A solutions engineer at a bank builds a slick AI-powered app, one that pulls live data, surfaces insights, recommends actions. Everyone who sees it wants a copy. The engineer hadn't planned on distributing the app widely but now has to figure out a way to get it to over 500 employees across 12 states...without creating a security nightmare.
That gap between a working prototype and safe, organization-wide deployment is what Tyler Clark, manager of solution engineering at Domo, tackled in a recent livestream. Tyler has spent 10 years at Domo building solutions for banking clients, and the demo he walked through shows what it takes to move from vibe coding on a laptop to secure production for an entire enterprise.
This session focuses on a banking intelligence solution running on Snowflake data with Cortex AI, but the deployment principles apply anywhere data access matters.
Below, find a checklist of five questions distilled from the session. Treat it as a decision framework for evaluating whether an AI prototype is ready to scale.
The vibe-to-production checklist: 5 questions before you ship
1. Can everyone who needs the app actually reach it?
Building an AI app solves one problem. Getting it into the hands of every bank teller, loan officer, and regional manager who needs it solves a different one entirely.
Tyler put it this way: "You create something really cool. What then? How do I get that, especially in a banking industry, to my bank tellers that might need to use it, or all the behind-the-scenes employees at the banks?"
A local prototype, like one made in Lovable, lives on one machine. To distribute it across an organization, the app needs to run in a shared environment where anyone with proper access can open it from any device, in any location, without asking the original builder to run it for them. The distribution question comes before any feature question. If the answer is "they'd have to come to my desk," the app isn't production-ready.
2. Does each person see only the data they're authorized to see?
A single dashboard showing consolidated financials might be fine for executives. The same dashboard in front of a teller who should only see their own branch creates a compliance problem.
Personalized permission policies filter what each person sees based on their role, location, and clearance level. Everyone opens the same app, but the data responds to who is viewing it. One person sees national numbers; another sees only their region; a third sees nothing at all if the data falls outside their access rights.
The alternative is building separate versions of the app for each role, which multiplies maintenance work and increases the chance that someone accidentally sees data they shouldn't. Per-person filtering built into the platform eliminates that overhead.
3. Do your cloud governance rules pass through automatically?
Most banks already have security and governance policies defined in their data warehouse (Snowflake, Databricks, or another cloud data platform). The question is whether those rules carry forward when data surfaces in an AI application.
Tyler explained that the solution he demonstrated passes Snowflake's existing rules directly into Domo. "We can bring down your Snowflake rules and policies as well, so you're not needing to recreate all those on Domo," he said. "We're just passing those through."
Recreating governance rules in a second system doubles the maintenance burden and introduces drift. If someone updates a policy in Snowflake but forgets to update the app, the systems fall out of sync. A better approach treats the warehouse as the single source of truth for governance and lets the AI application inherit those rules automatically.
4. Is the system auditable?
Regulators will not accept "the AI told me to" as an explanation. When a compliance question arises, someone needs to trace which data fed the insight, which model generated the recommendation, and which governance rules were in effect at the time.
Auditability means logging who accessed what, when, and under which permissions. It also means keeping a clear boundary between the data layer (where governance lives) and the AI layer (where insights are generated). When both layers operate on the same governed foundation, the audit trail is straightforward. When they operate in separate silos, reconciling records becomes a manual, error-prone process.
For banking clients, where missing a regulatory deadline can result in fines reaching hundreds of thousands of dollars, auditability isn't optional.
5. Is there a fast path from prototype to production?
Vibe-coding has value. It lets non-engineers explore ideas, test concepts, and validate whether an approach is worth pursuing. The mistake, though, is treating a vibe-coded prototype as the final product.
As Tyler noted, the progression should be: Start with the vibe-code to prove the idea works, then move it into a governed platform so it can reach everyone safely. That transition should take minutes, not months. If deploying an app to production requires a six-month security review every time, the organization will either skip the review (risky) or never ship the app (wasteful).
A practical deployment path keeps the prototyping freedom of vibe coding while adding the governance, distribution, and auditability that IT teams require. When the handoff is fast, more good ideas make it to the people who need them.
Watch the full livestream
The checklist above covers the deployment and governance angle, but the livestream goes deeper.
Tyler's demo includes an executive dashboard with AI-powered insight sections that recommend specific next actions (like shifting deposit mixes to improve net interest margin).
He also walks through Cloud Amplifier, which streams Snowflake data into Domo without duplicating or moving it. And for teams concerned about consistency, the session shows how Cortex AI delivers the same answers whether someone queries from within Domo or directly in Snowflake.
Watch the full session to see the banking intelligence solution in action and hear Tyler break down the technical details.
