Every integrator will modernize your applications. Only Avcel proves it works before we touch production.
We build a functional replica of your environment first, test every change against it, and only then move to production. That's the whole discipline: fixed-scope delivery, predictable AI costs, governance sized to your risk, handed over as a working system. Proven at a Tier-1 regulated U.S. financial institution held to federal examination standards. If our discipline satisfies examiners at that level, it will satisfy your team.
This is what runs underneath the four stages above: the mechanism that lets Avcel understand a system it has never seen, without guessing. We call it Phenocel. Select a step below, or read the full mechanism.
16 questions across validation, governance, security, and cost. You get an immediate score, a readiness tier, and a specific recommended next step. No sign-up required until you want your results.
Proven at a Tier-1 regulated U.S. financial institution held to federal examination standards, so whatever your governance needs are, we've already cleared a harder one. That rigor is available whether or not you're regulated at all.
Fixed-scope delivery and staged evaluation gates keep your AI investment predictable. We're not the expensive option, and we don't let your AI spend become one either.
Same people. Both numbers.
This isn't a story about teams getting sloppy. Everyone ran the reviews. Everyone signed off. The code passed every gate and broke things anyway, which means the confidence itself was the thing that failed. Review scales with headcount. Generation scales with agents. One of those went up tenfold and the other didn't.
Underneath it is a simpler problem: there's nothing credible to check against. Staging drifted from production a while ago. The documentation was accurate three years back. The dependency graph describes what was declared, not what actually runs. Teams aren't refusing to prove their changes are safe. They have nothing to prove them against.
And if your code can't leave the building, it's worse. The AI development boom happened on the open internet, and most popular tools assume your source can go to someone else's cloud. That assumption fails on day one in a regulated environment, which is why those teams get told to wait while everyone else compounds.
Source: CloudBees survey of 200+ enterprise technology leaders, May 2026.
Every enterprise AI conversation right now asks the same question: should an agent or a deterministic rule make the decision? In regulated environments the honest answer is usually both, agents handling the judgment calls, rules and audit trails handling the parts an examiner can ask about.
That debate is real, and it isn't the one that determines whether your next production incident happens. An AI agent is already writing the code that implements whichever layer wins the argument. Almost nobody is governing that agent the way the decision layer is governed.
Govern the agent that writes the code, not just the agent that makes the decision.
Every quarter they build with AI, they get faster, and the gap stops being recoverable.
No controls, no record. The exposure is already on your books; it just isn't written down.
Most alternatives give you two of the three. Avcel exists to give you all three at once.
AI agents work inside governed sandboxes: isolated, policy-enforced, and deployable fully air-gapped. Before anything reaches production, it runs against a carbon copy of your environment first.
Bring your own models, hosted in your environment. Prompts, code, and context never leave.
Fixed scope and staged evaluation gates: no open-ended token bills, no surprise consulting hours. You know the spend before you start.
Every agent action recorded against policy: who, what, which approval, which data. An audit trail as an output, not an afterthought.
Every vendor promising a finished, governed AI delivery system should be able to answer both. Most cannot.
Ours runs inside your perimeter, on your infrastructure, from day one. Not a vendor's cloud you're renting access to.
You keep the running system, the blueprint, and the evidence record outright. What's licensed is the engine that keeps the blueprint current, nothing else.
Others sell licenses. We deliver a running system.
A consulting firm with a product, by design. We deliver the outcome inside your walls, at a fixed scope you agree to before work starts, governed to the level you need, priced to compete, not to extract.
Perimeter, policy, examiner posture, data rules: we map your control environment.
How the proven design lands in your environment: gaps, sequencing, timeline.
A governed-AI-SDLC architecture document, yours regardless of what happens next.
Avcel is an enterprise specification compiler and control layer for AI software delivery, built by AVM. It turns business intent into validated, evidence-backed requirements, executes them under policy inside your own network, and produces an audit trail as a natural output rather than a separate exercise.
Organizations that need AI-assisted software delivery but cannot let code, data, or prompts leave their environment. We're built first for regulated financial and public-sector institutions, the buyers who face the strictest examiners. Insurance, healthcare, and critical-infrastructure teams with similar constraints are a strong fit too.
Yes. Avcel is deployable fully air-gapped and runs that way today in production at a Tier-1 regulated U.S. financial institution. The compiler is a self-contained CLI wrapping a point model, so configuring a local model gives the deployment zero external egress.
Coding assistants make engineers faster at producing changes. Avcel governs what reaches production: it validates each change against a carbon copy of your environment, including infrastructure-as-code, before anything is promoted. Most organizations end up using both.