Structural Integrity Intelligence for Commercial Vessels

Executive Summary

This document defines a product for commercial vessel structural integrity assessment: a multi-modal structural integrity intelligence platform. An autonomous robotic system (aerial platforms rated for hazardous confined spaces) captures repeatable high-resolution imagery and targeted ultrasonic thickness measurements (UTM) inside ballast tanks and cargo holds. Reconstruction and fusion software builds persistent 3D context tied to class-approved structural references. An analysis layer informed by surface physics, corrosion science, and class rules ranks findings by structural significance and predicted progression rather than visual severity or arbitrary spot sampling alone. Because each finding is tracked over time and compared against vessel-class-wide degradation baselines, the platform forecasts how corrosion and thickness loss will evolve — shifting surveys from reactive to predictive. Final survey decisions and class sign-off remain with certified surveyors.

The initial deployment targets vessels covered by IACS UR-Z10 close-up survey and thickness measurement requirements (oil tankers, chemical tankers, bulk carriers, etc.), through partnerships with classification societies (DNV, ABS, Lloyd’s Register and others) that provide access to structural models, class drawings, survey procedures, and historical thickness databases. Access to this engineering and class data is what defines the product. It enables inspection routes derived from actual tank geometry, risk scoring that accounts for load paths and corrosion-prone details, and class-level statistical baselines drawn from aggregated survey records across hundreds of vessels. No third-party robotic vendor has this integrated dataset without equivalent class or shipyard cooperation.

The founding team consists of three domain specialists whose expertise maps directly onto the requirements of AUTOASSESS Challenge 5. A mathematician with active work in optimal control, degradation modeling, and fleet-wide Bayesian inference contributes route optimization for limited robot endurance, corrosion progression models, and statistical prediction of when zones will reach class limits. A physicist contributes physically grounded computer vision and photometric/spectral surface models for corrosion and coating assessment, plus measurement uncertainty quantification for UTM fusion. An aerospace-turned-maritime engineer with CAD, robotics, and structural experience contributes the integration layer that maps robotic data to class structural zones, interprets findings against IACS rules and fatigue-critical details, and ensures outputs are surveyor-ready. These are not generic computer-vision or robotics roles: each requires sustained domain expertise that shapes the product from acquisition through class-compliant decision support.

The business model is SaaS per vessel per year, offered in tiers from baseline digital survey reporting through full structural integrity intelligence with class-wide analytics. Distribution runs through classification societies’ digital services and survey support organizations, which introduce the product to their existing network of operators and shipyards rather than requiring the startup to build relationships from scratch. The product is positioned as the assessment intelligence layer that complements and enhances the robotic hardware developed by AUTOASSESS and similar initiatives.

Phase 1 begins mid-2026 with a single-robot pilot on one vessel class (e.g., Handysize bulk carrier or MR tanker) at one shipyard or class-approved facility, with full surveyor confirmation on all flagged zones. The robotic payload is modular from the start: RGB + multispectral core for visual corrosion and coating assessment, optional radiometric thermal for subsurface triage, and mechanical provision for targeted UTM probe deployment on ranked hotspots. Phase 2 (2028) deepens quantitative fusion with calibrated UTM on all high-priority zones, adds hyperspectral pilot for objective coating-chemistry ground truth, and validates active thermography for early delamination detection. The architecture is class-agnostic and vessel-type extensible: the same platform applies to any IACS society and any vessel type through equivalent data partnerships.

Problem and Market Opportunity

The inspection problem — and why Challenge 5 exists

Commercial vessel structural surveys in ballast tanks and cargo holds remain overwhelmingly manual and hazardous. During special surveys, intermediate surveys, and close-up inspections required by class rules (IACS UR-Z10), certified surveyors must enter confined spaces, often on scaffolding or staging, to perform visual examination and ultrasonic thickness measurements. Each survey is treated as a fresh exercise: the surveyor documents what is visible and measures at selected spots, but that record is rarely registered to precise structural geometry, rarely compared systematically against prior surveys of the same vessel, and almost never aggregated across sister vessels or the class fleet for trend analysis.

