In partnership with BIP, a major maritime operation connected documents, data, and analytics in a single operational experience. By applying generative AI to shipping, the initiative improved efficiency, accelerated critical processes, and expanded decision-making capacity at scale.
Documents indexed
Strategic workstreams accelerated
A single layer for knowledge and decision-making
In complex operations such as shipping and integrated logistics, decisions depend on large volumes of data, technical documents, and specialized knowledge. When that information remains fragmented across systems, spreadsheets, and subject-matter experts, the operation loses speed, scale, and predictability. In this context, generative AI is taking on a central role by transforming dispersed knowledge into actionable, accessible, and reliable answers at the point of decision.
Generative Artificial Intelligence is accelerating the adoption of agents capable of interpreting complex data, automating processes, and supporting decisions dynamically and contextually. Across industries, this technology is improving efficiency, reducing costs, and transforming how knowledge is accessed and used in operations.
Against this backdrop, an intelligent knowledge and decision agent was created to transform operational knowledge management, automate critical navigation calculations and workflows, integrate structured and unstructured data, and make forecasting and advanced analytics more accessible to business teams. The initiative also began supporting maritime and logistics decisions with applied intelligence, combining speed, consistency, and traceability.
As it evolved from a conversational assistant into a complete digital ecosystem, the solution incorporated generative AI, deterministic automation, and deep integrations with corporate systems, creating a new intelligence layer to support day-to-day operations.
Operational complexity, fragmented knowledge, and manual processes
During the discovery phase, several structural pain points and gaps were identified in maritime business routines:
Fragmented knowledge and difficult access
Critical information—including contracts, PROs, operating manuals, and instructions—was scattered across multiple sources and required excessive search time. Work depended heavily on subject-matter experts, creating risk and rework.
Repetitive, manual processes
This was especially true in workflows involving:
- laytime calculations;
- freight simulation;
- IMO document analysis;
- queries to structured IA Fleet data.
These activities were time-consuming, prone to human error, and lacked standardization.
Limited predictability and access to analytics
Sophisticated tools such as the IA Fleet freight calculator were highly accurate but largely invisible to end users and restricted to technical teams.
Dependence on tacit knowledge
Several processes relied on individual history and experience, particularly:
- contract interpretation;
- reviewing Statements of Facts (SOFs);
- identifying sensitive proposals in IMO documents.
Knowledge that was not creating value in the operation
BIP’s analysis showed that the main challenge was not a lack of data, documents, or tools—all of these assets already existed and were widely used across the operation.
The central issue was the inability to transform those assets into accessible, consistent, and scalable operational intelligence.
In practice, this appeared in three main dimensions:
- Fragmented and difficult-to-access knowledge
Critical information was distributed across contracts, manuals, systems, and structured databases, requiring significant manual effort to locate and interpret. Access depended more on individual experience than on a structured system. - Dependence on tacit knowledge and limited standardization
Important processes such as contract interpretation, SOF review, and regulatory document analysis varied depending on the professional involved, creating inconsistencies, rework, and operational risk. - Analytics that were not broadly accessible
Although advanced models existed—including freight calculation engines—their use was restricted to specialists or technical teams, without an accessible interface for daily operations.
Given this scenario, it became clear that the required transformation was not only technological but structural: a layer was needed to connect data, documents, and business rules so the operation could access reliable, actionable answers in real time.
An intelligent agent for knowledge, automation, and decision-making
The agent was developed as a comprehensive solution that:
- Understands documents and extracts knowledge: automatically indexes, interprets, and references content;
- Performs complex calculations using contractual rules: automates laytime, validates contracts, and applies exceptions;
- Integrates structured corporate data: connects to IA Fleet, enabling queries on voyages, freight, consumption, routes, and scenarios;
- Automates regulatory workflows: processes IMO document packages intelligently;
- Responds accurately in natural language with full traceability, based on real data and official documents.
Technology as an enabler of transformation
The solution was designed as an integrated technology ecosystem combining:
- generative AI models;
- semantic search capabilities;
- structured document repositories;
- integration with corporate systems;
- deterministic automation for executing business rules.
This architecture enables continuous evolution, scalability, and expansion into new use cases.
The solution in practice
The solution was developed to transform operational knowledge, corporate data, and business rules into intelligence that the operation can readily access. The platform connects different maritime business needs in a single experience, improving efficiency, standardization, and decision-making agility.
Intelligent document management
Maritime teams work daily with highly critical contracts, manuals, procedures, and operating instructions. Before the solution, this information was dispersed across multiple sources, requiring manual searches and strong dependence on subject-matter experts.
