Genesis: Reimagining the Enterprise Platform
A guided, AI-powered environment for building enterprise applications from intent to deployment.
Senior Product Designer
2022 — 2023
Product trio + 8 engineers
At a Glance
Building an enterprise AI application on C3 required a team of developers working for weeks or months. Business users who understood the domain problem had no way to translate their knowledge into a working application without heavy engineering support.
- -Enabled non-technical users to build and deploy AI applications for the first time on C3
- -Reduced time from idea to deployed application from weeks to hours
- -Adopted by 3 Fortune 500 customers within the first quarter of launch
- -Featured in C3 AI's annual product keynote as a flagship capability
Overview
Genesis is a no-code environment where users describe what they want to build in plain language and the platform handles the rest. From capturing intent to deploying a live enterprise AI application, Genesis walks users through every step with AI guidance. I designed the end-to-end experience covering intent capture, app creation, data configuration, deployment, monitoring, and sharing. It shipped as a flagship feature in the C3 AI product suite and was featured in the company's annual product keynote.
My Role
- -Designed the full application flow from home screen through deployment and sharing
- -Created the intent-capture interaction model where users describe what they want in natural language
- -Defined the guided step-by-step experience for data hydration and pipeline configuration
- -Worked with engineering to scope what AI could reliably generate versus what needed manual input
- -Led design reviews with the VP of Product and CEO
The Problem
Building an enterprise AI application on C3 required a team of developers working for weeks or months. Business users who understood the domain problem had no way to translate their knowledge into a working application without heavy engineering support. This created a bottleneck where the people closest to the problem were the furthest from the solution. Competitors were shipping simpler tools that let business users self-serve, but those tools could not handle the complexity of real enterprise data. We needed something that was genuinely easy to use without sacrificing the power that made C3 valuable in the first place.
What I Learned from Users
Business analysts could clearly articulate what application they needed but had no mental model for how to build it technically
The gap between "I want a predictive maintenance dashboard" and a working application was filled entirely by developer time and meetings
Users who tried the existing developer tools gave up within the first hour because the starting point assumed too much technical knowledge
The most successful customer deployments started with a clear natural language description of the use case, suggesting that intent capture could drive automation
Data configuration was the single biggest source of project delays, not model building or UI design
The Approach
I started by studying the 10 most successful C3 customer deployments to find patterns in how they went from idea to working application. A clear sequence emerged: capture intent, form a plan, create the app scaffold, configure data, deploy, and monitor. Each step had well-defined inputs and outputs. That meant we could build a guided flow where the AI handles the heavy lifting and the user makes decisions at key checkpoints. I designed the experience as a linear journey with clear stages. Each stage shows the user exactly what the system is doing, what it needs from them, and what comes next. No hidden complexity. If the AI is uncertain about something, it asks rather than guessing. We tested the flow with 5 enterprise customers using realistic data and iterated on the areas where users got stuck or lost confidence.
Key Design Decisions
Intent capture through natural language
The home screen starts with a single prompt: describe what you want to build. The system interprets the user's natural language input and generates a structured plan. This was a deliberate choice to meet users where they are. Instead of asking them to fill out forms or choose from templates, we let them express their intent in their own words. The AI then translates that into a concrete application specification that the user can review and adjust.
Genesis Home1 Start
Genesis Home2 Capture Intent
Genesis Home3 Form Plan
Transparent app creation with live preview
Once the user approves the plan, the system generates the application. Instead of a loading spinner, I designed a creation sequence that shows what the system is building in real time. The user sees the app take shape and can preview it before any data is connected. This built trust and gave users a chance to course-correct early rather than discovering problems after a lengthy build process.
Genesis App Creation
Genesis App Preview
Guided data hydration in three clear steps
Data configuration was the biggest pain point in the old workflow. I broke it into three explicit steps: add your data source, configure the processing pipelines, and define the data model. Each step has sensible defaults and clear explanations of what each setting does. The system suggests configurations based on the data it discovers, so users are reviewing and approving rather than building from scratch. This turned a multi-week task into something achievable in an afternoon.
Genesis App Hydrate Data1 Add Source
Genesis App Hydrate Data2 Configure Pipelines
Genesis App Hydrate Data3 Configure Data Model
One-click deployment with environment selection
Deploying an enterprise application usually involves DevOps teams and staging environments. I designed a deployment flow that abstracts that complexity. Users select their target environment, click deploy, and watch the progress. The system handles provisioning, configuration, and health checks. A clear status screen shows deployment progress and confirms when the app is live.
Genesis App Deploy1 Select
Genesis App Deploy2 Deploying
Genesis App Deploy3 Deployed
Built-in monitoring and sharing
After deployment, users need to know their application is working and be able to share it with stakeholders. I added a monitoring view that surfaces key health metrics and usage data without requiring users to set up separate analytics. A sharing flow lets users generate access links with configurable permissions, making it easy to get the right people using the application quickly.
Genesis App Monitor
Genesis App Share
Impact
- -Enabled non-technical users to build and deploy AI applications for the first time on C3
- -Reduced time from idea to deployed application from weeks to hours
- -Adopted by 3 Fortune 500 customers within the first quarter of launch
- -Featured in C3 AI's annual product keynote as a flagship capability
- -Eliminated the need for dedicated developer support during initial app creation
- -Customer satisfaction scores improved from 3.2 to 4.6 out of 5
What I Would Do Next
- -Add real-time collaboration so multiple stakeholders can shape an application together
- -Expand AI generation to handle more complex application types beyond analytics dashboards
- -Build a template marketplace where customers can share and fork successful application configurations
- -Integrate deeper monitoring with automated AI-driven alerts and optimization suggestions