This process has four critical weaknesses that AUTOASSESS Challenge 5 directly targets:

1. Dangerous and access-constrained. Ballast tanks and cargo holds are confined, poorly lit, often corroded, and may contain residual hazardous atmospheres. Human entry carries well-documented risks; the industry goal (explicit in AUTOASSESS) is to remove surveyors from these spaces entirely for routine data collection.

2. Fragmented data streams. Visual inspection and UTM are performed as parallel or sequential activities. Visual imagery (even with AI defect detection) identifies surface corrosion, coating breakdown, and cracks but provides no depth information. Ultrasonic readings give quantitative remaining thickness but lack spatial context and correlation with visual patterns. The result is incomplete evaluations, missed degradation mechanisms, and reports that require hours of manual reconciliation.

3. Non-repeatable and non-persistent. Two surveyors examining the same tank on the same vessel will frame, light, and document findings differently. There is no persistent digital twin that accumulates visual + thickness history per structural zone across multiple surveys and vessels.

4. No class-level or fleet learning. Each vessel is surveyed in isolation. Patterns that would be visible across hundreds of sister ships — zone-specific corrosion rates by operating profile, coating performance by cargo type, repeat problem details at manholes or hopper knuckles — remain invisible because data never leaves individual survey reports and is never geometrically registered to a common structural model.

AUTOASSESS Challenge 5 exists precisely because current robotic inspection prototypes still inherit this fragmentation: they can deliver imagery and limited UTM, but they lack the unified methodology, expert-infused models, historical-data fusion, and class-compliant outputs that turn raw robotic data into actionable structural integrity intelligence.

Why now

The hardware for autonomous robotic inspection of confined vessel spaces has matured rapidly. Projects such as AUTOASSESS, and parallel commercial developments, demonstrate GPS-denied aerial robots and crawlers that can operate safely in ballast tanks and cargo holds, capturing high-resolution imagery and deploying UT probes under remote supervision. This commoditization of capture shifts the value from the robot itself to what happens after the data is collected: precise structural registration, multi-modal fusion (imaging + UTM), degradation modeling, expert knowledge integration, and class-rule-mapped reporting. The competitive frontier is no longer “can a robot enter a tank” but “what does the combined visual + quantitative dataset tell you that a traditional survey cannot — and how does it stay within class acceptance?”

Market opportunity

The addressable market is every commercial vessel that undergoes class-mandated close-up surveys and thickness measurements: effectively the global fleet of oil tankers, chemical tankers, bulk carriers, and other vessels subject to IACS UR-Z10. The initial target is vessels already engaged with forward-leaning classification societies and operators participating in digitalization pilots, where the value of persistent digital records and predictive maintenance is most immediately recognized.

The per-vessel value of a structural integrity intelligence subscription comes from three sources: reduced human entry time and risk during surveys, consistent and class-traceable documentation that reduces rework, disputes, and audit friction, and — most importantly — predictive intelligence that allows operators and class to focus limited survey resources on zones that are actually deteriorating, while safely extending intervals or reducing scope on stable zones. As the platform accumulates inspection history across vessels and survey cycles, the dataset itself becomes a strategic asset for operators (fleet maintenance optimization) and for classification societies (design feedback, service bulletin planning, and rule development).

The product architecture is not tied to a single vessel type or class society. Any IACS partnership that provides equivalent access to structural models, survey procedures, and historical thickness databases enables the same platform on additional vessel segments. The initial program is the entry point; the long-term market is class-partnered structural integrity intelligence across commercial shipping.

Product: Multi-Modal Structural Integrity Intelligence Platform

The product has three layers. The first captures and registers multi-modal data inside confined spaces. The second builds structural understanding and longitudinal records from that data. The third converts understanding into class-compliant maintenance and survey decisions. Each layer adds value the one below it cannot provide alone, and together they deliver exactly the “unified methodology that merges visual and ultrasonic datasets, supported by real operational expertise and long-term historical records” required by Challenge 5.

Layer 1: Autonomous Robotic Inspection Cell for Confined Spaces

The physical product is a shipyard- or port-deployable inspection cell. One or more autonomous robotic platforms (aerial drones or hybrid crawlers rated for hazardous atmospheres, IP67+, ATEX/IECEx where required) operate in GPS-denied, dark, humid, and potentially explosive environments inside ballast tanks and cargo holds. One control station manages mission execution, real-time quality monitoring, and surveyor safety supervision. One route library, derived from class-approved structural models and 3D tank scans, defines repeatable standoff distances, high-overlap imaging paths, and prioritized UTM measurement sequences for each tank type and survey scope.

