Infracodebase
The agentic AI platform for creating production-ready, enterprise-grade cloud infrastructure you can trust.
The problem
Everyone wants speed.
Trust is the limiting factor.
0+
months to deliver
Gartner
0%
trust AI output
Stack Overflow 2025
0%
of cloud failures from misconfig
Gartner
0x
cheaper to fix early
IBM
The business is ready
Nothing moves until the cloud shows up.
What happens next
Request
Backlog grows because past work keeps failing review or breaking in production. Teams wait while fires get fought.
Plan
Teams operate in silos, manually writing docs. Slow and inconsistently implemented.
Design
Architecture diagrams created manually, rarely updated when code changes. No guarantee the design reflects what's actually running.
Code
Manual, slow, dependent on experts already stretched thin. AI could accelerate, but teams don't trust the output.
Ship
CI catches what should've been caught at design time. Every issue found here means rework upstream.
Rework
Issues hit production. Incidents, rollbacks, emergency fixes. What would've taken minutes now takes weeks.
Months pass. Costs grow. Trust erodes.
Months pass. Costs grow. Trust erodes.
Why now
The right problem at the right time.
AI is moving from experimentation to expectation. Teams want it to help build infrastructure faster, but the cost of getting it wrong is too high. Enterprises can't afford slow, manual delivery or fast, unsafe automation. They need both.
Why existing solutions fall short
Enterprises are trying everything.
None of it is working.
Prompt and pray
This works for demos and cloud sandboxes. It breaks down in governed environments where security, consistency, and accountability matter. Generated code often fails to apply, violates internal standards, or includes hallucinated configurations. Each failure further erodes trust in AI for infrastructure.
moreNo understanding of enterprise rules or context.
Developer AI tools
General-purpose coding assistants boost individual productivity but lack infrastructure context. They're biased toward execution, not governance. Results depend heavily on expert operators, and architects, security, and operations teams are left out entirely.
moreAI assists the developer, not the organization.
Scan and fix
Orchestration platforms and policy engines reduce risk by catching violations after code is written. But detection happens too late. AI produces output, humans clean it up. Rework is required, feedback loops get longer, and velocity suffers. Governance remains reactive instead of preventative.
moreControls are bolted on after generation.
Build it yourself
Enterprises try to solve this by building their own AI systems. These efforts require specialized talent, constant model updates, and ongoing prompt engineering. Most stall in pilot purgatory, never reach broad adoption, and fail to deliver ROI. 95% of enterprise AI projects fail.
moreThe problem is too complex to rebuild repeatedly in-house.
The insight
When agents can access your standards and tools, intent becomes shippable infrastructure.
The solution
The agentic AI platform for enterprise infra design and code.
Agents generate infrastructure designs and code grounded in your standards, tools, and way of working.
Any cloud. Any language. Any tool.
How it works
Why this is better
Cloud at the speed of business.
Teams ship faster because there is less friction and trust is built in. Business creates value for customers and employees while windows of opportunity are open. Everybody wins.
Go from... (tap to reveal)
Disconnected
No enterprise context, standards, or tooling integration
Integrated
Fully connected to your standards, tools, and ways of working
General purpose
Generic AI built for individual developers
Built for purpose
For the whole team: architects, security, platform, operations
Reactive
Catches issues after code is written
Proactive
Problems solved before a single line is committed
DIY
Expensive, constant care and feeding, worse results
Enterprise-ready
Maintained for you, at a fraction of the cost
Why customers will pay
They're spending billions and still failing.
The reality
Internal pilots don't scale. General-purpose AI lacks cloud context. The result is more delays, more toil, and teams wondering why AI isn't making things better.
The budget exists
Platform engineering
$6B → $40B by 2032
DevOps tools
$16B → $43B by 2030
Cloud security
$9B growing 15% annually
Infracodebase maps directly to these budgets.
Enterprises
A better answer to a problem they're failing to build and actively buying. Budgets are tight and they need to scale without adding headcount.
Consultancies
Better client outcomes, bigger margins on cloud deals, and a way to stay ahead as services commoditize.
