The building blocks of a geospatial assurance system.

A geospatial platform for bridges, culverts, tunnels, retaining walls and operational structures. Connects asset identity, location, examination history, engineering reviews, risk scores and recommendations into a single navigable system.

Built around a clear data model: Asset → Exams → Reviews → Recommendations → Evidence → Audit. Each layer is explicitly modelled and linked, so it is always possible to trace a risk score back to the examination evidence and engineering judgement that produced it.

Modules

A structured system for engineering reviews where reviewers assess examination evidence, photographs and documents, record risk and severity, and create recommendations. Designed to support — not constrain — engineering judgement.

The workbench is designed so that a reviewer sees the asset record, all linked examination evidence and any prior recommendations in a single interface. Risk decisions are recorded with the rationale, not just the score.

Modules

A system for generating audit-ready evidence packs for individual assets, routes, regions or whole programmes. Designed to support regulatory review, internal audit and programme assurance.

Evidence packs are assembled on demand from the underlying data model — not manually compiled. This means they always reflect the current state of the record and can be regenerated if new information is added.

Modules

A future geospatial overlay layer that adds environmental and climate risk context to the asset map. Designed to help prioritise inspection and intervention at assets with the highest environmental exposure.

Environmental risk context changes the prioritisation picture. An asset with a moderate structural risk score but located in a flood zone or adjacent to a known scour site may warrant earlier intervention than its structural score alone would suggest.

Modules

A future-ready data model for recording bridge strikes, flood and scour events, structural damage reports and emergency inspections. Designed to connect incident records back to asset and examination history.

This module does not need to be customer-facing initially. It can begin as an internal recording and tracking system, with the map and dashboard views added once the data model is established and populated.

Modules

Using historic review baselines — such as DER-style reviews — to compare what was flagged historically with what has changed. Enables structured assurance that historical recommendations have been acted on and that risks have been managed.

The key questions this answers: Which historical recommendations were completed? Which assets remain at high risk? Which risks have been demonstrably reduced? Which evidence is missing? Which assets need a current-state review? These questions are hard to answer from static records alone.

Modules

Careful, targeted use of AI to support engineering analysis — not to replace engineering judgement. AI assistance is used to accelerate repetitive analytical tasks and surface patterns that are difficult to detect manually.

All AI outputs are presented as suggestions for review, not as decisions. The system flags, clusters and summarises — the engineer decides. This matters particularly in safety-critical contexts where the reasoning behind a risk decision must be defensible and human-authored.

Modules