CONNECT. VALIDATE. ANALYSE. INTERPRET.
RAUZ Environmental & Engineering Monitoring Platform
RAUZ connects monitoring, survey, environmental and InSAR data in one engineer-reviewed platform for QA/QC, analytics, alerts, AI-assisted review and automated reporting across ground, infrastructure and natural systems.
RAUZ Platform
A monitoring-intelligence layer between field evidence and technical decisions.
The RAUZ Platform brings monitoring, survey, environmental and remote-sensing evidence into one review framework. It is built for projects that already have useful instruments, field teams or data systems but need stronger data assurance, cross-source analysis, anomaly investigation, engineering interpretation and repeatable reporting.
It connects available monitoring evidence, checks whether the data are fit for the intended review, analyses change across time and space, compares independent sources, supports engineer-reviewed interpretation and turns the result into alerts, technical findings and controlled reports.
Keep useful field systems in place.
The platform is designed to work above existing instruments, survey workflows, logger exports, environmental systems and suitable third-party data rather than make equipment replacement the default.
Review evidence together.
Measurements are more useful when their timestamps, baselines, locations, project events and neighbouring observations can be compared in the same technical context.
Automation prepares; accountable review decides meaning.
Repeatable checks and report preparation can be automated. Consequential conclusions remain tied to the evidence, project context, limitations and the responsible technical review.
Above measurement. Before decision.
Platform Architecture
One technical chain from observation to reviewed output.
RAUZ separates acquisition, data trust, analytics and interpretation so that a project can see where each conclusion came from and where uncertainty remains.
Field instruments, manual readings, survey, GNSS, environmental sensors, InSAR, weather information and other project-approved sources.
Bring agreed files, exports, feeds and metadata into a consistent structure without assuming every source uses the same format, unit or timestamp convention.
Check provenance, completeness, continuity, baselines, units, references, duplicates, abnormal jumps, stale channels and other conditions that affect confidence.
Review magnitude, direction, trend, rate, acceleration, change points, spatial behaviour, thresholds and relationships between independent observations.
Relate the evidence to geology, groundwater, climate, construction sequence, asset behaviour, environmental conditions and the question the project is trying to answer.
Present findings through dashboards, exception lists, alerts, technical review, automated reports and project-specific decision-support outputs.
Architecture principle: data availability is not the same as data credibility, and a trigger event is not the same as an engineering conclusion. The platform keeps those stages separate so that automation does not hide the reasoning.
Platform Modules
Eight connected capabilities, each solving a different part of the monitoring problem.
The Platform page is the hub. Each module below has a narrower technical role and should be read together with the project’s monitoring objective, data availability and engineering responsibilities.
Analytical Core
Coordinates data validation, trend screening, anomaly review, cross-source correlation, confidence assessment and reviewed technical interpretation.
Explore Analytical Core →Data Integration
Structures information from monitoring, survey, environmental and remote-sensing sources so that independent datasets can be reviewed together.
Explore Data Integration →Data Quality & QA/QC
Checks source identity, completeness, timestamps, units, baseline behaviour, abnormal records and other conditions that influence data confidence.
Explore Data Quality →Monitoring Analytics
Reviews trends, rates, thresholds, exceptions and relationships between observations so attention can be focused on behaviour that merits technical review.
Explore Monitoring Analytics →InSAR Intelligence
Adds satellite-derived ground-motion evidence where coverage, geometry, reference frame, acquisition history and data quality are suitable for the question.
Explore InSAR Intelligence →Alert Intelligence
Reviews the evidence behind thresholds, trigger exceedances and anomalies instead of treating every automatic notification as a complete technical conclusion.
Explore Alert Intelligence →AI-Assisted Review
Supports repetitive screening, comparison, retrieval and draft preparation while preserving traceability and engineer review for consequential conclusions.
Explore AI-Assisted Review →Automated Reporting
Automates repeatable charting, threshold overlays, QA/QC checks, exception lists and report assembly while keeping engineering commentary under review.
