The Impact
95
%
accuracy in automated field population across all vessel report types
90
%
of applicable target system fields populated automatically without manual input
4
source system report types handled
AI ROI: 2026 Benchmarks
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This Maritime Transportation company's vessel crews reported operational data into their primary logbook system, but the same data then had to be manually re-entered into downstream reporting platforms for commercial partners. Field names, structures, and requirements differed across systems, making manual mapping slow and error-prone. Crew members spent 1 - 3 hours a day validating data consistency across platforms. Existing API integrations were unreliable and RPA approaches proved brittle, breaking whenever systems were updated. With reporting effort scaling poorly across vessels and partners, every new relationship multiplied the manual burden.
Unframe delivered an AI vessel report automation solution that extracted vessel report data from the source logbook system and populated target system fields using semantic interpretation combined with hard-coded rules as guardrails. The system identified which source fields mapped to target fields based on context and report type, adapting its behaviour across varying report types. Every populated report passed through a mandatory human-in-the-loop review before submission.. The approach was designed to scale beyond the initial target system to additional target systems without rebuilding integration logic.