The Phase-1 hardware is a proven or near-proven autonomous confined-space inspection platform in the class of systems already demonstrated in AUTOASSESS and parallel projects — not a bespoke vessel design. Public and project evidence already shows that suitably equipped robots can complete comprehensive visual coverage of a ballast tank in a fraction of the time required for human staging, with no human entry for routine data collection. The differentiation is not in building a better robot. It is in what the software does with the combined imagery + UTM dataset after the robot returns.

The operating stack has five subsystems: the autonomous robotic platform(s), a positioning and obstacle-handling layer for confined-space navigation (LiDAR, SLAM, fiducial markers on class-known structure), a modular imaging payload (RGB/multispectral core + optional radiometric thermal), a deployable UTM probe or pulsed-eddy-current adapter for targeted quantitative confirmation, and a supervised safety layer with abort logic, gas detection, and surveyor takeover capability. Real ballast tanks impose weak texture, specular reflections from liquid residues, heavy occlusion by internal structure, CAD mismatch after repairs, and strict endurance limits. A deployable product therefore needs image-quality gates that detect blur, low contrast, or coverage gaps; missed-zone detection that triggers recapture; controlled LED lighting to manage glare and shadows; and quality-gated revisit logic rather than a single-pass assumption. UTM probe placement must be accurate to centimeters relative to the structural model so that thickness readings land on the exact plating the class rules intend to measure.

The Phase-1 inspection package covers the core requirements of a close-up survey plus thickness measurement scoping:

• Repeatable full-surface (or critical-zone) image capture with structural registration
• Digital reporting mapped to class structural zones and IACS UR-Z10 requirements
• Coating condition and corrosion mapping (visual + optional thermal)
• Lightning-strike / deformation screening where relevant
• Targeted UTM on AI-ranked hotspots (with manual confirmation fallback)
• Optional 3D local geometry for deformation or buckling assessment

Targeted UTM and local 3D fit as integrated sensing modes within the same product family. The baseline RGB + thermal path runs first; the system then recommends specific locations and standoffs for quantitative follow-up, maximizing the value of limited robot endurance.

Layer 2: Structural Integrity Software

Repeatable capture is necessary but not sufficient. The software layer converts recurring robotic surveys into a persistent structural integrity record that improves with every inspection and every vessel added to the class baseline.

The first software component is the structural integrity fusion layer. Rather than treating each survey as an isolated event, the product compares visual findings and UTM readings over time, aligns recurring structural zones using the class model, detects progression (corrosion spread, coating breakdown acceleration, thickness loss rate), and forecasts how each zone will evolve before the next special survey. Near-term outputs include:

• Repeat change maps and corrosion progression flags
• Thickness deviation maps color-coded against class minima (IACS UR-Z10 and vessel-specific)
• Early corrosion-risk indicators based on recurrent coating failure at known moisture-trap geometries (manholes, brackets, lap joints)
• Estimated time-to-limit forecasts that tell superintendents and class when a zone will require intervention or close-up re-inspection

These are survey-planning and maintenance cues for certified surveyors, not autonomous repair decisions. The mathematical foundation — corrosion degradation models calibrated to real vessel operating profiles, fleet-wide Bayesian inference that treats the entire class as a statistical prior, statistically grounded zone-risk scores — is where the founding team’s applied-mathematics expertise directly shapes the product.

This quantitative analysis is complemented by systematic codification of surveyors’ and naval architects’ tacit knowledge. Through structured interviews with veteran class surveyors and shipyard repair specialists, the platform captures subtle visual cues for early subsurface corrosion, pattern recognition heuristics for high-risk details, and judgment calls on when a finding warrants immediate engineering referral versus continued monitoring. With an aging surveyor workforce, embedding this hard-won knowledge in a searchable database converts individual experience into a durable class and operator resource. The result sharpens the platform’s risk models and triage logic with judgment that data alone cannot provide — exactly the “infuse human expert knowledge” requirement of Challenge 5.

