We Engineer What Others Can't.

Challenge: Manual equipment tracking across construction sites is slow, inconsistent, and can't scale across multiple cameras and projects.

Solution: Object detection model trained on annotated site imagery to recognise heavy machinery such as excavators, cranes, concrete trucks, and piling rigs, supported by a quality-controlled annotation pipeline.

Outcome: Automated equipment identification from site camera feeds, enabling fleet tracking, utilisation analysis, and safety compliance monitoring.

Challenge: Manual review of construction QA submissions against specifications was slow, expensive, and prone to inconsistency.

Solution: Two-stage AI pipeline that automatically classifies work types, then runs spec-aware compliance analysis across a library of workflows and specification documents.

Outcome: Dramatic reduction in review time with structured audit reports, batch processing with cross-lot pattern detection, and automated deficiency notices.

Challenge: Waste identification and classification on environmental sites relies on manual inspection, which is slow, inconsistent, and difficult to scale across multiple locations.

Solution: Computer vision model trained on expert-annotated waste imagery, with a collaborative data pipeline where domain specialists classify images while the AI learns to detect and categorise waste types automatically.

Outcome: Automated waste detection from site imagery, enabling consistent classification at scale with real-time inference.

Challenge: Engineers spent hours searching through large specification libraries to find relevant clauses and cross-references.

Solution: RAG chatbot with hybrid search, cross-reference resolution, and drawing lookups. Answers are grounded in actual spec text with clickable citations.

Outcome: Instant, cited answers to specification queries with streamed responses and full chat history.

Challenge: Construction site cameras degrade silently. Obstructions, fog, lens flare, blur, and frozen frames go unnoticed until surveillance footage is needed.

Solution: Two-stage pipeline where computer vision detects multiple issue types, then AI verification cuts false positives. Detection thresholds auto-tune over time based on confirmation rates.

Outcome: Scheduled cloud deployment monitoring site cameras at regular intervals with per-camera baselines, trend detection, and zero-touch threshold calibration.

Challenge: Planning multi-day road inspection routes across council networks manually resulted in excessive travel time and missed roads.

Solution: Route optimisation engine that ingests road network data and generates efficient multi-day schedules with an interactive map interface for plan management.

Outcome: Optimised inspection schedules that minimise travel distance while respecting daily time limits, with data export and multi-plan support.

Challenge: Capturing road and asset condition data traditionally required specialised equipment, trained operators, and expensive processing. Results were inconsistent and difficult to compare across surveys.

Solution: Attach a standard camera to any vehicle and drive. Footage is uploaded and automatically processed into location-based, georeferenced imagery matched to the road network, with AI-driven analysis and an interactive web viewer for review, comparison, and export. Backed by active university research into how detection models generalise across different road materials and regions.

Outcome: Consistent, reproducible asset data capture deployed across road maintenance surveys, disaster recovery documentation, infrastructure audits, and construction monitoring without specialised skills or equipment.