Explore Automated Reporting →Data Inputs
The platform starts with evidence the project can trace.
RAUZ can work with different monitoring families, but the usefulness of each source depends on metadata, reference systems, units, timing, maintenance history and the physical question being investigated.
Ground & groundwater
Inclinometer, piezometer, groundwater, settlement, extensometer, load and other geotechnical observations where they are relevant to the project mechanism.
Survey & positioning
Total-station observations, GNSS, levelling and other survey records can provide independent spatial evidence when the control network and references are understood.
Asset response
Tilt, crack or joint movement, strain, load, displacement, vibration and other response measurements where their installation and interpretation are defined by the project.
Water, weather & site conditions
Rainfall, water level, soil-water information, weather, noise, vibration and other environmental observations can help explain or challenge a suspected mechanism.
InSAR & spatial products
Satellite-derived displacement and velocity can extend the spatial and historical context of ground movement where the remote-sensing method is technically suitable.
Project records
Drawings, monitoring plans, trigger criteria, construction sequence, ground information, environmental records, maintenance logs and issue history are often essential to interpretation.
Project Context
A global platform still has to understand the local ground, climate and project stage.
This Platform page does not assign generic geology, groundwater or climate to a project. RAUZ uses official project information and relevant public-authority data for the actual location because the same numerical movement can have different meaning under different physical conditions.
Geology & stratigraphy
Soil and rock units, fill, weathering, weak layers, interfaces and excavation or foundation geometry define which movement mechanisms are credible.
Groundwater & pore pressure
Water levels, pressure changes, dewatering, recharge and drainage conditions may need to be reviewed alongside deformation rather than in a separate data silo.
Rainfall, weather & season
USGS landslide monitoring is an official example of combining precipitation, soil-water, groundwater-pressure and movement observations where hydrological conditions influence slope behaviour.
Official USGS source ↗Stage, sequence & events
Excavation, tunnelling, piling, loading, dewatering, blasting, demolition or adjacent work can provide the event timeline needed to explain a monitoring response.
Geometry & consequence
A tunnel, railway, slope, bridge, dam, building or natural system has a different response mechanism and decision pathway. Analytics must stay tied to the asset question.
Criteria & responsibilities
Approved trigger levels, reporting periods, alarm routes, data ownership and sign-off responsibilities should come from the project’s controlled documents rather than a platform default.
Localisation rule: when RAUZ reviews a defined project in the South Caucasus, EU, UK, Middle East or Africa, the technical discussion should use the official geology, environmental, climate and infrastructure information for that specific project location. This global page deliberately does not invent a site condition.
Operational Workflow
From incoming data to a technical finding that can be traced.
A practical deployment can be narrow or broad, but the review logic should remain visible. RAUZ uses a staged workflow so that data processing, automated screening and professional interpretation are not blurred together.
Set the monitoring question, asset, decision need, data owners, reporting frequency and responsibility boundary.
Map instruments, files, feeds, locations, units, timestamps, references and relevant project records.
Screen provenance, completeness, baselines, continuity, duplicate records, abnormal values and source health.
Review trends, rates, thresholds, spatial patterns, exceptions and relationships between data sources.
Relate credible evidence to the ground, environment, construction stage, asset behaviour and documented mechanism.
Issue reviewed dashboards, alerts, exception lists, technical commentary, reports or another agreed decision-support output.
Project-specific implementation: “platform available” does not mean every third-party system is automatically connected without configuration. Each data source still requires an agreed interface, metadata, units, timestamps, access rights and validation route.
Platform Outputs
Make the exception visible, but keep the supporting evidence behind it.
RAUZ outputs are designed to shorten the distance between a large monitoring dataset and the small number of issues that actually need technical attention.
Monitoring overview
Current data availability, important changes, open issues, instrument-health exceptions and areas requiring review.
Trend & correlation views
Time series, rates, threshold overlays, spatial context, construction annotations and comparison across independent observations.
