01
Predictive Analytics
Build models for demand, churn, risk, conversion and operational forecasting.
Machine Learning Engineering
Intelligent ModelsBuilt for Real-World Decisions
We design, train and deploy production-ready machine learning systems that turn business data into predictions, recommendations, automation and measurable outcomes.
ML
LEARNING ENGINE
Predictive ModelsForecast outcomes
RecommendationsPersonalized decisions
Computer VisionImage intelligence
MLOpsDeploy & monitor
NLPLanguage intelligence
Anomaly DetectionFind unusual signals
DATA → FEATURES
MODEL → INFERENCE
Capabilities
Machine learning engineered around your data and decisions.
From experimentation to reliable production inference, Cyanous connects data, models and software engineering into one delivery lifecycle.
02
Classification & Regression
Turn historical patterns into practical scores, categories and continuous predictions.
03
Recommendation Systems
Deliver relevant products, content, offers and next-best actions.
04
NLP & Text Intelligence
Extract meaning from documents, messages, tickets and customer conversations.
05
Computer Vision
Apply image classification, detection and visual inspection to real workflows.
06
Anomaly Detection
Identify unusual transactions, telemetry, behavior and operational signals.
07
Feature Engineering
Create robust model inputs from structured, behavioral and time-series data.
08
Model Training
Experiment, tune, validate and compare models using reproducible workflows.
09
MLOps & Deployment
Package, serve, monitor and continuously improve models in production.
Solutions
Models that become part of the product.
We connect ML inference to applications, APIs, dashboards and business processes instead of leaving models isolated in notebooks.
Customer IntelligenceChurn prediction, segmentation, lifetime value and propensity scoring.
Operations & ForecastingDemand planning, capacity forecasting and predictive maintenance.
Risk & FraudTransaction scoring, anomaly detection and behavioral signals.
PersonalizationRecommendations, ranking and next-best-action experiences.
Business Data
Events & APIs
Documents
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Pipelines
Feature Store
Data Quality
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Training
Evaluation
Model Registry
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Inference API
Application
Monitoring
Technology Stack
Flexible tools for the right machine learning workload.
Delivery Process
From business question to monitored production model.
01 · Discover
Define the business objective, success metrics, constraints and available signals.
02 · Prepare
Profile, clean, transform and validate the data pipeline.
03 · Experiment
Engineer features and compare suitable algorithms against meaningful baselines.
04 · Validate
Measure accuracy, robustness, bias, latency and business impact.
05 · Deploy
Expose the model through reliable batch or real-time inference services.
06 · Integrate
Connect predictions to applications, workflows, APIs and decision systems.
07 · Monitor
Track model performance, data drift, latency and operational health.
08 · Improve
Retrain and iterate as data, users and business conditions change.
Benefits
Built for measurable machine learning adoption.
Better Decisions
Use evidence-based predictions to support faster and more consistent decisions.
Operational Automation
Reduce repetitive analysis by embedding model outputs into workflows.
Personalized Experiences
Adapt products and content to individual customer behavior.
Scalable Inference
Serve predictions reliably across growing workloads and channels.
Reproducible ML
Track datasets, experiments, models and deployment versions.
Continuous Improvement
Monitor real-world performance and create feedback loops for retraining.
Industries
Machine learning across real business environments.
Finance
Risk scoring, fraud signals, forecasting and customer intelligence.
Healthcare
Operational prediction, document intelligence and imaging workflows.
Retail & eCommerce
Recommendations, demand forecasting, segmentation and personalization.
Manufacturing
Predictive maintenance, quality inspection and process optimization.
Logistics
Demand, route, capacity and delivery-time prediction.
SaaS & Technology
Usage intelligence, churn prediction, ranking and product analytics.
Quality & Responsible ML
Engineering discipline around every model.
Production ML needs more than a good metric. We consider data quality, reproducibility, access controls, explainability, monitoring and safe deployment throughout the lifecycle.
Data ValidationSchema, freshness, completeness and distribution checks.
Model EvaluationTask-specific metrics, baseline comparisons and validation datasets.
SecurityControlled data access, secrets management and protected inference endpoints.
MonitoringDrift, performance, latency and service health visibility.
GovernanceVersioned datasets, experiments, models and deployment history.
Why Cyanous
Machine learning with software engineering behind it.
Business-First Discovery
We start with the decision or workflow the model must improve.
Production Architecture
Models are designed for integration, reliability and maintainability.
End-to-End Delivery
Data, modeling, APIs, applications and deployment can be handled as one system.
Practical Model Selection
Choose methods based on data, accuracy, explainability and operating constraints.
Cloud-Ready
Deploy ML workloads with scalable infrastructure and automated pipelines.
Long-Term Support
Monitor and improve production systems as the underlying data evolves.
FAQ
Machine Learning questions.
Can Cyanous work with our existing data platform?+
Yes. ML solutions can be integrated with existing databases, warehouses, APIs, event streams and cloud platforms.
Do we need a large dataset to start?+
Not always. The right approach depends on the problem, data quality, target signal and required level of accuracy. An initial assessment can establish feasibility.
Can you deploy models into an existing application?+
Yes. Models can be exposed through APIs, batch jobs or embedded services and connected to web, mobile and enterprise applications.
How do you handle model drift?+
Production systems can monitor data distributions, model metrics and business outcomes, with retraining workflows triggered according to agreed criteria.
Can machine learning and generative AI work together?+
Yes. Traditional ML can provide scoring, prediction and ranking while generative AI can handle language, retrieval and interaction layers.
Turn your data into a smarter product.
Tell us what you want to predict, automate or optimize.