Shipping AI is easy now.
Proving it's ready isn't.
“Ready” means two things: it holds up in production, and it holds up when a customer's security team or an auditor comes asking. ShipSmith checks every AI workflow you ship against both, then helps you close the gaps and prove it.
In under two years, AI went from an experiment on the side to something running in the core of real products. Teams that had never shipped a model now have a dozen LLM-powered workflows in production: support agents, document pipelines, summarizers, copilots, internal tools nobody put on a roadmap.
The building part turned out to be the easy part. The hard part is everything after: knowing that the tenth workflow is as solid as the first, that none of them are quietly leaking data or burning budget, and that when a customer's security team or a regulator asks you to prove it's safe, you actually can.
That gap is where things break. Not because the teams building AI aren't good (they are), but because AI spreads through a codebase faster than any one person can track, and the care that went into the flagship feature rarely survives the next deadline. Nobody has a full map of every AI workflow running in production, let alone proof that each one is ready.
So we built ShipSmith: a way to discover every AI workflow in your codebase, hold all of them to the same bar, and turn “we think it's ready” into something you can actually show.
What we believe.
A few convictions shape every part of the product.
The gap isn't capability. It's consistency.
The teams shipping AI are good at it. The first workflow gets real care: evals, guardrails, a fallback path. The tenth, built under deadline by someone else, doesn't. We don't assume you're doing it wrong; we assume you're moving fast and can't hold every workflow to the same bar by hand.
Readiness is something you prove, not something you claim.
“We’re production-ready” and “we’re compliant” are assertions until there’s evidence behind them: a grade, a control-by-control breakdown, a record you can hand to your team, a customer’s security reviewer, or an auditor. If you can’t show it, you can’t really claim it.
Every workflow counts.
Risk doesn’t live in the feature you demo. It lives in the one nobody remembers shipping. So we discover every AI workflow in your codebase and hold all of them to one standard, because only full coverage keeps you out of trouble.
Grounded in shared standards, not our opinion.
Our production readiness controls map to NIST AI RMF, the AWS Generative AI Lens, Microsoft’s Responsible AI standard, and the OWASP LLM Top 10; our compliance readiness controls map to the EU AI Act and ISO/IEC 42001. When ShipSmith flags a gap, it’s grounded in the frameworks your customers and auditors already trust.
Readiness is continuous, not a one-time audit.
Your workflows evolve as your product does, and the AI landscape shifts fast: new models, new failure modes, new attack surfaces, new controls. Readiness you proved last quarter isn’t readiness today. ShipSmith re-checks against the current bar so you stay ready without chasing it.
Finding the gap is only half the job.
A readiness score you can’t act on is just anxiety. So ShipSmith doesn’t stop at the diagnosis. It shows you how to close each gap in plain terms, and helps your team build the judgment to keep the next workflow ready too. The goal is a team that ships ready by default.
What ShipSmith actually does.
Point it at your repo. It discovers every AI workflow, then holds each one to two bars: production readiness — scored against 115+ controls across 9 dimensions, from data foundation and evaluation to security, privacy, and cost — and compliance readiness, assessed against the EU AI Act and ISO/IEC 42001 across 7 governance dimensions.
You get a readiness grade per workflow, a ranked list of where each one is weakest, and a prioritized set of fixes in plain English: a record you can act on and hand to anyone who asks.
And we don't stop at the list. ShipSmith shows you how to close each gap, and helps your team build the muscle to keep every future workflow ready, so you fix what's failing now and raise the bar for what you ship next.
Who it's for.
Everyone who has to stand behind the AI: build it, sign off on it, or vouch for it to someone else.
Engineering leaders scaling AI
CTOs, VPs of Engineering, and AI/ML leads who’ve moved past the prototype and now have AI in real products. You need to know every workflow is held to the same production bar, without personally reviewing each one.
Security, risk & compliance teams
The people who have to sign off that the AI is safe. You need control-level evidence mapped to the standards you’re measured against (the EU AI Act, ISO/IEC 42001, and GDPR), not a vendor’s marketing claims.
Auditors, consultancies & dev shops
Teams who assess or ship AI on behalf of others. You need a consistent, framework-grounded way to grade AI systems and produce evidence your clients (and their reviewers) will accept.
Why we're building this now.
We've shipped AI into production ourselves, and sat on the other side of the security review answering for it. Both taught the same lesson: the standards for what “ready” means already exist (NIST, OWASP, the cloud providers for production; the EU AI Act and ISO/IEC 42001 for compliance), but they're scattered across hundreds of pages and nobody has time to apply them to every workflow by hand.
ShipSmith is our attempt to make that bar automatic and honest: grounded in the frameworks everyone already agreed on, applied to all of your AI, every time, with the guidance to close the gaps and the know-how to keep your team ready. So you can keep shipping fast, and prove you did it right.
The ShipSmith team
Meet the founder.
Vivek Jain · Founder
I'm the founder of ShipSmith. Earlier in my career I worked at Goldman Sachs, and I went on to lead developer productivity, engineering efficiency, and compliance at Mindtickle, running its DevOps, SecOps, SRE, and engineering operations. The remit was broad: developer tooling and enablement, onboarding and offboarding, team training, and the day-to-day demands of GDPR and SOC 2. Since then I've spent years building companies of my own and consulting with others on taking their engineering and AI work from prototype to production — and plenty of time on the other side of the security review, answering to auditors and cloud providers rather than asking the questions.
Building my own AI workflows, I kept hitting the same wall: standing something up with a few prompts is easy, but making it production-grade is a different discipline. Across the teams I advised, I saw the pattern repeat. Plenty of AI features get built; far fewer get operated well, because the unglamorous parts of running AI in production rarely get the time they deserve, and leaders don't always have a reliable way to tell whether what shipped is actually ready.
My conviction is simple: an AI engineer's job is two halves, Build and Operate, and most of the value, and the risk, lives in Operate. A strong foundation matters, but a workflow is never really "done" when it ships; keeping it reliable, safe, and compliant is continuous work that rarely gets the attention it needs. Doing that well across every dimension beyond the core feature is genuinely hard, and usually the first thing to get deprioritized. ShipSmith is how I'm making it sustainable: carrying the operate side day to day, taking the routine, repeatable work off teams' plates, and leveling them up so every workflow, new and existing, stays ready over time.
See where your AI workflows stand.
Start with one, free.
Scan your repo, discover your AI workflows, and see where each one stands on production and compliance readiness. Or talk to us first, either way.
No credit card required. Most scans finish in minutes. We email your report either way.