AI Can't Fix Bad Data: The Maritime Intelligence Gap Data is still king | Kpler - Apr 09, 2026

Solutions

Industries

Resources

Company

Developers

Login

Request a demo

Back to all posts

Scott Sherwood<br>CTO

Tags

AI

Iran-US conflict

Share

https://kpler.com/blog/data-is-still-king

Heading<br>Request demo

Blog/<br>Tech/<br>Current post

April 9, 2026

Data is still king

Prototypes Are Free. Proprietary Data Is Priceless.

Tech has reached a tipping point. The time to market is quicker than ever, and I've experienced it firsthand. I built an open-source intelligence platform (OSINT) that aggregates signals from social media and news outlets, triangulates across sources, scores confidence, and surfaces emerging geopolitical events on a live map as they develop. It is a production-grade system, not a demo, and we are iterating on it publicly. It took one person and just a few days to build – not large cross-functional teams and months of development this kind of work would have required until recently.

AI is compressing development timelines, and that part of the narrative is true. But what is also true is what AI cannot do for you. Yes, your tech is fast, but if your data is garbage, all you're doing is... bringing garbage to market faster.<br>“Dashboards of misinformation” proliferation problem<br>Across commodities, maritime, and energy we are seeing an explosion of dashboards and intelligence tools built on open-source data. They are often visually impressive, with maps showing vessel positions, charts with price overlays, and feeds that look authoritative at first glance. The people building them are smart and moving fast, and in many cases the interfaces are genuinely well-crafted.<br>The problem is not the design, but the foundation on which these tools are built. If “data is the new oil”, and that data is fundamentally flawed, these dashboards become like petrol engines running entirely on ethanol.<br>Open-source AIS data is inherently incomplete and retrospective. Commercially available news feeds are aggregated from public sources, which can lag by weeks or months, and in some cases longer. Case in point, data is being intentionally delayed or omitted on traffic in the Strait of Hormuz.

‍The above screenshot is an example of a dashboard built on freely available data including some from MarineTraffic.com. The creator of the dashboard acknowledges that the data lags and cautions against relying on the “fun side project” for anything serious. This is indeed a great fun project for developers and we applaud their ingenuity if not for the data scrapping. The issue is when businesses or individual investors begin conflating such projects with factual data that should be used in decisions that will inevitably cost resources.<br>More importantly, the domain expertise required to know what you are actually looking at takes years to develop. Understanding why a vessel going dark in a particular location means something fundamentally different from going dark somewhere else is not a question you can resolve with a better prompt. I have seen dashboards presenting sanctioned trade flows with confident visualisations that would fall apart under scrutiny from anyone with real market experience, and tools described as AI-powered that are, on closer inspection, thin wrappers around data sources with known quality issues. The surface holds up until someone who knows the market asks a question.<br>This is the gap that AI cannot close: not at the interface layer, not at the workflow layer, but at the level of the underlying data itself.<br>Proprietary AIS as a case study in advantage<br>To make this concrete, I used ChatGPT as the test case, and the contrast is stark.<br>AIS, the Automatic Identification System that vessels use to broadcast their position, is the foundational signal layer for the entire maritime intelligence industry. On the surface it sounds open: vessels transmit, receivers pick it up, and there are public aggregators. The reality of working with raw AIS data at scale is considerably messier. Vessels spoof their positions. Flags change. Transponders go dark, sometimes legitimately and sometimes not. Vessel identities are recycled, and the same physical ship can appear under different names, IMO numbers, and ownership structures across different timeframes.<br>Building something genuinely useful on top of AIS requires years of continuous investment: collection infrastructure across terrestrial and satellite receivers, entity resolution across millions of vessel histories, anomaly detection trained on known patterns of evasion, and human analyst verification at the edges where models are not confident enough to act alone. You also need the commercial intelligence layer, covering fixture data, cargo surveys, and port agent reports, to validate and enrich what the AIS signal alone cannot tell you. That stack, built over a decade, produces something qualitatively different from what any well-funded team can replicate in six months...