Data Analytics & Predictive AI services

Less than most teams think for a first model, but it must be relevant and reasonably clean. Churn models need a history of customers who left; forecasting needs enough seasons to learn from. The feasibility study answers this concretely for your case before you commit to a build.

Time-aware validation, honest baselines, confidence intervals and a written model card. If a model does not beat the simple baseline meaningfully, we say so and recommend fixing data or process instead. Accuracy is reported on held-out data, not training data.

Not always. Many engagements start with a targeted analysis on exported data, and we only propose infrastructure once the value is proven. When you do need pipelines, we build them with Airflow or Prefect into Postgres, BigQuery or Snowflake. A modest, well-governed warehouse usually pays for itself quickly once two or more teams depend on the same numbers.

Yes. Churn scores can land in Salesforce or HubSpot, fraud alerts in a review queue, forecasts in your planning system or BI dashboards. We deliver predictions where decisions are made rather than in a separate report. Each integration includes a score explanation, so a salesperson or analyst understands why a record was flagged and what to do next.

We optimise for the business trade-off, not just accuracy: tune thresholds against the cost of false positives, use explainable features so reviewers understand alerts, and add feedback loops from analyst decisions so the model improves. We also report the false-positive rate as a first-class metric alongside catch rate, because both drive the real cost of the system.

Only if it is monitored. Customer behaviour, prices and fraud patterns drift, so we ship monitoring and a retraining playbook with every model, and can operate it for you under a retainer. A retraining cadence (monthly for fast-moving fraud patterns, quarterly for stable forecasts) is agreed at handover, with alerts for anything that drifts sooner.