AI & AUTOMATION / SYSTEM NODEPredictive Analytics

Predictive models that reach the people who act on them.

We build forecasting, anomaly detection, and decision support models — and integrate them into the dashboards and workflows your team already uses.

Delivery evidence
Ownership, deployment boundary, acceptance criteria, and handover are defined per engagement.
Data IngestHistorical + liveFeature Eng.Transform & enrichTrain/TestTemporal splitTrainXGB / LSTM / etc.ValidateMAPE / AUC / F1RegisterModel registryServeBI / API / webhookMonitorDrift & accuracy
MODEL LIFECYCLE
WHAT WE BUILD

Predictive Analytics, engineered as an operating system.

Scope follows the real workflow, environment, failure modes, and ownership model rather than a fixed vendor package.

01

Demand & Sales Forecasting

Time-series models accounting for seasonality, external signals, and promotional effects — integrated into your planning tools.

  • Seasonal decomposition
  • External signal integration
  • Confidence intervals
02

Anomaly Detection

Statistical and ML-based systems that flag outliers in operational, financial, and sensor data in real time with explainable alerts.

  • Threshold-free detection
  • Explainable alerts
  • Low false-positive tuning
03

Churn & Risk Prediction

Classification models identifying at-risk customers, transactions, or assets before the outcome becomes visible — with probability scores.

  • Calibrated probabilities
  • Feature importance report
  • Bias audit
04

Recommendation Systems

Collaborative and content-based filtering models that personalise product, content, or action recommendations at scale.

  • Cold-start handling
  • Online & batch serving
  • A/B test framework
05

BI Dashboard Integration

Surfacing model outputs inside Tableau, Looker, Power BI, or Metabase rather than requiring a separate interface or context switch.

  • Native BI connector
  • Scheduled refresh
  • No separate tool required
DELIVERY SEQUENCE

From unknowns to production evidence.

01

Data Audit

Assess the quality, completeness, and relevance of your historical data before committing to any modelling approach.

Historical Data Data Assessment
02

Feature Engineering

Transform raw data into predictive features — lag variables, rolling statistics, external signals, and domain-specific encodings.

Raw Dataset Feature Store
03

Baseline Model

A working prototype with measurable performance — giving you a concrete benchmark before full development investment.

Feature Store Baseline Report
04

Model Training

Select and train the right architecture — XGBoost, LightGBM, Prophet, LSTM, or ensemble — tuned via cross-validation on your data.

Feature Store Best Model
05

Integration

Surface model outputs inside your existing BI tools, databases, or applications via API, scheduled query, or webhook.

Best Model Live Predictions
06

Drift Monitoring

Automated drift detection triggers retraining when input distributions shift. Dashboard tracks accuracy over time.

Live Predictions Stable Model
TECHNOLOGY SURFACE

Tools selected for the system, not the trend.

Technology follows environment, operating constraints, team ownership, and long-term maintainability.

01Modelling

scikit-learn · XGBoost · LightGBM · Prophet · statsmodels

02Deep Learning

PyTorch · TensorFlow · Keras · Darts

03MLOps

MLflow · DVC · Weights & Biases · BentoML

04BI & Serving

Looker · Tableau · Power BI · FastAPI · Metabase

WHERE IT FITS

The same capability changes by domain.

01

Retail

Demand forecasting, dynamic pricing, recommendation engines, returns prediction, stock optimisation.

02

Finance

Fraud scoring, credit risk, revenue forecasting, portfolio anomaly detection, AML signals.

03

Logistics

Delivery time prediction, route demand forecasting, capacity planning, delay anomaly detection.

04

Manufacturing

Yield prediction, predictive maintenance scheduling, defect rate forecasting, supply demand sensing.

TECHNICAL FAQ

Questions worth resolving before delivery.

01How much historical data do we need for forecasting?+

For time-series forecasting, at least 2–3 full seasonal cycles gives a reasonable baseline. Less is workable but produces wider confidence intervals. We assess your specific data in discovery.

02How often does the model need retraining?+

That depends on how quickly your underlying patterns change. We set up drift monitoring so retraining is triggered by evidence rather than a fixed calendar.

03Can predictions appear in our existing BI dashboards?+

Yes — this is a specific design goal. We surface outputs via API, scheduled database table, or direct BI connector rather than creating a separate interface.

04How do you evaluate forecast accuracy?+

We use task-appropriate metrics — MAPE or WMAPE for demand, AUC for classification, RMSE for continuous outputs — agreed before training begins. We also measure against a naive baseline so improvement is meaningful.

OSYSTIC / PROJECT CHANNELOPEN
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Need better predictions?

Tell us what you are trying to forecast or detect. We will review your data and give you an honest picture of what is achievable.

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