Market opportunity
The intersection of two massive markets.
Global Cloud Computing
$750B
→ $2.4T by 2030
20% CAGR
AI Development Tools
$7B
→ $24B by 2030
27% CAGR
Click for breakdown
The opportunity
A wide-open market with no category leader. Cloud infrastructure is underserved by AI and enterprises are actively looking for solutions. Less than 0.5% market penetration = $100M+ ARR. Full category expansion puts us in billion-dollar territory.
The companies that establish trust and depth with enterprise cloud teams now will own this category for the next decade.
Addressable market
Gartner, Mordor Intelligence, Grand View Research
Traction
Early stage. Strong signals.
The product is resonating with large companies and the cloud engineering community. Enterprise sales motion and partner program underway with early positive signals. Punching above our weight.
500+
Organic users, no marketing spend
10+
Enterprise conversations per week
Global
Brand from day one
Enterprise
Consulting
Use cases resonating
850 registrants on launch webinar series
Go-to-market
Bottoms-up adoption. Top-down sales.
We build demand through the practitioners who use IaC every day, then convert that demand through enterprise sales to cloud leaders who need to prove AI delivers real value.
Bottoms-up
IaC practitioners
Engineers using Terraform, Pulumi, OpenTofu daily
Community + content
Organic discovery through value-first engagement
Adoption within companies
Users bring it into their workflows
Internal champions
Success stories spread organically
Top-down
Cloud leaders
Directors, VPs, CTOs with transformation mandates
Direct outreach
Enterprise sales motion with consultancy partners
Business alignment
Prove ROI, cost savings, delivery acceleration
Enterprise deals
Multi-year contracts with expansion potential
Multi-stakeholder buy-in
Engineering teams
Ship faster without sacrificing quality
Become internal champions
Business leaders
Real acceleration, not another AI experiment
Budget holders see measurable ROI
CTOs / Cloud leaders
Impact metrics, cost savings, governance
Executive sponsorship secured
Users → champions
→Champions → buyers
→Buyers → references
The enterprise flywheel
Growth strategy
Focused on durable growth.
Core Flywheel
Win the
Practitioner
Standardize the
Ecosystem
Enable the
Customer
Win the Practitioner
Build tools IaC engineers love. Organic adoption through daily use.
Standardize the Ecosystem
Become the way enterprises govern AI-generated infrastructure.
Enable the Customer
Convert practitioners into enterprise contracts with proven ROI.
Each turn of the flywheel makes the next turn easier. Practitioners drive adoption, adoption drives enterprise deals, enterprise standards attract more practitioners.
Why us
This company exists because we've lived the problem.
We've built and operated enterprise cloud platforms where speed, security, and scale collide daily. We've seen where shortcuts fail, where reviews become bottlenecks, and why most AI-driven approaches break under real constraints.
We're not guessing what this market needs. We're building the thing we wish existed.
Justin O'Connor
Founder / CEO / CTO
A decade in cloud across Azure, AWS, and GCP with some of the largest and most heavily regulated enterprises. Deep technical expertise in building and scaling AI systems. Leads product, operations, and business strategy.
Tarak
Co-Founder / CRO
Worked at large enterprise companies worldwide and built multiple startups. Previously led growth at Brainboard (Y Combinator). Deep network across the cloud ecosystem. Leads growth, sales, and marketing.
Why we win
AI-native
Agent-first architecture that SaaS can't retrofit
Network effects
Public sharing + internal collaboration compounds
Enterprise depth
Built for the last 20% that defines enterprise-grade
Sticky by value
Easy to adopt, hard to leave
The Ask
$1M to prove repeatable growth.
12–18 months of runway to validate product-market fit, close first enterprise contracts, and build the foundation for a category-defining company.
Use of funds
People
Founders, senior engineer, head of sales, phased head of product
Product & Growth
Contract development, design, infrastructure, marketing, partnerships
Operations
Software, cloud, legal, accounting, travel
Milestones: Land first enterprise customers, prove a consistent growth funnel, achieve $1M ARR runway extension.