Case Studies
AI Infrastructure

How Wizd went from zero infrastructure to a fully deployed AI platform

Wizd is an AI-powered platform that needed their entire technical foundation built from scratch: automation pipelines, third-party integrations, and a reliable production deployment. We designed and delivered the full stack.

W

100%

infrastructure automated

Weeks

from zero to production

0

manual deployment steps

The challenge

Building an AI product without the infrastructure to run it

Wizd had a strong product vision but no technical foundation to support it. Before we started, this is what they were dealing with:

No automation pipelines in place — every workflow was manual or non-existent

Fragmented third-party tools with no integrations between them

No deployment process — shipping updates was slow and error-prone

Needed a scalable infrastructure that could grow with the product

Complex technical requirements with a tight launch timeline

“Revlis AI helped us set up our entire AI infrastructure from scratch, automation pipelines, integrations, and deployment. They made a complex process feel effortless.”

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The solution

A complete AI infrastructure, designed and delivered in weeks

We took Wizd from a blank slate to a fully operational AI platform. Every layer of the stack was designed for reliability and built to scale as their product grows.

01

Infrastructure audit and architecture design

We started by mapping every system Wizd needed: their AI pipelines, data flows, third-party dependencies, and deployment targets. From that, we designed a clean, scalable architecture before writing a single line of code.

02

Automation pipelines built end to end

We built all core automation workflows from scratch: data ingestion, model triggers, output handling, and error recovery. Each pipeline was designed to run reliably without manual intervention.

03

Third-party integrations wired up

We connected every external service Wizd relied on, ensuring data passed cleanly between systems with proper authentication, rate limiting, and failure handling built in.

04

Deployment pipeline and production launch

We set up a repeatable deployment process so Wizd could ship updates confidently. Infrastructure as code, environment configs, and monitoring were all included at handover.

What we used

Tools integrated

Revlis AI

Automation pipeline engine

Cloudflare

Infrastructure and deployment

Supabase

Database and auth layer

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