AI-native engineering pods
that ship in your system, in days, not months.
We embed senior engineers into your team with an AI operating model already mapped to your codebase. From first PR to full delivery velocity, without the ramp tax.
Your AI tools got faster.
Your team didn't.
A few engineers improved. Most didn't. Team velocity stayed flat.
Because AI works on code.
Your product doesn't.
Your features span services. Data models. And decisions that live inside your senior engineers' heads.
That's the layer AI never sees.
This isn't a tooling problem.
It's an operating model problem.
And fixing it internally takes months.
From signed engagement
to first PR in days.
We don't just add engineers. We add engineers backed by a dedicated AI architect team that maps your system into the operating model before any code is written.
Define what ships.
We align on priorities, constraints, and ownership. Your definition of done.
Plug into your system.
Your repo. Your tools. Your policies. The pod operates inside your environment from hour one.
Build the context layer.
Architecture, conventions, and standards mapped into the AI operating model.
First PR merged.
In your repo. Reviewed by your team. The architect team steps back. Your embedded engineer keeps shipping inside a system that already has context.
Three ways to plug Genium
into your engineering org.
Same engineers. Same AI operating model. Different ramp model, pick how much of the system you want us to own.
- 1 to 5 engineers per pod
- AI embedded in workflows
- US time zone
- Onboarding time: 2-4 weeks
- 1 to 5 AI-native engineers per pod
- Same time zone, same sprint
- Onboarding time: 4 days
- Compliance and security built-in
- LIFT pod at the core
- Discovery, product, and PM included
- Fixed-scope or milestone pricing
- Outcome owned end-to-end
Every engagement includes a dedicated Technical Account Manager, single point of contact across SHIP, LIFT, and BUILD.
Built for modern stacks.
The model is built for teams running mainstream stacks where the AI tooling ecosystem is mature. If you're on these, this is exactly what it was built for.
High compliance.
Zero margin for error.
We've deployed this model across regulated industries where AI governance, code provenance, and audit trails aren't nice-to-haves, they're the contract.
Healthcare
HIPAA-aligned engagements. PHI never reaches external AI services.
Finance & Insurance
SOC 2 compliant. Code provenance and audit trails on every PR.
Government & Public Sector
ITAR-certified since 2018. Cleared for sensitive workloads.
Zero Data Retention
Sensitive data stays inside your environment. ZDR architecture by default.
Who you'll actually talk to.
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Alex brings over 10 years of experience connecting innovative companies with world-class engineering teams globally. At Genium, he has led engineers, developers, and specialists, delivering secure, production-grade mobile and web applications.
Alex has a strong background in secure mobile applications, real-time communication systems (VoIP), AR/VR and interactive environments, and scalable backend architectures, with a focus on performance, reliability, and production readiness.
Francisco leads day-to-day operations at Genium. Over the past decade, he has built and scaled engineering teams across healthcare, insurance, government, and transportation.
Before Genium, he led operations at Uber and invested in 35 startups through NXTP Labs up to Series A. He has seen firsthand how engineering capacity becomes the limiting factor, both as an operator and as an investor.
If your AI spend is visible,
but the results aren't.
Get your deployment plan. We'll look at your system and show you how engineers would plug in and start shipping. If it doesn't fit, you walk away with a clear diagnosis of your capacity gap. Either way, you win.