Karthik Ramesh
Co-founder & CEOCompany, customers and the fleets we serve.
Role-specific AI agents for shipboard and shore-based operations, beyond the limits of traditional, complex and expensive marine ERP.




A specialist on every desk: six AI agents, each built for one maritime role, each drafting with the context, evidence and ownership visible.
“Log a new defect on the MT pump, assign to the engineering team.”
A ship manager, a French naval shipbuilder and a hydropower company run Sea-Squad on contracts measured in years, not pilots measured in weeks.
Operators, engineers and product people building the squadron.
Company, customers and the fleets we serve.
Platform architecture, AI systems and security.
Operations and client delivery.
Co‑founder of Marine Insight.
Marketing and community.
Founder of Marine Insight.
Agent workflows, UX and the product roadmap.
Engineering delivery, integrations and reliability.
Sea-Squad sits on top of the PMS, ERP, mailboxes and reports you already run, shaped around each person's role.
Sea-Squad isn't another platform to learn. It's an AI overlay that sits quietly on top of your existing stack: your mailboxes, your dashboards, your reports, your workflows. We don't replace what already works. We make it sharper, faster, and a little more thoughtful.
Six modules. One squadron. Designed to fit naturally into your day, from the moment you log in to the last task you close.
A live map of every vessel with ETA and next port, beside the inspections, emails and action items that need attention now.
An add-in inside Outlook that reads long threads, links them to the vessel, and turns them into action items and drafts you approve.
Capture on board, online or offline; the platform turns it into an accountable report on shore.
Every task that matters in one feed, with an owner, a due date and a clean hand-off.
“Log noon report for MV Ocean Pearl”
Talk to your squadron where typing is not an option. Hands free, eyes free.
Ask about your fleet, documents and decisions and get the answer with its source, every time.
Engineering notes and product thinking from the team.
The same model scores 46% or 80% depending on what is wrapped around it. An analysis of why the harness, not the raw model, now decides whether an AI system works, and what follows for anyone building or buying one.
Read the postWhy specialist agents on localised models beat general chatbots for fleet operations: sovereignty, reliability and the cost case.
Read the white paper