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.

Start a Project Book a Free Call

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

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

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

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

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

06. Drift Monitoring: Automated drift detection triggers retraining when input distributions shift. Dashboard tracks accuracy over time.

How We Work

Every engagement follows defined phases — each delivering something tangible you can evaluate before we move forward.

Data Audit

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

Missing data map

Seasonality check

Target variable defined

INPUT

Historical Data

Quality · Completeness · Gaps

OUTPUT

Data Assessment

Step 1 of 6

What We Deliver

Specific capabilities and deliverables — built, tested, and handed over.

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

Seasonal decompositionExternal signal integrationConfidence intervals

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

Threshold-free detectionExplainable alertsLow false-positive tuning

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

Calibrated probabilitiesFeature importance reportBias audit

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

Cold-start handlingOnline & batch servingA/B test framework

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

Native BI connectorScheduled refreshNo separate tool required

Technology Stack

We select tools based on each project's requirements — not trends.

Modelling

scikit-learnXGBoostLightGBMProphetstatsmodels

Deep Learning

PyTorchTensorFlowKerasDarts

MLOps

MLflowDVCWeights & BiasesBentoML

BI & Serving

LookerTableauPower BIFastAPIMetabase

Industries We Serve

Sectors where we have applied predictive analytics — each with specific requirements we understand.

Retail

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

Finance

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

Logistics

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

Manufacturing

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

Telecommunications

Churn prediction, network anomaly detection, usage forecasting, lifetime value modelling.

See all industries

Frequently Asked Questions

Common questions about this service, process, and what we hand over at the end.

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.

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.

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.

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.

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.

Book a Free Call Contact Sales