Anomaly investigation
Focused review of jumps, drift, missing records, baseline changes, trigger exceedances, inconsistent instruments or unexpected spatial behaviour.
Prioritised event review
Separate network or data events from monitoring thresholds and from the later engineering judgement about credibility and significance.
Automated recurring reports
Repeatable charts, tables, threshold checks, exception lists and report assembly with controlled engineer-reviewed commentary.
Technical interpretation
A concise statement of what the evidence shows, what it may mean, what remains uncertain and what should be checked or escalated next.
Applications
One intelligence architecture across different monitoring domains.
The platform logic remains consistent while the measurements, physical mechanisms and review criteria change by domain.
Geotechnical and asset monitoring
Tunnels, excavations, rail, roads, bridges, slopes, dams, buildings, mining and other assets where deformation, groundwater, load or vibration data need context.
Environmental monitoring intelligence
Water, weather, noise, vibration, air and other environmental datasets can be reviewed with location, baseline, seasonality and project events rather than as isolated channels.
Soil, water & microclimate evidence
Soil, irrigation, rainfall, weather, land and suitable remote-sensing observations can use the same integration, QA/QC, analytics and reporting architecture with domain-specific interpretation.
Single platform, different engineering questions: RAUZ does not treat agriculture, environment and geotechnical monitoring as interchangeable disciplines. The shared layer is data integration and monitoring intelligence; the interpretation rules remain domain-specific.
Official Industry Context
Major monitoring platforms are converging on integrated data, analytics, alerts and contextual review.
The examples below are drawn only from the providers’ official websites. They are not RAUZ projects and do not imply partnership, endorsement or commercial association. They show the wider industry direction that RAUZ is designed to sit alongside and extend through independent, cross-source interpretation.
iTwin IoT
Bentley describes iTwin IoT as a cloud-based infrastructure monitoring platform that centralises sensor data, analytics and alerts, supports manual and automated data, and adds maps, 3D context and reporting.
Official Bentley source ↗GeoMonitoring
Hexagon presents GeoMonitoring as a safety-critical web platform covering the monitoring cycle from field-data collection through interpretation, with multi-sensor access, interactive 3D visualisation, trend identification and alerts.
Official Hexagon source ↗Trimble 4D Control
Trimble describes T4D as the core of a monitoring project, gathering measurements, managing and analysing monitoring data and alerts, and sharing real-time analysis with stakeholders.
Official Trimble source ↗MLSuite
Canary Systems describes MLSuite as a vendor-independent platform for collecting, managing, displaying and analysing automated and manually collected monitoring data, with cross-referencing, alarming, trend recognition and reporting.
Official Canary Systems source ↗CMT Cloud
Worldsensing describes CMT Cloud as a unified platform for managing monitoring networks, devices and data across projects, with network visibility, workflow processing and integration to third-party software.
Official Worldsensing source ↗GeoMoS
Leica states that GeoMoS can connect Leica and third-party monitoring sensors or software, combine geodetic, geotechnical and environmental data, perform validation and analysis, and support reporting and notifications.
Official Leica source ↗RAUZ’s position in this landscape: the platform does not need to own the field sensor network to add value. Its role is to connect suitable evidence, test data credibility, compare independent sources, support interpretation and create a traceable route from measurement to reviewed technical output.
Why RAUZ
Built around interpretation, not around forcing one hardware ecosystem.
RAUZ is the environmental intelligence and monitoring platform of Rauz Caucasus LLC in Tbilisi, with a remote-first delivery model for projects across the South Caucasus, European Union, United Kingdom, Middle East and Africa.
- Vendor-neutral integration around project-accessible data.
- Monitoring data QA/QC before interpretation.
- Geotechnical, environmental, survey and remote-sensing evidence in one review architecture.
- Trend, rate, change-point and cross-source analysis.
- InSAR used as a complementary spatial evidence layer where suitable.
