About SHIP

Agentic engineering needs engineering metrics.

I built SHIP to help teams deliver software faster and at a lower cost with AI, without lowering the quality bar or giving up control of how the work gets done.

Önder Ceylan, founder of SHIP and award-winning AI engineering leader

Önder Ceylan speaking on stage while holding an award — Twenty years across software engineering, from specialist roles to platform leadership.

Why I built it

The system around the agent matters.

I have spent twenty years working across the software development lifecycle. I worked in specialist roles on enterprise systems, being a generalist since the start. I understand how one stage in lifecycle affects all the rest.

One of the most complex projects I worked on was an internal no-code application platform built for high load, multi-platform delivery, and omnichannel experiences. Work at that scale teaches you quickly that business and engineering cannot operate as separate concerns.

Projects and people come and go. The work still rests on foundational engineering practices. If you skip them, the consequences arrive later, usually when the system is under pressure. SHIP carries those practices into agentic engineering through explicit quality gates and a direct view of cost.

A better measure

Tokenmaxxing is a spending story, not an engineering metric.

A token count says how much budget someone burned. It says nothing about the task, the outcome, the agent configuration, or how much repetitive and out-of-context work happened along the way.

Software engineering already has useful measures: cycle time, lead time for changes, deployment frequency, quality, and cost. An agentic delivery loop can measure many of them for each unit of work, down to the second. A CI failure can be fixed at the moment it occurs. Review or test feedback can be addressed as they arrive.

Teams should experiment with every stage of their delivery system and see whether a cheaper model actually holds the quality bar on their work.

Evidence from the Studiobook pilot

A strong loop changes which model you need.

I tested eleven builder configurations on 20 real-world SaaS tickets on a full-stack app while keeping a frontier planner, reviewer and QA agent fixed. The cheapest reliable builder was an open-weight model at $3.09 per resolved task. Claude Haiku resolved most of the tickets at $3.43 on average, compared with $6.40 for Opus.

This was a small directional pilot with one repository, 20 tickets, and one primary trial per setup. It does not prove that a budget model can handle every job. However, it does show why teams should test model choices inside a complete delivery loop instead of assuming every role needs the most capable model available.

An extensive benchmark study with more proof is in works and will be published soon.

Built in practice

SHIP ships SHIP.

A large share of the platform has been shipped by SHIP, and it is still growing. Dozens of verified PRs, every release, and every production deployment have benefited directly from the same delivery loop the product offers to customers.

Dogfooding removes a lot of comfortable assumptions. Weak and wasteful agent configurations become visible and show up in the bill. A missing quality gate eventually produces a real defect.

Our pillars

Four principles I want SHIP to keep.

Engineering quality is non-negotiable.

Generated code goes through the same review, test, CI, and acceptance gates as any other change. Faster output is useful only when the result matches the quality expectations.

Measure outcomes, not consumption.

Token volume is a session stat and a cost input. Cycle time, lead time, delivery frequency, quality, and resolved work tell an engineering team whether the system is improving.

Humans and agents improve the system together.

Agents take on repetitive delivery work. Engineers set direction, handle exceptions, and improve the practices that make the next run better.

Customers keep control.

Teams should be able to switch agents, models, and providers without rebuilding their workflow. The platform should help reduce AI costs, not profit from making them larger.

Founder

Önder Ceylan

Founder of SHIP and award-winning AI engineering leader

Önder has worked across software engineering, developer platforms, technical leadership, open source, and developer training for the past 2 decades. His work focuses on the systems that help people and agents build reliable software together.

Selected recognition

Google Developer Expert in Web Technologies and AI for Web Developers
Golden Award in B2B, KLM
Chrome Advanced Web Apps Fund, Google Chrome
JavaScript Hero of Community, JS Poland

The direction

In a few years, well-run engineering teams will spend less time doing repetitive development work and more time improving the agentic systems around it.

I do not want SHIP to become an arbitrage layer that profits from an organization's excitement about AI and their spend on inference. SHIP should be honest about the value it creates and give customers control over their tools, their data, and their economics.

Ready to put the fleet to work?

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