The second software component is a 3D digital twin of structural integrity per tank and per vessel. The twin combines registered multi-modal imagery, class structural references, local geometry from 3D reconstruction or prior scans, historical UTM logs (5+ years where available), and expert annotations into a living model. Its purpose is historical comparison, progression forecasting, and survey-scope optimization. Over time it becomes the authoritative record where surveyors review recurring hotspots, compare pre- and post-repair condition, and see not only whether a finding is isolated, stable, or progressing but how fast — and when it is expected to reach class allowable limits based on the vessel-class baseline for that exact structural detail.

Layer 3: Inspection-to-Decision Engine

The third layer transforms analysis into operational and class action. Instead of outputting a list of detected defects and raw thickness readings, the product produces a priority-ranked worklist where each suspect zone carries a recommended next step: visual review by surveyor (remote or on-site), repeat robotic capture at closer standoff or different lighting, targeted UTM or PEC confirmation, manual dry-film-thickness or pit-depth gauging, or immediate engineering referral for repair assessment. Structural context from class data, thickness deviation from minima, and progression forecasts from vessel-class baselines jointly determine urgency — a zone whose thickness loss trajectory indicates it will breach class limits before the next special survey is flagged differently from one that fleet history shows will remain stable for years.

A detection and fusion layer grounded in corrosion science and surface physics combines image findings with geometric context, UTM values, and structural significance. A coating blister near a fatigue-critical bracket in a ballast tank carries different operational weight than the same blister on a non-structural longitudinal in a cargo hold. Class structural reference data and IACS rules make that distinction computable and auditable. The most useful Phase-1 output is not an isolated bounding box or thickness value but a localized review map that links each suspect region to its structural reference (frame, stringer, plating strake), measurement confidence, prior survey history, class rule citation, and recommended next action with forecasted intervention window.

AI in this product is tuned for triage, hotspot prioritization, and surveyor decision support — not autonomous class sign-off. Review literature and current class practice support AI-assisted detection and data fusion, but they also show weak generalization across coating types, repair histories, variable tank conditions, and different vessel operating profiles. The mitigation is disciplined ground-truth collection during the pilot (paired robotic + manual surveyor findings on the same zones), operator- and class-specific fine-tuning, first-principles priors from corrosion physics and class rules that reduce dependence on purely data-driven pattern matching, and explicit human review thresholds for every finding category. Final survey decisions and class recommendations remain with certified surveyors.

Inspection Boundaries and Confirmatory Metrology

A coherent product requires keeping modalities separate because they use different evidence, different instruments, different approval paths, and different class acceptance criteria:

1. Visual and multispectral coating/corrosion screening: image-based, the core Phase-1 product. Qualitative assessment of coating condition, corrosion extent, cracks, and deformation.

2. Thermal subsurface triage: radiometric infrared for delamination, disbonds, and moisture detection behind coatings or in lap joints; optional in Phase 1 as a modular payload.

3. Quantitative thickness measurement: targeted UTM (or PEC) on zones ranked by visual + thermal triage; converts visual flags into measured remaining-thickness values against class minima. Phase-1 uses manual or semi-automated probe deployment with full surveyor oversight; Phase 2 moves to integrated robotic deployment where validation permits.

4. Spectral coating-chemistry assessment: hyperspectral imaging (400–1000 nm) for objective differentiation of coating composition, degradation state, and early chemical indicators of substrate corrosion beneath intact coatings; Phase-2 bounded pilot on high-value zones to build ground-truth for AI training.

5. Local geometric assessment: 3D reconstruction or structured-light for deformation, buckling, or dent mapping on flagged structure; optional workflow.

Public and project evidence supports full-zone visual screening now, passive thermal as a mature optional augmentation, and targeted UTM as the essential quantitative step already required by class rules. The innovation is not inventing new sensors but fusing them natively inside the class structural model with expert and historical context — delivering the “combined qualitative and quantitative assessment” that Challenge 5 demands.

