Analytics & AI

Measurement you can trust and AI teams adopt.

Build measurement you can trust and AI your teams adopt. We design for explainability, observability, and safe use.

Our Offerings

Measurement Strategy

Dashboards and data quality/observability designed around decisions leaders make every week.

Applied ML & AIoT

Predictive maintenance, triage, and computer vision for real business problems.

MLOps & Change Plans

Model monitoring, user enablement, and guardrails for production use.

Trust‑by‑Design Principles

Explainability

Show the reasoning in words users understand.

Observability

Track accuracy, data quality, drift, and overrides.

Guardrails

Boundaries for autonomy, with clear escalation.

Adoption

Designed to augment people and fit existing workflows.

Typical Outcomes

>50% reduction in unplanned downtime through predictive maintenance

Material maintenance savings with 94% prediction accuracy

Decision-ready metrics leaders use weekly with fewer meetings

AI in production that teams actually use with measurable business impact

Our Approach

Frame

Align on outcomes and constraints; define success criteria and KPIs.

Simplify

Map the few choices that matter. Remove complexity that slows execution.

Prove

Pilot assumptions with baselines. Publish results and implications.

Scale

Embed governance and review rhythms so the plan stays live.

Artefacts You Keep

Measurement strategy with KPI trees and data governance frameworks

Dashboards with real-time visibility and automated alerting

Model cards with explainability, performance metrics, and monitoring

Data quality and observability checks with automated validation

MLOps pipelines with version control, testing, and deployment automation

User enablement materials and guardrails for safe AI adoption

Frequently Asked Questions

Ready to build trustworthy AI?

Start with an outcome sprint to identify your highest-impact analytics and AI opportunities.

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