Build and Run is the far end of the ladder: we design, build, and operate a full product, then keep proving it holds as models and threats move. It is not the door — most engagements start with an assessment. It is for teams who want the people who know how AI breaks to be the ones who build it, and to stay on afterward.
Who this is for
Two situations bring people here.
- You have customers and a product to build right the first time. The AI feature is core to what you sell, an enterprise security review is coming, and you would rather build correctly than harden later.
- You have an assessed or hardened product that needs an owner. The prototype survived a readiness assessment or a hardening engagement, it is real, and now someone has to keep it that way while you focus on the business.
Both are steady work, not a rescue. If your situation is “the demo works and the review is in three weeks,” that is a hardening engagement, not this.
What we build
Full SaaS and AI products, spec to production — production-grade from the first commit, in your stack and your repositories. Not a prototype you inherit.
Product engineering
The unglamorous parts, done correctly: the data model, background jobs, error handling, observability, and a deployment path you can reason about. A product is only as production-ready as its least-tested boundary, so we build the boundaries first.
AI features with evals from day one
Every AI feature ships with an evaluation set before it ships to users. We write the failure cases first — prompt injection (LLM01), sensitive information disclosure (LLM02), and the task-specific ways your feature can be wrong — and wire them into the pipeline so a regression is caught by a failing eval, not by a customer. The eval set is the contract; see the guide to LLM evals for how we build and score one.
Payments, auth, and tenancy done properly
Multi-tenant isolation, authentication, and billing are where “it works in the demo” quietly becomes one customer reading another customer’s data. We build tenancy boundaries that are tested, not assumed, and we test them the way an attacker would — by trying to cross them.
How “run” works
Run is a subscription to staying correct, not a support retainer.
- Continuous evaluation and monitoring against the eval set, in production, so drift shows up as a number before it shows up as an escalation.
- Scheduled re-assessment. When you change a model, add a tool to an agent, or the threat landscape moves, we re-run the relevant OWASP LLM Top 10 and Agentic checks on a schedule — the same evidence-first method we use everywhere, documented in our methodology.
- Your stack, your repositories, your ownership — always. Run is us operating alongside you against the production-readiness checklist, not a black box you have to trust.
The independence rule
One hard line, and we will not bend it: we do not audit our own build for the same client. Assurance you buy from the team that wrote the code is not assurance — the people who built a thing share the blind spots that let it break.
So when a Phixe-built product needs a production-readiness assessment or an artifact for your enterprise buyer’s security team, an independent third party re-verifies it — one you choose, or one we recommend — agreed in writing before the review begins. It is the same reason our reports carry reproducible evidence rather than a certificate: we prove what we tested, and we let someone with fresh eyes confirm it.
What we’ve shipped
The clearest example is faben — an AI decision engine we build, break, and run ourselves under the same evals we would apply to your product. It is where we prove the method on our own risk before we bring it to yours. See what we’ve shipped for the rest.
If you are choosing between building here and building elsewhere, the difference is not speed — it is that the assurance is designed in, and then independently checked. That is the whole point of the ladder.