SOLUTIONS
The assessment determines coverage, repair scope and cost.
If this first step is inconsistent, ambiguous or reliant on freetext interpretation, the entire claim slows,increasing leakage and reducing decision confidence.
Lodgicl ensures every assessment starts with structure, policy alignment and AI-assisted intelligence that closes the gap between the field and the decision.
Assessments performed without structure lead to inconsistent outputs, unclear coverage recommendations and high support costs.
This slows approvals, increases leakage risk, and adds friction at the stage where clarity matters most.
Claims volumes continue to rise and severe weather events are becoming more frequent. Sudden surges in demand strain the entire supply chain, and inconsistent assessment quality amplifies delays and operational pressure at the worst possible time.
Repair costs continue to climb due to material shortages, labour demand and inflationary pressure. Insurers need assessments that are accurate, structured and aligned to policy rules to control leakage and ensure fair, defensible outcomes.
As insurers introduce more personalised and modular products, assessment complexity increases. Accurate coverage decisions now depend on structured, real-time alignment between field data and policy conditions, which free-text reports cannot deliver consistently.
Site (our mobile app) guides assessors through structured data capture in the field.
PolicyPath™ aligns assessment data to insurer coverage rules in real time.
Lodgicl Intelligence surfaces comparable past assessments to support preliminary scopes before assessors leave the site.
Together, they improve decision accuracy, reduce leakage and strengthen performance across the supply chain.
Site adapts each assessment based on insurer rules, claim type and policy conditions, guiding assessors through a clear, consistent workflow on their mobile device. The result is structured, accurate data captured at the point of assessment rather than reconstructed afterwards.
PolicyPath™ interprets assessment data against insurer coverage rules , helping assessors make accurate on-site recommendations or passing decision support data to insurers. This lowers leakage, reduces clarifications and speeds approvals.
LR- is our retrieval model. It surfaces comparable past assessments by meaning, giving assessors a preliminary scope to review and refine before leaving the site. This reduces the time between assessment and submission, and eliminates the work that comes from scoping from scratch or static templates.
Guided workflows eliminate uncertainty and reduce back-and-forth. Assessors complete jobs faster and produce consistent outputs that support faster decision-making, with less work throughout the process.
Lodgicl captures structured, policy-aligned dataat the point of assessment. Insurers receive consistent information they can act on, enabling faster, more accurate and more defensible decisions across the supply chain.
Clearer assessments lead to quicker decisions.Customers benefit from fewer repeat visits, shorter wait times and more transparent communication throughout their claim.
Lodgicl combines two distinct capabilities that no single system provides today. PolicyPath™ is our patent-pending algorithmic engine that maps structured assessment data to insurer coverage rules, delivering auditable and consistent outcomes every time.
Lodgicl Intelligence (LR-) is our contextual retrieval model that surfaces comparable past assessments by meaning, supporting preliminary scopes in the field without black-box AI making decisions.
Together, they make every assessment faster, moreaccurate and more defensible.
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Lodgicl is designed to workalongside your existing systems rather than replace them. Site integrates with Prime Ecosystem and other repairer platforms.
Lodgicl Intelligence plugs into configured workflows via API. PolicyPath™ operates within the assessment layer, not across your claims platform.
Lodgicl scales across repair networks and insurers without requiring major operational change.