The practical workflow follows naturally:

1. Autonomous robotic mission executes visual + thermal scan of tank or hold.
2. AI triage (visual + thermal + structural zone importance) ranks zones for quantitative follow-up.
3. Robot or surveyor deploys UTM probe at highest-priority locations; readings are automatically registered to the structural model.
4. Fusion engine updates the digital twin, applies degradation models, and generates the ranked worklist with class references and forecasts.
5. Surveyor reviews the package remotely or on-site, confirms critical findings, and issues the class-endorsed report.

Competitive Landscape

The confined-space robotic inspection market for vessels is nascent but accelerating, driven by safety regulations, labor shortages, and projects such as AUTOASSESS. Relevant players and their positioning relative to Vesselinspect are as follows.

AUTOASSESS consortium and similar EU projects focus on the robotic hardware, navigation in GNSS-denied hazardous spaces, and basic AI for visual defect detection. Their strength is domain-specific engineering for ballast tanks and cargo holds. Their limitation (and the explicit gap Challenge 5 is designed to fill) is the absence of a production-grade, class-integrated assessment layer that fuses visual and UTM data with historical records, expert knowledge, and predictive models. Vesselinspect is positioned as the complementary software intelligence layer that turns AUTOASSESS-style robotic data into class-ready structural integrity intelligence.

Commercial robotic inspection vendors (e.g., those offering drone or crawler systems for tanks) typically provide visual mapping, basic AI annotation, and optional UT payload integration. Most still treat visual and thickness data as parallel outputs rather than a fused, zone-registered, longitudinal record. They lack deep class structural model integration and fleet-wide statistical baselines. Vesselinspect does not compete on robot hardware; it competes on the intelligence that sits between raw multi-modal data and class-accepted survey decisions.

Classification societies’ own digital initiatives are increasingly exploring remote inspection technologies (RIT) and AI-assisted tools. Societies already accept certain drone-captured imagery and phased-array UT under controlled conditions, provided human oversight and validation are maintained. Vesselinspect’s partnership model with societies positions it as an enabler that strengthens, rather than disrupts, existing survey workflows and approval pathways.

Traditional MRO / shipyard survey teams remain the baseline. Manual close-up surveys with staging, cherry-pickers, and handheld UT gauges are still the dominant method. The competitive position is not that manual survey is obsolete — it is that manual survey cannot economically produce the repeatable, zone-registered, multi-year, class-fleet-comparable structural integrity record that enables the predictive and scope-optimization value described in this document.

This product does not compete on robotic platforms. It competes on the unified assessment intelligence layer that Challenge 5 explicitly calls for — a layer that requires both class data access and the domain expertise to reason about corrosion dynamics, structural significance, and predictive maintenance within class rules.

Competitive Advantage: Class Data Access and Domain Expertise

The product requires two things simultaneously: access to classification society and shipyard engineering data, and the domain expertise to convert that data into structurally and regulatorily meaningful integrity intelligence. Neither alone produces the same product.

What class and shipyard access enables

A classification partnership provides four categories of data that no third-party robotic vendor can obtain independently at scale:

1. Vessel structural models and class drawings that define actual tank geometry, plating boundaries, stiffener locations, and critical details — enabling routes and measurement sequences derived from real engineering rather than photogrammetric reconstruction alone.

2. Class rule references and survey procedures (IACS UR-Z10, UR-Z7, vessel-specific thickness measurement guidelines, repair criteria) that map every structural zone to its minimum allowable thickness, inspection requirements, and acceptance standards.

3. Historical thickness databases (often 10–20+ years for in-service vessels) that provide the longitudinal baseline per zone and per vessel class — the essential input for degradation modeling and time-to-limit forecasting.

4. Fleet-wide anonymized survey records that, in aggregate, reveal statistical patterns invisible to any single operator or surveyor: which zones corrode fastest under which cargo/operating profiles, which coating systems perform best, which structural details are repeat problem areas across sister ships.

What domain expertise enables

The founding team brings three disciplines that are necessary to exploit class data and that generic robotics or computer-vision engineering does not cover.

Applied mathematics contributes corrosion degradation models calibrated to real vessel data, optimal route planning with formal coverage and endurance guarantees for confined spaces with limited robot flight time, and fleet-wide Bayesian inference that uses the entire class population as a statistical prior for individual-vessel risk assessment.

