learning capabilities
AI applications across diverse sectors
Industry-leading AI and ML tools
From data to deployed intelligence
Understand business objectives, define success metrics, and assess data availability and quality.
Gather, clean, and prepare data. Feature engineering and exploratory data analysis.
Train multiple models, perform hyperparameter tuning, and validate performance with cross-validation.
Rigorous testing against business metrics, bias detection, and explainability analysis.
Deploy models to production with API endpoints, monitoring, and integration with existing systems.
Continuous performance monitoring, retraining pipelines, and model improvement based on new data.
Illustrative examples of common AI/ML engagements - not delivered client results
Illustrative example · Sample manufacturing company
Hypothetical scenario for planning purposes - not a delivered client engagement.
Equipment downtime disrupts production; leadership wants earlier warning signals from sensor data.
Example approach: ingest IoT sensor readings, train a classification model on historical failure labels, and surface alerts in an operations dashboard.
Illustrative example · Sample subscription business
Hypothetical scenario for planning purposes - not a delivered client engagement.
Retention teams need a ranked list of accounts likely to cancel so outreach can be prioritised.
Example approach: feature engineering on usage and billing data, ensemble model training, and CRM-integrated risk scores.
Illustrative example · Sample e-commerce retailer
Hypothetical scenario for planning purposes - not a delivered client engagement.
Product discovery is weak; the business wants personalised recommendations without rebuilding the entire storefront.
Example approach: collaborative filtering or embedding-based recommendations exposed via API to the existing catalogue.
Everything you need to know about AI & machine learning
AI (Artificial Intelligence) is the broader concept of machines mimicking human intelligence. Machine Learning is a subset of AI where systems learn from data without explicit programming. Deep Learning is a subset of ML using neural networks with multiple layers. In practice, when businesses say "AI," they usually mean ML applications like predictive analytics, recommendation engines, or natural language processing.
Timeline varies significantly based on complexity and data availability. A simple predictive model takes 1-2 months, standard ML solutions with custom features take 3-4 months, and complex deep learning systems take 6-12 months. Data quality and availability are often the biggest factors-we may spend 60% of time on data preparation. We start with MVPs to demonstrate value quickly.
It depends on the problem complexity. Simple regression models work with hundreds of examples, standard classification needs thousands, while deep learning typically requires tens of thousands or more. Quality matters more than quantity-clean, relevant, representative data yields better results. We can also use transfer learning and data augmentation techniques to work with smaller datasets effectively.
Costs vary widely based on complexity. Simple ML models start from LKR 800,000, standard solutions from LKR 2,500,000, and complex deep learning systems from LKR 5,000,000. Ongoing costs include cloud infrastructure for training and inference (LKR 100,000-500,000/month), model monitoring, and retraining. We provide transparent pricing and ROI projections showing how AI investments pay back through efficiency gains or revenue growth.
Absolutely. We integrate with existing databases, data warehouses, CRM systems, ERP platforms, and APIs. We work with structured data (databases, spreadsheets), unstructured data (text, images, videos), and streaming data. Our solutions expose APIs for seamless integration with your applications. We also handle data privacy and compliance requirements (GDPR, HIPAA) as needed.
Model performance degrades over time due to data drift and changing patterns. We implement MLOps practices including automated monitoring of model accuracy, data quality checks, alert systems for performance degradation, and automated retraining pipelines. We set up A/B testing frameworks to safely deploy new models and roll back if needed. Regular model audits ensure continued business value and identify improvement opportunities.
Let's explore how machine learning can solve your toughest challenges