By Janani Yagnamurthy, VP, Product, Strategic Growth
Charter party management sits at the heart of shipping profitability, yet the process of identifying risks in these contracts remains stubbornly manual. Over the past year, I've worked closely with chartering teams to understand where their contract review process breaks down, and more importantly, what it costs them when it does.
What I've learned challenges the assumption that AI in shipping primarily delivers efficiency gains. The real value lies somewhere more strategic: transforming how chartering teams negotiate by giving them intelligence they simply couldn't access before.
Counting the cost of manual review
Chartering teams face an impossible trade-off. They need to move fast to secure the best deals, but thoroughness takes time they don't have. The cost of context-switching particularly undermines thorough review. People get diverted by other urgent tasks and fail to notice critical details.
Experienced chartering managers catch the obvious problems. The revenue leakage happens in the subtle gaps:
Missing clauses that should favour you
Ambiguous language that creates negotiating room for counterparties
Incomplete terms that fail to protect your interests when disputes arise.
One contract we reviewed specified SHEX terms (Sundays and Holidays Excluded) in one section but SHINC terms (Sundays and Holidays Included) in another. When the vessel reached berth for cargo operations, this contradiction meant the owner paid for weekends that should have been covered under laytime.
The maths on this adds up quickly.
Twenty percent of voyages face price write-downs or losses. When average revenue leakage per affected voyage runs around $3,000, a company with 1,000 annual port calls looks at $1-3 million in preventable losses. Individual instances seem manageable, until you multiply them across your entire operation.
Why smart people miss critical details
Chartering teams know their business. The problem stems from cognitive load and context-switching.
A thorough manual review can take two to three hours, depending on complexity.
In practice, teams triage, they focus on the highest-risk items and skim the rest.
Market shifts, counterparty preferences, and evolving legal nuances mean that what was once considered boilerplate may now carry new implications.
Rethinking what AI should do
While most maritime AI tools focus on automation, the Charter Party Risk Analyser we’ve developed to delivers insight that simply wouldn’t happen manually.
In internal testing, the system delivers a full-document review including clause-level annotations in around six minutes. But the real value comes from what that speed enables: analysis that wouldn't happen otherwise.
When you upload a charter party, the system identifies risky clauses with specific annotations showing where concerns originate. You're not just getting flagged items, you're understanding the reasoning with citations demonstratingthe logic. This transparency matters because chartering teams need to trust the analysis before they'll use it in negotiations.
The system understands context between clauses. It catches contradictions like the SHEX/SHINC example I mentioned earlier.
When laytime terms conflict with demurrage calculations, it flags the inconsistency. This contextual analysis mirrors how an experienced legal team reviews contracts but happens in the time it takes to make a cup of tea.
Concerned about what your team might be missing in contract reviews?
Find out what Marcura's CP Risk Analyser could do for you
Integration changes everything
We've integrated historical insights from PortLog and Marcura Claims for customers who use those products. This brings industry benchmarks and company-specific data directly into risk analysis.
PortLog insights include:
Turnaround and waiting time indicators.
Weather and seasonal disruption patterns.
Historical trends for the specific port in question.
This helps validate whether voyage calculations align with operational reality.
Claims insights add:
Visibility into the clause types that have driven past laytime disputes.
Patterns of recurring issues at similar ports or under similar terms.
This allows teams to challenge or renegotiate clauses proactively.
AI-based counterparty screening through Marcura Compliance adds a third layer of assurance, providing by surfacing potential sanctions risks early in the process.
Feedback loop delivering compounding advantage
The capability I'm most excited about: the system learns from each customer's experience.
Each customer operates a protected instance. When chartering, legal, or contracts teams add context or refine interpretations, the system incorporates that internal guidance.
Over time, this creates a compounding advantage:
Institutional knowledge becomes centralised and reusable.
New joiners inherit years of accumulated expertise.
Edge cases that previously caused losses are automatically flagged.
Pattern recognition that once took years to develop becomes visible instantly.
With clear visibility into how specific clauses affect outcomes, chartering teams can negotiate freight rates or terms from a position of evidence, not instinct. In early trials, one customer identified four missing clauses worth an estimated $120,000 in potential exposure before signing.
What this means for the industry
I expect this type of capability to become table stakes within two years. The economics simply favour it: six minutes of comprehensive analysis versus three hours of selective review, with measurably better outcomes.
The competitive dynamic shifts from "who can review fastest" to "who can leverage the deepest intelligence." Companies that integrate risk analysis with historical performance data, industry benchmarks, and institutional learning will negotiate better terms consistently.
The barriers to entry aren't technological. AI capabilities for document analysis are widely available. What sets Marcura apart is the bedrock of data and experience that form the foundations of what we’re creating.
What to expect next
We're releasing this capability in phases. The current version delivers holistic risky clause analysis, counterparty screening, and integration with PortLog and Claims. It covers voyage charter parties, with time charter party analysis following within a month.
Phase two will attach dollar values to identified risks, showing potential financial impact of specific clauses. We're expanding the feedback loop capabilities and exploring integration with other data sources, including analysing weather conditions at time of voyage alongside bunker price comparisons between budgeted and actual costs at evaluation time.
The question that drives our roadmap: what decisions would chartering teams make differently if they had perfect information? Every feature we build moves closer to answering that.
The barriers to entry aren't technological. AI capabilities for document analysis are widely available. What sets Marcura apart is the bedrock of data and experience that form the foundations of what we’re creating.
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