Physics contributes models of how light and infrared interact with coated steel surfaces under tank lighting and humidity conditions, spectral signatures for coating condition, and rigorous uncertainty quantification for fused visual + UTM measurements — reducing dependence on large labeled datasets and improving generalization across coating ages, cargo residues, and lighting environments.

Maritime structural engineering (adapted from aerospace CAD and robotics experience) contributes the domain knowledge to interpret which findings matter: understanding load paths in ballast tank and cargo hold structure, fatigue concentrations at brackets and hopper knuckles, the relationship between coating breakdown and underlying corrosion rate, and the exact mapping of robotic data to class survey task cards and repair recommendations. Credible engagement with class surveyors, superintendents, and shipyard repair teams depends on this domain fluency.

Why both are necessary

Class data without the right expertise is just files. A society or shipyard could license an existing robotic vendor’s software and supply it with structural models, but without physically grounded corrosion and coating models, degradation mathematics, and class-rule-aware risk reasoning, the result would be visual anomaly detection with thickness numbers added after the fact — still fragmented, still lacking the unified predictive intelligence Challenge 5 demands. Conversely, the team’s expertise without class data produces academically interesting algorithms with no regulatory grounding — models that lack the actual geometry, minimum thickness tables, historical records, and surveyor acceptance criteria to be operationally and class-relevant. The product requires both.

Business Model and Revenue

Subscription tiers

The primary revenue model is SaaS per vessel per year, offered in three tiers that reflect increasing depth of analysis and class integration.

Base tier: Autonomous robotic mission execution (or integration with existing robotic hardware), registered multi-modal imagery and UTM, 3D structural context, and digital reporting mapped to class zones and IACS UR-Z10 requirements. This tier replaces fragmented manual photography and ad-hoc thickness logs with a repeatable, indexed, class-traceable record. Its value is measured in reduced human entry time and risk, faster survey documentation turnaround, and audit-grade traceability.

Professional tier: Adds the structural integrity fusion layer: time-series comparison across surveys, corrosion progression maps, thickness deviation trends, vessel-class statistical baselines, and expert-codified risk scoring. This tier enables superintendents and class to make survey-scope decisions based on how integrity is actually evolving, not just what a single snapshot shows.

Enterprise tier: Adds the inspection-to-decision engine with priority-ranked worklists, forecasted intervention windows, API integration with class survey planning systems and operator maintenance management platforms, and full class-wide analytics across the operator’s fleet or the society’s broader population. This tier turns the product from a reporting tool into a predictive survey-optimization and integrity-management system.

Classification revenue share and partnership model

Distribution through classification societies’ digital services and survey support organizations involves a revenue-sharing arrangement. The society provides the distribution channel, customer relationships, structural data access, and regulatory endorsement pathway; the startup provides the product, the fusion and prediction platform, and ongoing development. The specific split is a commercial negotiation, but the alignment of incentives is straightforward: the society gains a differentiated digital service offering that increases the value and efficiency of its survey network while strengthening its data position; the startup gains distribution reach and class credibility that would otherwise take years of direct sales and approval processes to build.

Expansion revenue

Two additional revenue streams emerge as the platform matures. First, a per-event or per-zone confirmatory-metrology module that adds targeted UTM density, hyperspectral chemistry assessment, or 3D deformation mapping to specific survey events, priced as a supplement to the base subscription. Second, anonymized and aggregated class-wide intelligence — corrosion rates by zone type, coating performance by cargo profile, effectiveness of repair strategies — licensed back to the classification society’s engineering and rule-development teams for design feedback and service bulletin planning.

Go-to-Market and Customer Acquisition

Phase 1: Prove value at one shipyard / class facility

The first deployment is a pilot program at a single shipyard or class-approved survey facility, introduced through the classification partnership, operating on one vessel class (e.g., a series of Handysize bulk carriers or MR product tankers). The goal is not immediate revenue. It is operational and regulatory proof: demonstrating that the robotic cell produces repeatable coverage and quality UTM placement inside real ballast tanks and cargo holds; that the fused digital record is useful and trusted by surveyors; that the AI triage and prioritization reduce rather than add workload; and that the structural risk scoring and class-rule mapping produce outputs that surveyors and class can endorse. The pilot runs with full manual/surveyor confirmation on every flagged zone and every UTM reading. Nothing is accepted on the basis of automated analysis alone.