- Alert review that separates threshold events from engineering significance.
- AI-assisted processing without removing accountable technical review.
- Automated reporting with traceable source data and review boundaries.
- Independent review can sit above a contractor or client monitoring system.
- Remote-first technical delivery while local field teams remain in place.
Technical & Responsibility Boundaries
A useful platform should make its limits visible.
The platform can strengthen data handling, analysis and review, but it does not turn incomplete evidence into certainty or remove contractual and professional responsibilities from the project team.
Repeatable analytical work
- Data intake and normalisation
- QA/QC and completeness checks
- Trend, rate and threshold calculations
- Anomaly triage and cross-source comparison
- Alert evidence review
- Chart, table and report preparation
- AI-assisted retrieval and drafting
- Engineer-reviewed interpretation
Consequential project authority
- Whether an asset is safe
- Whether work must stop or continue
- Whether a trigger should be changed
- Whether a measurement mechanism has been proven
- Which remedial works must be adopted
- Statutory or Engineer-of-Record approval
- Contractual instruction unless expressly appointed
- Emergency-response authority
Important: monitoring interpretation is only as strong as the available evidence. Missing metadata, uncertain baselines, poor reference control, inadequate spatial coverage, incompatible timestamps or unavailable project context should be reported as limitations rather than hidden by software.
Frequently Asked Questions
Questions clients usually need answered before connecting a monitoring platform.
Is the RAUZ Platform a replacement for our current monitoring system?
Can RAUZ work with different sensor brands and data sources?
What is the difference between the Platform and Monitoring Intelligence?
Can InSAR data be used with ground instruments?
Does RAUZ use AI in the platform?
Can the platform automate monitoring reports?
Can the platform be used for environmental as well as geotechnical monitoring?
What is needed to start a platform discussion?
Official Reference Library
Public technical context used for this Platform page.
External references below are first-party or public-authority sources. They provide industry context only; they do not represent RAUZ projects, partnerships or endorsements.
Bentley Systems — iTwin IoT
Official platform description covering sensor-agnostic data integration, time-series analytics, alerts, remote sensing, reporting and AI-enabled workflows.
Open Bentley source ↗Hexagon — GeoMonitoring
Official platform description covering the monitoring cycle from field data collection to interpretation, with multi-sensor visualisation and trend identification.
Open Hexagon source ↗Trimble — 4D Control
Official product material describing automated movement detection, multi-sensor monitoring, data analysis, alarms and real-time information sharing.
Open Trimble source ↗Canary Systems — MLSuite
Official software material describing vendor-independent monitoring-data collection, management, visualisation, analysis, alarming and reporting.
Open Canary Systems source ↗Worldsensing — CMT Cloud
Official product material covering centralised management of networks, devices and monitoring data, plus workflow processing and third-party integration.
Open Worldsensing source ↗Leica Geosystems — GeoMoS
Official material describing connection to Leica and third-party sensors, sensor fusion, validation, analysis, alarming, visualisation and reporting.
Open Leica source ↗U.S. Geological Survey — Landslide Monitoring
Official public-agency example of combining rainfall, soil-water, groundwater-pressure and movement observations in landslide monitoring.
Open USGS source ↗RAUZ — Quality & Data Assurance
RAUZ first-party material on provenance, QA/QC, traceability, uncertainty and the separation of measured evidence from interpretation.
Open RAUZ framework →Discuss the Platform
Already have monitoring data but need a stronger intelligence layer?
Send RAUZ a sample dataset, monitoring report or short system description. The first discussion can identify what data are available, what can be connected now, which QA/QC and analytical checks are useful, and where independent engineering or environmental review should remain explicit.
What to share
A sample dataset or report, instrument register, data-export method, monitoring objective, trigger table, relevant drawings and the main issue your team is trying to understand or automate.
What RAUZ can define
Data-source fit, integration route, required metadata, QA/QC rules, analytical workflow, alert and reporting logic, review responsibility and a practical first implementation scope.