Predictive models that reach the people who act on them.

Forecasting, anomaly detection, and risk prediction — integrated into the dashboards and workflows your team already uses.

Start a Project Book a Free Call

01. Data Audit — We assess the quality, completeness, and relevance of your historical data before committing to any modelling approach. This surfaces problems early — missing periods, distribution shifts, label leakage — before they become expensive to fix.

02. Feature Engineering — Transform raw data into predictive features — lag variables, rolling statistics, calendar features, and domain-specific encodings. We test for leakage explicitly using a temporal split before any modelling begins.

03. Baseline Model — Train a simple, interpretable baseline — naive seasonal, linear regression — before anything complex. This gives you a meaningful benchmark and confirms the data is correctly prepared before investing in tuning.

04. Model Training & Tuning — Train and tune the right architecture for your task — XGBoost, LightGBM, Prophet, LSTM, or ensemble — using cross-validation with a temporal split. Hyperparameter search is logged and reproducible.

05. Integration & Serving — Surface model outputs in your existing systems — BI dashboard, application API, or scheduled database table. Output format, latency, and refresh frequency are agreed before build.

06. Drift Monitoring — Automated monitoring tracks input distribution drift and prediction accuracy over time. Alerts fire when drift crosses a defined threshold. Retraining pipeline is set up and documented.

The AI Development Lifecycle

Every system we build follows this pipeline — from raw data to a monitored, self-improving production deployment.

01Data Audit

Data Audit

We assess the quality, completeness, and relevance of your historical data before committing to any modelling approach. This surfaces problems early — missing periods, distribution shifts, label leakage — before they become expensive to fix.

Stage 1 of 6

Historical Data

Transactions · Events · Sensors

Data Assessment

Ready

Missing period map

Distribution check

Target variable confirmed

AI & Deep Learning Services We Offer

From custom model training and NLP to computer vision and edge AI — every solution is designed, built, and deployed for production.

Time-series models built on your historical transaction data — accounting for seasonality, promotional effects, and external signals where relevant. We scope what the data can support before committing to an approach.

Seasonal decompositionExternal signal integrationConfidence intervals included

Prophet Docs

Statistical and ML-based systems that surface outliers in operational, financial, and sensor data. We tune false-positive rates explicitly — a detector that fires constantly is not useful.

Threshold-free detectionExplainable alert outputFalse-positive rate tuning

Scikit-learn Docs

Classification models that identify at-risk customers, transactions, or assets before the outcome is visible. Outputs include calibrated probability scores, not just binary flags.

Calibrated probability scoresFeature importance reportSegment-level analysis

XGBoost Docs

Collaborative and content-based filtering models that personalise product, content, or action recommendations. Cold-start handling and online serving architecture are scoped from the start.

Cold-start handlingOnline & batch servingA/B test framework

Surprise Docs

Model outputs surfaced inside your existing BI tools — Tableau, Looker, Power BI — rather than a separate interface requiring a context switch. Outputs arrive via API, scheduled table, or direct connector.

No separate tool requiredScheduled refreshNative BI connector

NLP Services Computer Vision Edge AI Case Studies

AI Technology Stack & Frameworks

We select frameworks — TensorFlow, PyTorch, Hugging Face, and more — based on each project's requirements, not trends.

Modelling

scikit-learnXGBoostLightGBMstatsmodelsProphet

Deep Learning

PyTorchTensorFlowDartsNeuralForecast

MLOps

MLflowDVCWeights & BiasesBentoML

BI & Serving

LookerTableauPower BIFastAPIMetabase

Industries We Serve

Predictive analytics applied where historical data can reduce uncertainty in operational decisions.

Retail

Demand forecasting for inventory and replenishment, promotion uplift modelling, customer lifetime value, returns prediction.

Finance

Credit risk scoring, fraud probability, revenue forecasting, AML signal models, portfolio anomaly detection.

Logistics

Delivery time prediction, route demand forecasting, capacity planning, SLA breach early warning.

Manufacturing

Predictive maintenance scheduling, yield and defect rate forecasting, supply demand sensing, energy consumption prediction.

Telecommunications

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

Healthcare

Patient readmission risk, appointment no-show prediction, supply demand forecasting, operational capacity planning.

See all industries

Frequently Asked Questions

Common questions about custom AI model development, timelines, data requirements, and deployment.

For time-series forecasting, at least two to three full seasonal cycles gives a reasonable baseline. Less data is workable but produces wider confidence intervals and less reliable seasonality decomposition. We assess your specific data in discovery and tell you what is achievable before committing to a target.

That depends on how quickly your underlying patterns change. We set up monitoring to detect drift in input distributions and prediction accuracy — retraining is triggered by evidence rather than a fixed calendar. For stable domains, monthly or quarterly retraining is often sufficient.

Yes — we specifically design for integration into your existing tools rather than creating a separate interface. Model outputs are made available via API, scheduled database table, or direct connector to Tableau, Looker, or Power BI.

Yes. All model weights, training pipelines, feature engineering code, and documentation are transferred to you at project end. No ongoing licence fees and no dependency on our infrastructure to run inference.

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