During the pilot, the product team collects paired data — robotic findings alongside traditional surveyor findings on the same zones — to build the ground-truth dataset that validates detection performance, UTM placement accuracy, fusion quality, and false-positive/false-negative rates. This dataset is the foundation for every subsequent claim about accuracy, reliability, and class acceptance.

Phase 2: Distribute through classification network

Once the pilot demonstrates measurable value — reduced human entry time and risk, improved documentation quality and traceability, surveyor acceptance of the fused digital record, and preliminary evidence that prioritized zones align with actual degradation — the classification society’s survey support organization becomes the distribution channel. The product is offered to the society’s network of operators, shipyards, and superintendents as a digital service, bundled with existing survey agreements or sold as a standalone subscription. The society’s existing customer relationships, technical support infrastructure, and surveyor training programs dramatically reduce the startup’s sales and onboarding burden.

At this stage, the platform begins accumulating data across multiple vessels and operators. The vessel-class statistical baselines that differentiate the professional and enterprise tiers become increasingly substantive as more survey events contribute to the dataset.

Phase 3: Expand across vessel types and class partnerships

With a stable product on one vessel segment and one lead society, expansion follows two axes. Within the initial society relationship, the platform extends to additional vessel types (tankers, bulkers, container ships, etc.) and survey scopes (intermediate surveys, damage surveys, post-repair verification). Beyond it, the same architecture — class structural model access, IACS rule integration, multi-modal fusion, fleet-wide intelligence — is offered to other IACS members through equivalent partnership agreements. Each new society partnership opens a new vessel population and a new distribution channel without requiring fundamental changes to the product architecture.

Team

The founding team consists of three domain specialists whose backgrounds directly address the technical and regulatory requirements of AUTOASSESS Challenge 5 and the broader vessel integrity market.

The applied mathematician has active research and practical experience in optimal control, route optimization under endurance constraints, degradation modeling, and hierarchical statistical inference. For this product, the specific contributions are mission planning that guarantees coverage of critical zones within robot flight-time limits, corrosion progression models that forecast thickness loss trajectories from sparse UTM + visual data, and fleet-wide Bayesian methods that use class-level statistics as a prior for individual-vessel risk. These tasks require mathematical reasoning about spatial coverage, time-series dynamics, and uncertainty propagation — none of which generic robotics software provides.

The physicist brings expertise in physically grounded computer vision, photometric and spectral surface modeling, and measurement uncertainty quantification. For vessel integrity, this translates to models of how RGB, thermal, and hyperspectral signatures relate to coating condition and early corrosion under real tank lighting and humidity; rigorous fusion of visual and UTM data with calibrated confidence bounds; and reduction of dependence on massive labeled datasets through first-principles priors. Sensor characterization, calibration, and traceability — essential for any metrology-adjacent product that must satisfy class requirements — also fall within this role.

The maritime structural engineer (background in aerospace CAD, robotics, and launch-vehicle structural dynamics) brings the domain knowledge to connect robotic data to class engineering reality: mapping image and UTM coordinates to frame stations, strakes, and stiffener positions; interpreting findings against load paths, fatigue-critical details, and IACS repair criteria; and ensuring that every output is formatted and referenced in a way that surveyors and class can directly use. Credible technical engagement with class surveyors, shipyard superintendents, and regulatory stakeholders depends on this fluency.

Two near-term hires close operational gaps: an MRO / shipyard domain expert who understands confined-space survey procedures, staging logistics, coating repair workflows, and class survey acceptance criteria from direct experience; and a regulatory and class liaison engineer experienced with IACS recommendation development, RIT approval pathways, and EASA/IMO-adjacent processes for digital survey tools.

Roadmap

The roadmap separates three phases because each adds sensing depth, regulatory burden, and operational complexity while building on the unified fusion architecture that Challenge 5 requires.

