AI predictive analytics

Predictive analytics is where telemetry becomes decisions. The difference between a dashboard and an operational analytics system is simple: a dashboard shows signals, but predictive analytics drives action with measurable accuracy, controlled false positives, and a lifecycle that survives real operations.

Squared Technologies builds bespoke predictive analytics for IoT and RF estates. We do not ship templates and we do not run “notebook theatre”. We engineer the end-to-end system: signal capture, validation, feature pipelines, model governance, and operational deployment so the outcome remains trusted outside the lab.

The foundation is a secure, attributable data plane. If you cannot prove what produced the data, how it moved, what changed, and who accessed it, you cannot safely automate decisions. We design identity, access control, and evidence trails so analytics has provenance, not assumptions.

Signal quality is the real bottleneck. RF and low-power telemetry is bursty, intermittent, and noisy. We engineer sampling, time alignment, calibration, missing-data behaviour, and baselines before model work begins. This prevents confident nonsense and makes anomalies and forecasts interpretable to operators.

We build feature pipelines that survive drift. That means deterministic schemas, windowing, baseline models, ground truth capture, and data contracts that make retraining safe. Predictive maintenance depends on these foundations because the model is only as stable as the pipeline feeding it.

The analytics layer is selected for the decision. We build anomaly detection, forecasting, health scoring, and failure prediction with honest validation: leakage control, error analysis, explainability, and false-positive budgets aligned to operational cost. If an alert is not actionable, it is noise.

Deployment is engineered as a lifecycle. We ship monitoring, drift detection, retraining triggers, versioning, rollout and rollback, and an evidence pack that ties performance back to acceptance criteria. The goal is repeatable operation across sites, not a one-off model.

For enterprise-grade deployments we commonly implement on AWS using patterns that support scale and assurance: authenticated ingestion, stream processing, durable storage, governed APIs, and managed ML workflows. For smaller or dedicated deployments we can deliver a lean sovereign stack using Postgres and hardened services without compromising governance. The footprint changes, the discipline does not.