95%
of enterprise gen-AI pilots deliver zero measurable impact (MIT).
Take AI-built pilots to production, governed.
The bottleneck moved from speed to control. Your teams are already building with AI. Xano makes the pilot and the production system the same governed artifact, on the same platform.
Everyone is building. Almost nothing ships.
Why AI pilots stall before production.
The models work. The path from a pilot to a governed production system is what’s missing.
Workflow gap
MIT's diagnosis. The models work. The path to production doesn't.
The hard part
The prototype was easy. Production is where pilots go to die.
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The pilot lives in one person’s tools
It was built in a hackathon tool or on one developer’s laptop, so taking it to production means rebuilding it somewhere else.
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The rules arrive after the code
Auth, PII handling and audit requirements get reviewed after the last prompt instead of set before the first one, so every pilot fails the same checks.
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Nobody can review what the agent wrote
Agents write more of the code every quarter, and no one reads all of it later. Without a shared picture of what was built, review becomes a bottleneck.
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What was tested is not what ships
When the reviewed build and the deployed build differ, approval stops meaning anything, and IT stops approving.
Guardrails, not gatekeeping.
Every project governed before the first prompt, instead of reviewed after the last one.
The lifecycle
Pilot to production is the enterprise AI SDLC.
Agents write more of the code every quarter. No one reads all of it later. So the lifecycle that matters runs left to right, and one artifact travels the whole way.
“We experimented with AI builders which were great for a hackathon, but our business users were unable to move it into something usable. We're looking for anything to help us have consistency along the innovation lifecycle, from ideation to prototype, MVP, and production.”
Four stops
Set the policy once. Everything inherits it.
- 01 · Set the rules
IT writes the policy
Every project in the workspace inherits it, and what security sets stays set.
- 02 · Everyone builds
In their own tools
Developers bring Claude Code, Codex, or Cursor; business users bring a Bolt front end. Xano hands every agent the same policy, docs and skills. The prompt never mentions PII rules, and the app inherits them anyway.
- 03 · Review each build
Nothing touches production
Every build runs in an ephemeral sandbox until IT says otherwise.
- 04 · Promote what passes
Review is a real check
The release IT reviews is the release that runs. Promote the exact artifact.
Workspace rules your organization owns.
Most AI rules today are one developer’s markdown files, and no two are alike. Workspace rules are organizational: auth is done this way, every time, checked again at promotion. Regulated? HIPAA, SOC 2 and your audit rules live in the same policy layer, enforced per environment and per tenant.
Couplings
Bring the stack you have.
Postgres included, Snowflake and your services connected, any major cloud or on-prem, under your certifications. Host your static front end on Xano or connect one built anywhere.
Integrations Experiments
Let them build. Ship what matters.
You never know which experiment will matter. Workspace rules exist so the one that does can reach production without a rewrite.
Questions
AI pilot to production, answered
How do you take an AI pilot to production?
Make the pilot and the production system the same artifact on the same platform. On Xano, IT sets the workspace rules once, every project inherits them, each build runs in an ephemeral sandbox, and the exact release IT reviewed is the one promoted to production, with no rewrite in between.
Why do most enterprise AI pilots never reach production?
Because the path to production is missing, not the model. MIT found that 95% of enterprise gen-AI pilots deliver zero measurable impact and called it a workflow gap. Pilots are built outside the rules production needs, so each one has to be rebuilt, re-reviewed and re-secured before it can ship.
What are workspace rules in Xano?
Workspace rules are one set of rules for the whole team: the policy, docs and skills every person and AI agent in a Xano workspace builds by. IT writes them once, every project inherits them, and they are checked again at promotion, so a prompt never has to mention PII rules for the app to follow them.
Can developers and business users build in their own tools?
Yes. Developers bring Claude Code, Codex or Cursor, and business users can bring a Bolt front end. Xano hands every agent the same policy, docs and skills, so whatever tool started the pilot, the backend it produces follows the same workspace rules.
How does Xano keep AI-built code out of production until it is reviewed?
Every build runs in an ephemeral sandbox, and nothing touches production until IT promotes it. Promotion re-checks the workspace rules and ships the exact artifact that was reviewed, so review is a real check. AI code governance explains the model.
Does this work for regulated enterprises?
Yes. HIPAA, SOC 2 and your own audit rules live in the same policy layer as everything else, enforced per environment and per tenant. Xano runs on any major cloud or on-prem under your certifications, with Postgres included and Snowflake and your existing services connected.
AI builds software. Xano makes it trustworthy.
Every enterprise is working out what AI governance looks like and who owns it. Start with a conversation.