Phase 1 execution gates (must hold before broader deployment)

• Repeatable coverage and image/UTM quality sufficient for class surveyor review
• Reliable missed-zone detection, recapture logic, and probe placement accuracy (cm-level relative to class model)
• Acceptable false-positive rate for visual + thermal triage, validated on paired robotic + manual data
• Digital reports correctly mapped to class structural zones and IACS rule references
• Surveyor acceptance that the ranked worklist and forecasts add value rather than workload
• Alignment with lead classification society on pilot procedure and evidence requirements for RIT acceptance

What success at Phase 1 means

If Phase 1 works, the company holds more than a robotic survey workflow. It holds a growing, class-registered dataset of visual + UTM findings, a persistent structural integrity model per tank and vessel, and a predictive engine whose accuracy and value improve as more vessels and survey cycles are recorded. The accumulated multi-modal, zone-aligned, longitudinal data — neither the robot nor the software alone — is what makes the product difficult to replicate and increasingly useful over time. It is exactly the “considerable historical data per vessel (e.g. 5 years) including visual and UTM logs” and the “expert-infused machine learning” that Challenge 5 specifies.

Phase 2 and beyond

Phase 2 deepens the quantitative core: moving from targeted manual UTM confirmation to integrated robotic deployment where measurement uncertainty is fully characterized and accepted by class. The hyperspectral pilot collects ground-truth coating chemistry data on high-value zones (leading edges of hopper tanks, moisture-prone lap joints, manhole surrounds) to train the visual AI layer to infer coating condition and early corrosion risk from RGB + thermal alone where spectral signatures correlate. Active thermography (brief controlled heating) is evaluated for improved sensitivity to shallow delamination in coated structure. All additions are bounded pilots with explicit pass/fail criteria; nothing is productized before validation against manual reference methods and class surveyor acceptance.

Regulatory Strategy and Risk

Regulatory approach

The regulatory position follows directly from the product’s inspection boundaries and Challenge 5’s emphasis on human-expert oversight. The product is framed as a documented survey aid and data-fusion tool under class-approved procedure, with explicit surveyor review and confirmation on every finding and every UTM reading. It does not claim autonomous survey release or replacement of surveyor judgment. It positions itself as the tool that makes robotic data class-usable by providing the missing unified assessment layer.

Public precedent supports this positioning. IACS has issued recommendations on Remote Inspection Technologies (RIT) that accept drone and crawler imagery for certain survey tasks when appropriate safeguards, data quality, and human oversight are in place. Individual societies have approved specific drone and UT drone applications on a case-by-case basis. The pilot operates under internal class-approved procedure with full confirmation; formal society endorsement of the fused workflow follows once sufficient paired data and operational evidence have been generated.

The classification partnership is central to the regulatory strategy: the society’s engineering endorsement and data access strengthen the case that the structural risk scoring and class-rule mapping have a sound technical basis. The product’s outputs are designed from the start to slot into existing survey report formats and task-card structures, minimizing friction with current class processes.

Risk matrix

References

1. AUTOASSESS Project (2026). Open Call #2 – Guide for Applicants, Annex 1: Challenge 5 Description – Combined Qualitative and Quantitative Assessment of Structural Integrity Using Imaging and Measurements. autoassess.eu
2. IACS (2023). Unified Requirement UR-Z10 (Rev. 34) – Hull Surveys of Oil Tankers, Chemical Tankers, Bulk Carriers, etc. International Association of Classification Societies.
3. IMO (2024). MSC.1/Circ.1649 – Guidelines for the Use of Remote Inspection Technologies (RIT) in Surveys of Ships and Mobile Offshore Units.
4. DNV (2025). Rules for Classification – Ships, Part 7: Surveys, Chapter 1: Survey Requirements. DNV AS.
5. ABS (2025). Rules for Survey After Construction – Part 1: General. American Bureau of Shipping.
6. Lloyd’s Register (2025). Rules and Regulations for the Classification of Ships – Survey Requirements. Lloyd’s Register Group.
7. Voliro (2025). Drone-Enabled Pulsed Eddy Current (PEC) Sensor for Wall Thickness Measurement. voliro.com
8. Cubert (2025). Ultris X20 Plus — Hyperspectral Camera for UAV/UGV Applications. cubert-hyperspectral.com
9. DJI Enterprise (2025). Zenmuse H30 Series — Multi-Sensor Payload with Radiometric Thermal Imaging. dji.com/zenmuse-h30-series
10. Polanyi, M. (1966). The Tacit Dimension. University of Chicago Press.