Why we started ShipSmith
We got tired of watching capable businesses get sold AI strategies that never became AI systems.
Between our team's previous roles — product, engineering, and operations across software, fintech, and enterprise SaaS — we had a front-row seat to how AI projects fail. Not because the technology doesn't work. Because the engagement model doesn't.
The typical pattern: a consulting firm sells a six-figure AI strategy. The strategy recommends three pilots. Two pilots run for six months and produce PowerPoints. One gets a small production deployment that nobody fully trusts, so the team works around it. Two years later, the company is back at square one, now more sceptical of AI than before.
The problem isn't capability — it's incentives. Firms that bill by the hour have no reason to move fast. Firms that measure success by client satisfaction surveys have no reason to define hard outcomes. Firms that staff projects with junior consultants have no reason to take personal accountability.
ShipSmith is structured to remove those misalignments. Fixed price means we absorb scope risk, not you. Outcome guarantees mean we write down what success looks like before we start. Founding team on every project means the people who built our methodology are accountable to your results.
We work with mid-market companies because that's where the opportunity is clearest. These are businesses with real operational complexity — $10M–$500M in revenue, hundreds of employees, legacy systems that aren't going anywhere — and they've been underserved by AI vendors who target either large enterprise or early-stage startups.
The 10-20-70 principle
Successful AI implementation is 10% technology, 20% process redesign, and 70% people. Most vendors get this backwards.
Technology
AI models, orchestration, infrastructure. This is the smallest part of a successful implementation. Choosing GPT-4 over Claude, or RAG over fine-tuning, rarely determines whether a project succeeds.
Process
Workflow redesign, exception handling, human oversight layers. Most implementations fail because the process around the AI isn't redesigned — the AI is bolted onto a broken workflow.
People
Adoption, training, change management, feedback loops. This is where implementations live or die. The team using the system every day has to trust it, understand its limits, and know when to override it.
This is why every engagement includes a parallel-run period, exception-handling training, and an adoption review at day 30 and day 90. We're not done when the software ships. We're done when your team is running it confidently.
How we work
We define 'done' before we start
Every project has a written definition of success: specific metrics, measured over a defined period, agreed before we write a line of code. If we can't define what success looks like, we don't take the project.
We build for operators, not demos
The person who will use your AI system every day is probably not technical. We obsess over the exception queue, the override workflow, the monitoring dashboard. The demo isn't the product — the daily workflow is.
We stay through the messy middle
Going live is week 1 of the hard part, not the finish line. Production systems encounter edge cases, data drift, and use patterns that weren't in the spec. Our 60-day hypercare isn't a courtesy — it's where the real work happens.
We tell you when not to use AI
Some processes shouldn't be automated. Some should be improved before they're automated. Some are actually fine as-is. We'd rather lose a project than sell you an implementation that doesn't need to exist.
Built for operational reality, not demos
We don't drop generic AI templates into your environment. Every implementation is designed around how your business actually operates — your compliance requirements, your communication channels, your existing systems, your data patterns.
When we build a document processing system, it understands your document formats and filing structures natively. When we build a customer-facing agent, it follows your industry's regulatory requirements and integrates with the tools your team already uses every day.
Work with a team that ships
If you're evaluating AI partners and want to talk to the people who will actually do the work — not a sales team — we're happy to have that conversation.
Talk to the founding team