Predictive Analyticspredict the future
Predictive analytics tool
predictive analytics tool
Client
PredictAI
Industry
AI
Headquarters
Norway
Services
UI/UX Design
About project
PredictAI is a predictive analytics platform that empowers business analysts to build, deploy, and monitor forecasting models without requiring data science expertise.
Challenges & Solutions
Problem
Business analysts were blocked from building forecasting models by complex tooling, requiring data science support for every model iteration — slowing decision cycles significantly.
Solution
Zylo designed a self-serve predictive analytics platform where analysts configure models through guided workflows, monitor performance in real time, and act on forecasts through integrated dashboards.
Process
Discovery
Analytics use case mapping
Data source assessment
Analyst workflow research
Model complexity calibration
Business outcome alignment
Platform Architecture
AutoML pipeline design
Data connector layer
Model monitoring framework
Forecast delivery structure
Alert logic design
Implementation
Model validation
Analyst beta testing
Performance tuning
Enterprise rollout
System Architecture
A forecasting model library connects to business data sources through pre-built connectors, with an AutoML backend handling feature engineering, model selection, and deployment.
Interface Engineering
Model configuration wizards, forecast dashboards, and scenario simulation tools are organized in a logical workflow that guides analysts from data input to actionable predictions.
Predictive intelligence for every business analyst — no data science required.
MOBILE-FIRST
EXECUTION
Mobile forecast monitoring provides key prediction snapshots, model accuracy alerts, and threshold notifications for business decision-makers.
UI Kit
Component library covers forecast charts, model performance gauges, scenario comparison panels, prediction confidence intervals, and alert management cards.
AUTOMATION
FRAMEWORK
Feature engineering, model retraining, forecast updates, accuracy monitoring, and alert dispatching are fully automated through the analytics platform.
Design system
Results
84%
Average model forecast accuracy across use cases
7×
Faster model build time vs. manual development
+71%
Improvement in business decision speed
-59%
Reduction in data science dependency for forecasting
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