The platform created an intelligent knowledge-access layer with:
- indexing and processing of critical documents;
- advanced semantic search;
- natural-language answers with source traceability;
- a responsive interface for rapid consultation;
- standardized responses to recurring questions.
This workstream reduced information search time, centralized knowledge, and accelerated the onboarding of new professionals.
Operational intelligence and decision support
Beyond document management, the solution expands the operation’s access to critical information by connecting different data sources and business rules through a simple, accessible interface.
The platform enables:
- natural-language queries;
- cross-referencing of documents and structured data;
- faster access to operational information;
- greater consistency across teams;
- more agile support for decision-making.
As a result, the operation reduces its dependence on tacit knowledge and gains speed in critical analyses and routines.
Continuous solution evolution
In addition to the capabilities already implemented, the solution has further development workstreams structured to deepen operational automation, expand the use of analytical data, and increase efficiency in critical processes.
For confidentiality reasons and to comply with the client’s guidelines, upcoming developments are not detailed in this case.
This roadmap reinforces the initiative’s potential as a scalable operational intelligence platform that is prepared to evolve alongside business needs.
Consolidated business value
By integrating knowledge, data, and operational intelligence in a single platform, the solution strengthens operational efficiency and advances the organization’s digital maturity.
More than answering questions, the platform creates a new way to access information, execute critical routines, and make decisions with greater speed and consistency.
Early gains and the foundation for an intelligence-driven operation
The initial implementation—especially the intelligent document management workstream—has already generated meaningful impacts on operational routines. Beyond isolated gains, the results indicate a shift in how knowledge is accessed and used across the operation.
Key results include:
- Simplified access to critical knowledge
Information that was previously dispersed became centrally available, with natural-language queries and source traceability. - Significant reduction in operational time
Searching contracts and technical documents moved from a manual process to an assisted one, reducing effort and accelerating analysis. - Stronger document governance
Structuring, indexing, and standardizing documents improved answer consistency and query reliability. - Faster onboarding of new employees
Easier access to knowledge reduced dependence on experts and shortened the learning curve for new professionals. - Alignment with leading technology practices
The adoption of generative AI positions the operation in line with advanced trends in digitalization and operational intelligence.
More than a set of isolated results, the solution establishes a foundation for continuous evolution—one in which knowledge is no longer a bottleneck but a strategic asset.
Why did BIP’s approach work?
The initiative succeeded because of how the solution was designed and implemented, combining a business perspective, technical depth, and a focus on real operational value.
Several factors were decisive:
- Integration of business, data, and technology
The solution was designed not simply as a technology project, but as a direct response to concrete operational challenges, ensuring alignment with the client’s context. - Building on existing assets
Instead of replacing systems or models, the approach amplified existing assets—including documents, structured databases, and analytical engines—by connecting them through a new intelligence layer. - Focus on usability and adoption
Using natural language as the primary interface reduced barriers to adoption and allowed different user profiles to access the solution without technical expertise. - Incremental, value-driven evolution
Implementation was organized into workstreams, beginning with document management and evolving toward automation and data integration, ensuring continuous delivery and visible value from the earliest phases. - Reduced dependence on individual knowledge
By structuring and systematically making knowledge available, the solution improved standardization, reduced operational risk, and increased scalability.
What can industry leaders learn from this case?
Complex operations—particularly in sectors such as mining, energy, and logistics—share similar challenges: large volumes of information, critical processes, and dependence on specialists.
This case offers several important lessons:
- The challenge is not having data, but making it usable
Many organizations already have advanced data and models but still struggle to make them accessible to the operation. - Unstructured knowledge is one of the largest operational bottlenecks
Documents, contracts, and operating instructions represent a significant volume of critical information that limits efficiency without the right structure. - Generative AI is more than automation—it is an access layer for intelligence
When applied effectively, it connects different information sources and transforms complex queries into simple, actionable answers. - Democratizing information reduces risk and enables scale
Reducing dependence on specialists does not mean sacrificing quality; it makes knowledge more consistent and widely available. - Effective transformations begin with real problems, not technology
The solution’s impact is directly connected to the fact that it was built around concrete operational pain points.
From information management to operational intelligence
The platform represents the evolution of knowledge management toward an operational intelligence model in which data, documents, and business rules are integrated into a single decision-support layer.
This transformation enables greater efficiency, lower risk, and increased analytical capacity across operations, positioning the organization in line with leading trends in the use of generative AI in the enterprise.
Our team brings together strategy, technology, and execution to design high-impact programs for complex maritime and logistics environments. If operational efficiency, knowledge management, and smarter decision-making are on your strategic agenda, talk to our experts to learn how we can help.
